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WO2012036490A2 - Kit expérimental pour le traitement numérique de signaux biologiques - Google Patents

Kit expérimental pour le traitement numérique de signaux biologiques Download PDF

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Publication number
WO2012036490A2
WO2012036490A2 PCT/KR2011/006828 KR2011006828W WO2012036490A2 WO 2012036490 A2 WO2012036490 A2 WO 2012036490A2 KR 2011006828 W KR2011006828 W KR 2011006828W WO 2012036490 A2 WO2012036490 A2 WO 2012036490A2
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WIPO (PCT)
Prior art keywords
signal
unit
filter
biosignal
bio
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PCT/KR2011/006828
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English (en)
Korean (ko)
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WO2012036490A3 (fr
Inventor
허재만
김원기
한영오
김동준
이정환
홍성찬
안혜정
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케이티메드 주식회사
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Publication of WO2012036490A2 publication Critical patent/WO2012036490A2/fr
Publication of WO2012036490A3 publication Critical patent/WO2012036490A3/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A61B5/02055—Simultaneously evaluating both cardiovascular condition and temperature
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B23/00—Models for scientific, medical, or mathematical purposes, e.g. full-sized devices for demonstration purposes
    • G09B23/28—Models for scientific, medical, or mathematical purposes, e.g. full-sized devices for demonstration purposes for medicine
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7228—Signal modulation applied to the input signal sent to patient or subject; demodulation to recover the physiological signal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B25/00—Models for purposes not provided for in G09B23/00, e.g. full-sized devices for demonstration purposes
    • G09B25/02—Models for purposes not provided for in G09B23/00, e.g. full-sized devices for demonstration purposes of industrial processes; of machinery
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/021—Measuring pressure in heart or blood vessels
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/14542—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue for measuring blood gases
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]

Definitions

  • the present invention relates to a test kit for signal processing incorporating a digital signal processing experiment process based on a biosignal into a single kit, and more particularly, to a plurality of biosignals (electrocardiogram measurement signal, oxygen saturation measurement signal) generated in the human body. Physical blood pressure measurement signal, body temperature measurement signal, etc.) in real time, and these bio signals can be used in the digital signal processing experiment process in real time, the physical understanding of the digital signal processing process in the experiment process using the biological signal.
  • the present invention relates to an experimental kit for digital signal processing of a biosignal which can increase.
  • bio-signal measuring devices are devices that measure and display biosignals such as electrocardiogram, blood pressure, oxygen saturation, respiration, and body temperature, respectively, and then process the digital signals, mainly for the purpose of monitoring a patient's condition in a hospital or a university or laboratory. It has been developed and marketed as devices that can be used for research purposes.
  • the conventional bio-signal measuring apparatus has been developed a connection device for transmitting the bio-signal data to the computer through a computer interface means while measuring the bio-signal, but this simply functions to transmit and store the measured bio-signal data to the computer Have only. That is, only the biological signal transmission and storage function through the computer interface, and the experimental kit for applying the biological signal to be processed into various digital signals is configured separately from the bio-signal measuring device.
  • the technical problem to be solved by the present invention by directly converting the biological signal, such as electrocardiogram, blood pressure, respiration, blood oxygen saturation concentration, body temperature measured directly in the body to be delivered directly to the experiment kit used in the experimental process
  • the biological signal such as electrocardiogram, blood pressure, respiration, blood oxygen saturation concentration, body temperature measured directly in the body to be delivered directly to the experiment kit used in the experimental process
  • Another technical problem to be solved by the present invention is to assist in easily understanding the interrelationships between the time domain and the frequency domain through biosignals in which several frequency components are combined. It is intended to provide an experimental kit for digital signal processing that can be facilitated .
  • One embodiment of the present invention for achieving the above object including a plurality of sensors, one or more of ECG (ECG), blood pressure (NIBP), respiration (RESP), blood oxygen saturation (SpO2), body temperature (TEMP)
  • ECG ECG
  • NIBP blood pressure
  • RSP respiration
  • SpO2 blood oxygen saturation
  • TTP body temperature
  • a biosignal measuring unit detecting a biosignal including a; Read the pre-stored waveform in the memory unit or receive the bio-signal from the bio-signal measuring unit as an input signal, and receive the values of the time (T) and the frequency range (f) input by the user from the setting unit
  • An arithmetic processing unit including a signal principle training mode unit for performing pre-transformation according to the time T and the frequency range f; And an output unit for outputting a calculation result of the operation processor as a graph or data.
  • the calculation processing unit reads a waveform previously stored in a memory unit, receives a biosignal from the biosignal measuring unit as an input signal, and receives a system signal of a black box set by a user from a setting unit.
  • a time domain representation training mode unit for convolving a signal and the system signal; Reads a waveform previously stored in the memory unit or receives a bio-signal from the bio-signal measuring unit as an input signal, and a filter type of one of a low pass filter, a high pass filter, a band pass filter, and a band stop filter.
  • a frequency domain representation training mode unit for filtering the input signal by the designed filter when the cutoff frequency is set and the filter is designed; Reads a waveform previously stored in the memory unit, receives a biosignal from the biosignal measuring unit as an input signal, receives a sampling frequency set by the user from the setter, or sets a sampling rate set by the user to the setter.
  • a sampling training mode unit for receiving a sampling signal and outputting the sampling result; Designing a filter by reading a pre-stored waveform in a memory unit or receiving a biosignal from the biosignal measuring unit as an input signal and receiving a set number of sample points and a moving average filter from the setter, or setting a set FIR
  • An FIR filter basic training mode unit for designing a filter by receiving coefficients of a differential equation of the filter from a setting unit and performing filtering by the designed filter; IIR filter basic practice of reading a waveform previously stored in a memory unit or receiving a biosignal from the biosignal measuring unit as an input signal and filtering by a filter designed based on coefficients of a differential equation of a set IIR filter.
  • a mode unit Read the pre-stored waveform in the memory unit or receive the bio-signal from the bio-signal measuring unit as an input signal, and implement LPF according to the set cutoff frequency, but the rectangular window function, the triangle (bartlet) window function, and the hanning
  • An FIR filter training mode unit for designing an LPF to which one of a window function, a hamming window function, a blackman window function, and a Kaiser window function is applied, and filtering the input signal by a designed LPF
  • An IIR filter training mode unit for designing a filter according to coefficient values of an equation and filtering the designed filter; Receiving a bio-signal from the bio-signal measuring unit as an input signal, the filter is designed by the set filter type, low-pass
  • a filter application training mode unit configured to output a result of calculating the squared value of the differential waveform; Receiving a bio-signal from the bio-signal measuring unit, when the open button is selected, a communication port is opened and the bio-signal detected through the bio-signal measuring unit is transmitted. When the stop button is selected, the transmission is stopped and the protocol data window is displayed.
  • Data communication training mode for controlling to display the protocol of the bio-signal further comprises at least one or more.
  • each sensor drive means comprising an electrocardiogram, blood pressure, respiration, blood oxygen saturation concentration, body temperature from a portion of the body through each sensor
  • a biosignal measuring unit for detecting a biosignal
  • a digital signal processor for driving each sensor by outputting a control signal for controlling the operation of each sensor, and amplifying and filtering the electrical signal measured by each sensor, and digitally processing the digital signal processor.
  • Experimental kit for digital signal processing of biological signals including a communication interface for performing a communication interface for wired / wireless transmission of each digitally processed biological signals.
  • an experimental kit for digital signal processing of a biosignal further includes a kit output unit including a plurality of analog output terminals respectively outputting the biosignal detected by each sensor of the biosignal measuring unit in analog form. to be.
  • Still another embodiment of the present invention is an experimental kit for digital signal processing of a biosignal further including a display unit for analyzing the biosignals sent to the communication interface in real time and storing or outputting the analysis result values.
  • the experimental kit for digital signal processing according to the present invention can easily understand the correlation between the time domain and the frequency domain through a biosignal in which several frequency components are combined in simple signals such as sine waves used in the experiment. It will be possible, and through the various filter experiments there is an advantage that can facilitate the filter design. In addition, the use of the bio-signal will cause interest in the experiment, so it will be possible to enhance the effect of signal processing experiment training.
  • the experimental kit for digital signal processing according to the present invention in particular, it is possible to analog output for bio-signals, so it is possible to study U-Health, signal processing, etc. using bio-signals, and can also be used as a teaching aid for professors. There is an advantage to doing this.
  • the present invention is an experimental kit for digital signal processing, which is applied to real life or actual research, and processes signals using a biosignal detected in a real human body, but these biosignals are signals in which several frequency components are combined. This makes it easier to understand the interrelationships between the time domain and the frequency domain, and also enables various filter experiments.
  • FIG. 1 is a block diagram schematically showing the overall configuration of an experimental kit for digital signal processing of a biological signal according to the present invention.
  • FIG. 2 is a block diagram schematically illustrating a configuration of an operation processor of FIG. 1.
  • FIG. 3 is an explanatory diagram for explaining an impulse response mode among the signal processing unit of FIG. 1 and the time domain representation training mode of the system.
  • FIG. 4 is a detailed diagram illustrating a detailed configuration of an experimental kit for digital signal processing of FIG. 1.
  • FIG. 5 is a detailed block diagram of the kit output unit of the experimental kit of FIG.
  • FIG. 6 is a flowchart illustrating an operation of a digital signal processing experiment process using a biosignal in an experiment kit according to the present invention.
  • FIG. 7 is a reference diagram illustrating digital signal processing methods that can be performed as an experimental kit according to the present invention.
  • FIG. 8 is an explanatory diagram for describing a biosignal detection training mode unit of the arithmetic processing unit of FIG. 2.
  • FIG. 9 is an explanatory diagram for illustrating a signal principle training mode unit of the arithmetic processing unit of FIG. 2.
  • FIG. 10 is an explanatory diagram for describing a signal of the arithmetic processing unit of FIG. 2 and a time domain representation training mode unit of the system.
  • FIG. 11 is an explanatory diagram for explaining a signal processing unit of FIG. 2 and a frequency domain representation training mode unit of the system;
  • FIG. 12 is an explanatory diagram for illustrating a sampling training mode unit of the arithmetic processing unit of FIG. 2.
  • FIG. 13 is an explanatory diagram for illustrating a filter basic training mode unit of the arithmetic processing unit of FIG. 2.
  • FIG. 14 is an explanatory diagram for explaining an FIR filter training mode unit of the calculation processing unit of FIG. 2.
  • FIG. 15 is an explanatory diagram for explaining an IIR filter training mode unit of the calculation processing unit of FIG. 2.
  • 16 is an explanatory diagram for explaining a filter application training mode unit of the arithmetic processing unit of FIG. 2.
  • FIG. 17 is an explanatory diagram for explaining a demodulation training mode unit of a signal of the arithmetic processing unit of FIG. 2.
  • FIG. 18 is an explanatory diagram for explaining a data communication training mode unit of the arithmetic processing unit of FIG. 2.
  • the digital signal processing experiment kit 90 itself can perform a digital calculation processing experiment through the biological experiment and its output, and further, the digital signal processing experiment kit 90 through the communication interface 500 It is connected to an external computer (user's computer) 590 to perform digital computation processing experiments.
  • FIG. 1 is a block diagram schematically showing the overall configuration of an experimental kit for digital signal processing according to an embodiment of the present invention
  • Figure 2 is a block diagram schematically showing the configuration of the processing unit of FIG.
  • the present invention includes a bio-signal measuring unit 100, arithmetic processing unit 200, memory unit 300, kit output unit 400, communication interface 500, output unit 600, setting unit 700 It is done by In this case, the output unit 600 and the setting unit 700 are not included in the experiment kit 90 for digital signal processing in some cases, and are provided in an external computer equipped with the experiment signal 90 for digital signal processing. In some cases, the output unit 600 and the setting unit 700 may be formed of a single touch pad.
  • the biosignal measuring unit 100 includes a plurality of sensors for measuring a biosignal, sensor driving means for driving each sensor, and a signal preprocessing unit, and the electrocardiogram, body impedance, blood pressure, oxygen from a part of the body through each sensor. It detects biological signals including saturation, body temperature, respiration, and voice.
  • the plurality of sensors may include an ECG electrode, a blood pressure detection sensor, an oxygen saturation detection sensor, a body temperature sensor, a respiration sensor, a microphone, and the like.
  • the operation processor 200 receives a biosignal detected from the biosignal measuring unit 100, that is, an electrocardiogram signal, a body impedance signal, a blood pressure signal, an oxygen saturation signal, a body temperature signal, and the like, and is input from the setting unit 700. Outputs arithmetic processing depending on the mode.
  • the arithmetic processing unit 200 may be configured only as a digital signal processing unit of the digital signal processing experiment kit 90, and in some cases, the arithmetic processing unit 200 is a digital signal processing unit of the digital signal processing experiment kit 90. And an operation processor of the external computer 590. That is, in some cases, the arithmetic processing of the present invention can be driven only in the experimental signal 90 for digital signal processing, or in some cases, the arithmetic processing of the present invention is performed in the experimental kit 90 and the external computer 590 for digital signal processing. Can be done in conjunction.
  • the calculation processor 200 may include two digital signal processors, that is, a first digital signal processor and a second digital signal processor.
  • the biosignal detection training mode unit 210 detects an actual biosignal and outputs the detected signal to the output unit 600 or the kit output unit 400, so that each biosignal measured in real time, that is, an electrocardiogram ( ECG), respiration (RESP), oxygen saturation (SpO2) (pulse wave), blood pressure (NiBP), body temperature (TEMP), and the form of the voice, such as to determine the measurement method, etc. can be learned.
  • ECG electrocardiogram
  • RSP respiration
  • SpO2 oxygen saturation
  • Pulse wave pulse wave
  • NiBP blood pressure
  • body temperature body temperature
  • the biosignal detection training mode unit 210 receives the biosignal detected from the biosignal measurement unit 100, outputs the biosignal to the kit output unit 400, and outputs the output unit through the communication interface 500. 600), and in some cases, it is stored in the memory unit 300 by setting.
  • the signal principle training mode unit 220 is a means for practicing Fourier series and Fourier transform.
  • the signal principle training mode unit 220 selects one of Fourier series and Fourier transform, and uses a waveform previously stored in the memory unit 300 according to a setting. Or by detecting a bio-signal and practicing using the detected bio-signal.
  • the predetermined waveform pre-stored sinusoidal wave, square wave, or biosignal, etc.
  • the predetermined waveform previously stored in the memory unit 300 may be read or the biosignal measuring unit according to the setting input from the setting unit 700.
  • Receives a biosignal detected from 100 combines a sine wave according to the principle of Fourier series, displays a predetermined waveform with Fourier series, and outputs the waveform to the kit output unit 400 or the output unit 600.
  • the Fourier series can be generated as shown in Equation 1 by linearly combining a large number of sinusoids in any period (T) signal.
  • the user sets a value of n (order of harmonics) to form a predetermined waveform by combining the sinusoids linearly, and outputs the predetermined waveform to the kit output unit 400 or the output unit 600.
  • the predetermined waveform pre-stored sine wave, square wave, or biosignal, etc.
  • the predetermined waveform previously stored in the memory unit 300 may be read or the biosignal measuring unit according to the setting input from the setting unit 700.
  • the frequency component check button is selected, outputs the graph or data for checking the frequency component for the frequency component equation of the square wave to the kit output unit 400 or output unit 600
  • the user inputs the value of the time (T) and the frequency range (f) input through the setting unit 700, the Fourier transformed graph or data according to the setting kit output unit 400 or output unit 600 ) So that the user can check the frequency range.
  • the input ECG signal is simplified to an impulse square wave having an amplitude of 1 and a time of 0.05 sec, and outputs a graph or data, and is a Fourier transform according to a set time (T) and a frequency range (f). 4 to output the Fourier transformed graph or data to the kit output unit 400 or the output unit 600.
  • T set time
  • f frequency range
  • the frequency component of the signal displayed in the graph is an absolute value and the negative region and the positive region are visible according to the frequency range f.
  • the time domain representation training mode of the signal and the system 230 is a means for practicing the time domain representation of the signal and the system (black box) through continuous signal convolution and discrete signal convolution.
  • the input signal is a cone signal with the system signal of the black box (which can be set in the setting unit 700). It is output to the output.
  • the mode unit is driven, a black box is displayed on the output unit 600, and the input signal is set at the black box input terminal through the setting unit 700.
  • the time domain representation training mode 230 of the signal and the system includes an impulse response training mode, a convolution training mode in the continuous time domain, an RC circuit analysis training mode using convolution, and an RL circuit analysis training mode using convolution. do.
  • the impulse (delta) function ⁇ (t) input signal to the black box produces an impulse response function h (t) as shown in FIG.
  • the convoluted line process according to the setting of x (t) and h (t) is summarized.
  • the function h ( ⁇ ) is inverted (inverted) with respect to time, and h (- ⁇ ) is obtained.
  • h (t1- ⁇ ) and x ( ⁇ ) are multiplied to x ( ⁇ ) h (t1- ⁇ ).
  • the convolution y (t) of the two input signals x (t) and h (t) is expressed by an integral as in Equation (2).
  • Convolution is performed according to the set x (t) and h (t), and the result is expressed by a graph or a mathematical expression and output to the kit output unit 400 or the output unit 600.
  • Frequency domain representation training mode of the signal and the system 240 is a means for practicing the frequency domain representation of the various signals and systems, after setting this mode, according to the subsequent mode set in the setting unit 700, that is, Fourier transform understanding training Mode, virtual signal using system understanding training mode, biological signal using system understanding training mode is made to practice in the frequency domain.
  • the frequency domain representation training mode unit 240 of the signal and the system includes a system understanding training mode and a signal and system training mode.
  • the understanding mode of the system includes a synthesized wave shaping mode and a synthesized wave spectrum checking mode.
  • a synthesized wave shaping mode a first signal, a second signal, and a third signal having different predetermined frequency ranges are inputted,
  • the synthesized wave button is selected, the first signal, the second signal, the third signal, etc. are synthesized and displayed as a graph or a formula and output to the kit output unit 400 or the output unit 600, and output to the memory unit 300. do.
  • the synthesized wave generated in the composite wave forming mode is read and the spectrum is output.
  • One of the filter types one of LPF and BPF, is selected and the cutoff frequency is selected. If set, the filtered output to the kit output unit 400 or output unit 600.
  • the signal and system training mode includes voice signal and system training mode and other non-voice biosignal training modes, and the other non-voice biosignal training modes include a signal selected from electrocardiogram, respiratory rate, and oxygen saturation.
  • the voice signal and the system training mode have a predetermined sampling frequency and receive the voice signal through the biosignal measurement unit 100 for a predetermined recording time.
  • the filter type and the cutoff frequency are input and the filter is set, the received voice signal is filtered. And output the result to the kit output unit 400 or the output unit 600, and also outputs the voice before filtering and the filtered voice through the speaker (not shown).
  • the sampling training mode unit 250 provides a training for sampling and discrete time convolution-related training, sampling the input signal according to a setting, and restoring the discrete signal.
  • the sampling training mode unit 250 includes a training mode for converting into discrete signals and a discrete signal convolution training mode.
  • a pre-stored waveform or biosignal received through the biosignal measuring unit 100 as read from the memory unit 300 is used as an input signal.
  • the sampling frequency is set differently and the sampling button is input, the spectrum is output, and the spectrum of each input signal having a different sampling frequency is output. In this mode, you can enter the sampling rate, frequency, and amplitude values and look at the distortion level of the input signal.
  • the discrete signal convolution training mode When the discrete signal convolution training mode is selected, setting the input signal x [n] and the system h [n] outputs a convolution result.
  • the first step to the fourth stem described in the impulse response training mode of the time domain representation training mode unit 230 of the signal and the system can be performed step by step to check the result.
  • we also design a lowpass filter whose received pleated biosignal and highpass frequency are 1/2 of the sampling rate, and convolution when the convolution button is selected, and combines the result with the biosignal before filtering. Output it for comparison.
  • the filter basic training mode unit 260 is a basic training means for a filter that serves to pass only a signal of a component desired by a user from an input signal.
  • filters are classified into FIR (finite impulse response) filters and IIR (infinite impulse response) filters according to the characteristics of the impulse response.
  • IIR infinite impulse response
  • the input and output differential equation of the IIR filter is
  • the transfer function by the z transform is
  • a moving average filter for obtaining an average of x [n] and the previous M-1 may be expressed as Equation 3 below.
  • the filter basic training mode unit 260 includes an FIR filter basic mode and an IIR filter basic mode.
  • a signal previously stored in the memory unit 300 or a signal detected from the biosignal measurement unit 100 is used as an input signal, and the number of sample points and the moving average filter are set in the filter selection.
  • the IIR filter basic mode When the IIR filter basic mode is selected, a signal previously stored in the memory unit 300 or a signal detected from the biosignal measurement unit 100 is used as an input signal, and in selecting a filter, an IIR filter is selected, but the differential equation of the IIR filter is selected. When inputting the coefficient of, outputs the spectrum of the set filter, and if the convolution button is selected, it outputs the result of the convolution, that is, the filtered input signal.
  • a causal FIR filter The response H (e j ⁇ ) is a convolution of the response H d (e j ⁇ ) and the window function response W (e j ⁇ ) of the ideal frequency selective filter, which is given by Equation 4, and has the following FIR filter characteristics.
  • the window function ⁇ (n) has a finite length of M
  • the response has major and minor lobes with a width proportional to 1 / M.
  • Third main lobe is to make the transition band in response H (e j ⁇ ) of the filter transition determined bandwidth, yipok is proportional to 1 / M The wider the main lobe, the higher the transition bandwidth.
  • the sublobes create ripples with similar shapes in both the passband and stopband.
  • the FIR filter training mode unit 270 is configured to practice the above-described FIR filter features using various window functions.
  • a rectangular window function a triangle (bartlet) window function, a hanning window function, a hamming window function, a blackman window function, and a Kaiser window function can be used.
  • the rectangular window function ( ⁇ R ) is a window that takes the sample value in the effective section as it is, Same as Where B is Noise Equivalen Bandwidth.
  • the triangular (bartlet) window function ( ⁇ T ) is a triangular window, which eliminates the sudden discontinuity of the rectangular window. Same as
  • the Hanning window function ( ⁇ HN ) is a window that improves the performance of the side lobe by using the cosine function to emphasize the vicinity of the center value more than the triangular window in the process of eliminating the discontinuity of the rectangular window.
  • the Blackman window function ( ⁇ B ) is a window that improves the performance of the side lobes by using one more cosine function than the Hamming window (second harmonic). Same as
  • Kaiser window function ( ⁇ K [n]) is the optimal window function that represents the largest leaf width under a given stopband attenuation, with the steepest transition band, Same as
  • the FIR filter training mode unit 270 includes a window function design mode and an input signal filtering mode.
  • the window function design mode of the FIR filter training mode unit 270 is designed to design a FIR filter using a window, and when a cutoff frequency is input and a filter button is selected, LPF is implemented, and then a filter IFFT When the button is selected, the filter designed in the frequency domain can be checked in the time domain.
  • the desired window function is determined in the window type item, the window function width is determined in the window width, and when the window button is selected, the window function is checked. After that, the windowing button is selected and the window function is applied to the designed filter.
  • the window FFT button is selected, the window function is designed in the frequency domain.
  • a signal previously stored in the memory unit 300 or a signal detected by the biosignal measurement unit 100 is used as an input signal. This can be checked by outputting the spectrum, and by selecting the filter frequency button to read the LPF designed in the window function design mode, selecting the filtering button filters the input signal with the LPF and outputs the filtered result in the frequency domain. You can check this by selecting the IFFT button and outputting the filtered results in the time domain.
  • the IIR filter training mode unit 280 is a means for practicing the IIR filter.
  • the IIR filter performs filtering with an infinite impulse response.
  • a general system function is represented by Equation 5, and the poles and zeros of the system function are Z. It can be located at any point in the plane, the general difference equation is shown in equation (6).
  • the relationship between the input signal and the output signal of the digital filter is represented by a differential equation, and the IIR filter has a form of a differential equation as shown in Equation (7).
  • the basic method of designing an IIR filter is, firstly, designing an analog lowpass filter, specifying the specification of an analog filter with desired characteristics, and second, applying a filter transformation (s ⁇ z) to obtain a digital lowpass filter. Third, frequency band conversion is applied to obtain another filter from the digital lowpass filter.
  • the IIR filter training mode unit 280 includes an IIR filter training mode of a first order differential equation and an IIR filter training mode of a second order equation.
  • the coefficient values of the first-order differential equation are input, the poles and zeros are displayed on the Z domain graph, and the frequency response curve is output as the magnitude spectrum graph, or the poles and By inputting a zero value, the poles and zeros are displayed on the Z domain graph, the frequency response curve is output as a magnitude spectrum graph, and the coefficients of the first-order differential equation can be obtained using the poles and zeros by selecting the coefficient button.
  • a signal pre-stored in the memory unit 300 or a signal detected by the biosignal measurement unit 100 is used as an input signal, and when a signal frequency button is selected, a signal spectrum is output.
  • the coefficient value of the first-order differential equation is input, and when the filter button is selected, the frequency response curve of the filter is output and checked.
  • the result button is selected, the filtered result is output and confirmed.
  • the pole and zero values of the quadratic differential equation are input, and the frequency response curve is output.
  • the coefficients of the quadratic differential equation are calculated using the pole and zero points by selecting the coefficient button. .
  • a signal previously stored in the memory unit 300 or a signal detected from the biosignal measurement unit 100 is used as an input signal, and when a signal frequency button is selected, a signal spectrum is output.
  • the coefficient value of the quadratic differential equation is input, and if the filter button is selected, the frequency response curve of the filter can be output and can be checked. If the result button is selected, the filtered result can be output and confirmed.
  • the filter application training mode unit 285 is a means for training to implement a filter applied to a biosignal and derive a result by applying the biosignal, and includes an ECG signal application mode and a pulse wave signal application mode.
  • the ECG signal application mode includes an ECG preprocessing mode, an ECG differential mode, and an ECG peak detection mode.
  • the ECG preprocessing mode of the ECG signal application mode stores the detected signal from the biosignal measurement unit 100, reads the stored ECG signal, outputs the frequency component of the waveform of the ECG signal when the frequency button is selected, and inputs the filter type. And input the low cutoff frequency or high cutoff frequency and select the filter button to output the filtered result.
  • the ECG differential mode of the present invention is a differential (differential filter) mode, and includes a two-point differential algorithm mode, a three-point central differential algorithm mode, and a least-squares polynomial differential approximation mode.
  • ECG differential mode of ECG signal application mode is one of two-point differential algorithm mode, three-point central differential algorithm mode and least-squares polynomial differential approximation mode to the ECG signal filtered (bandpassed) in ECG preprocessing mode.
  • the filter is selected, the coefficient of the differential filter is input in the filter coefficient input window, and the result is output.
  • the result of calculating the squared value of the derivative waveform is output.
  • the two-point differential algorithm mode calculates the difference between two consecutive points. Same as
  • the three-point central differential algorithm mode calculates the difference between left and right points about a center point. Same as
  • the least-squares polynomial differential approximation mode calculates the slope based on the center point among the samples. For example, the derivative value is calculated using five points as follows.
  • the ECG peak detection mode of the ECG signal application mode is a means for performing an exercise of estimating the position of a QRS wave representing a peak point in an ECG waveform (P, Q, R, S, T waves) by using a moving window integration.
  • the general moving average integral is Same as
  • the ECG peak detection mode of the ECG signal application mode of the present invention reads the squared data output from the ECG differential mode, sets the number in the sample number input window, sets the moving average filter to perform moving average filtering, and then displays the result. Outputs, sets a threshold for detecting an electrocardiogram peak (QRS), and displays a graph with a beep sound when a QRS exceeding the threshold is detected. In addition, the number of electrocardiogram peaks (QRS) during one second is counted and output as a heart rate.
  • QRS electrocardiogram peak
  • the pulse wave signal application mode includes a pulse wave preprocessing mode, a pulse wave differential mode, and a pulse wave peak detection mode.
  • the pulse wave differential mode is a derivative of one of a two-point differential algorithm mode, a three-point central differential algorithm mode, and a least-squares polynomial differential approximation mode to an ECG signal filtered (bandpassed) in the pulse wave preprocessing mode.
  • the filter is selected, the coefficient of the differential filter is input in the filter coefficient input window, and the result is output.
  • the result of calculating the squared value of the derivative waveform is output.
  • the pulse wave peak detection mode of the pulse wave signal application mode reads the squared data output from the pulse wave differential mode, sets the number in the sample number input window, sets the moving average filter, performs moving average filtering, and outputs the result.
  • a threshold for pulse wave peak detection is set, and when a pulse wave peak exceeding the threshold is detected, a beep sound is displayed on the graph. In addition, the number of pulse wave peaks during one second is counted and output as heart rate.
  • the signal demodulation training mode unit 290 is a signal for demodulation demodulation exercise, and includes a modulation mode unit, a demodulation mode unit, a double modulation mode unit, and a dual demodulation mode unit.
  • the modulation mode unit is a signal pre-stored in the memory unit 300, or a signal made by the signal or the signal measured by the chanchae signal measuring unit 100 as an input signal, when a carrier button is selected, a predetermined sine wave is set as a carrier signal.
  • a predetermined sinusoidal signal carrier signal
  • the demodulation mode unit uses a pre-stored signal (modulated and stored signal) as an input signal, selects a detection button, and sets a signal smaller than zero among the modulated signals to zero. Design a filter (eg low pass filter) by setting the cutoff frequency, count value and (sampling frequency), and select the OK button to output the filtered signal and check the filtered signal through the speaker.
  • a filter eg low pass filter
  • the dual modulation mode unit simultaneously measures and stores two biosignals, for example, ECG and oxygen saturation, reads the two biosignals thus measured, modulates the two biosignals with a predetermined sine wave signal, and outputs the modulated biosignals. do.
  • two biosignals for example, ECG and oxygen saturation
  • the dual demodulation mode unit sequentially reads and demodulates a dual modulated signal in a first manner, and uses a signal (modulated and stored signal) previously stored in the memory unit 300 as an input signal, and selects a frequency button to Output the spectrum, design the bandpass filter by setting the high cutoff frequency, low cutoff frequency and the coefficient value, and select the IFFT button to check the filtered signal.
  • the dual demodulation mode unit simultaneously demodulates the signal in a second manner.
  • the dual demodulation mode unit uses a pre-stored signal (double modulated and stored signal) as an input signal, selects a detection button, and selects zero (zero) among the modulated signals. Signals smaller than) are set to 0 (zero) .
  • Design the filter e.g., lowpass filter
  • the data communication training mode unit 295 is a means for practicing communication of sending and receiving data between two devices, and includes a sawtooth protocol mode, a biosignal protocol mode, and a serial communication application mode.
  • a sawtooth transmission button is selected, and when an open button is selected, a communication port is opened and data is received. If no message is displayed, reset the port.
  • the open button is selected, the sawtooth wave data is outputted and confirmed, and the noise on button is pressed to confirm that the data including the noise is transmitted.
  • the stop button is selected, communication stops and the protocol is displayed in the protocol data window. Error Bit Use the ON / OFF button to adjust the Error Bit and use the File Save button to save the displayed protocol as a file.
  • the biosignal protocol mode is a means configured to transmit a signal while measuring a biosignal.
  • a communication port is opened and a biosignal detected by the biosignal measuring unit 100 is input.
  • the stop button is selected, communication stops and the protocol is displayed in the protocol data window. Analyze and print the displayed protocol.
  • HR heart rate
  • RESP is analyzed when the biosignal is respiratory signal
  • SPO2 is analyzed when the biosignal is oxygen saturation.
  • Serial communication application mode uses MATLAB and CEMTool to open serial port, receive data, send data, and close serial port.
  • the memory unit 300 includes a basic program for driving in the arithmetic processing unit 200 and includes basic data that can be used according to each training mode, for example, a sine wave.
  • the calculation result of the operation unit 200 or stores the detected bio-signals and the like.
  • the kit output unit 400 is an output unit mounted on the experimental signal 90 for digital signal processing, and the arithmetic processing is performed even when the digital signal processing kit 90 is not connected to an external computer (user's computer) 590. Output section for outputting results.
  • the kit output unit 400 may include a display unit, and in some cases, may be an output port.
  • the communication interface unit 500 is connected to the digital signal processing experiment kit 90 and an external computer (user's computer) 590 to perform digital computation processing experiments, and also performs digital signal processing experiments. It is designed to connect the kit with MATLAB, CEMTool, and other external computers to perform data communication exercises.
  • the setting unit 700 may be provided in an external computer equipped with an experiment kit 90 for digital signal processing, or may be provided in an experiment kit 90 for digital signal processing, or may be set for arithmetic processing of the operation processor 200. It is a means to input various settings according to each training mode.
  • the setting unit 700 may be formed of a keyboard, a touch pad, or the like.
  • FIG. 4 is a detailed diagram illustrating a detailed configuration of an experimental kit for digital signal processing of FIG. 1, and FIG. 5 is a detailed configuration diagram of a kit output unit of the experimental kit of FIG. 4.
  • Experimental kit for biosignal digital signal processing comprises a biosignal measuring unit 110, a digital signal processing unit 120, and a communication interface unit 130, and also the other
  • the experimental kit for biosignal digital signal processing according to the embodiment further includes a kit output unit 140.
  • the experimental kit of each embodiment according to the present invention may be configured to further include a power supply for supplying operating power to each part.
  • the biosignal measuring unit 110 may include an electrocardiogram / breath measuring unit 111a divided by electrical insulation, a body temperature measuring unit 111b, a pressure pump control unit, an exhaust valve control unit, a pressure sensor input unit 112, and a pressure sensor input unit. It is composed of an oxygen saturation measuring unit 113 composed of an infrared LED control, an optical sensor input unit.
  • the signals measured by the measuring units 111a, 111b, 112, and 113 are input to the respective digital signal processors 121, 122 through the AD converters, and are connected to the external device (for example, a computer) by serial communication through the communication interface unit. ) And the measurement data.
  • the electrocardiogram / breath measurement unit 111a includes an electrocardiogram electrode connected to an input terminal, an impedance measurer for respiration measurement, a lead selector for electrocardiogram and respiration measurement, and an amplifier for measuring an electrocardiogram and the like.
  • the ECG electrode is electrically insulated from the impedance measuring unit, and the ECG signal measured here is transmitted to the first digital signal processor 121 through an amplifier and an analog / digital converter.
  • the impedance measurer is electrically insulated from the ECG electrode and is selected by the lead selector, and the measured respiration signal is transmitted to the first digital signal processor 121 through an analog / digital converter.
  • the body temperature measuring unit 111b transmits the signal measured by the temperature sensor connected to the temperature sensor input unit to the first digital signal processing unit 121 through an analog / digital converter.
  • the blood pressure measuring unit 112 includes an exhaust valve and a pressure pump connected to the cuff, an exhaust valve control unit and a pressure pump control unit for controlling the operation of the valve and the pump, and a pressure sensor and a pressure sensor input unit for blood pressure measurement.
  • the blood pressure signal measured by the pressure sensor input unit is transmitted to the second digital signal processor 122 through an analog / digital converter.
  • the oxygen saturation measuring unit 113 is composed of an infrared LED control unit and an optical sensor input unit for measuring oxygen saturation in the blood.
  • the oxygen saturation signal measured by the optical sensor input unit is transmitted to the second digital signal processor 122 through an analog / digital converter.
  • the digital signal processor 120 includes a first digital signal processor 121 for measuring ECG and respiration and signal processing, and a second digital signal processor 122 for measuring blood oxygen saturation and blood pressure and signal processing. .
  • the first digital signal processor 121 includes an analog / digital converter and a first signal processor for digital processing of the bio signals measured by the ECG / breath measuring unit 111a and the body temperature measuring unit 111b, respectively.
  • the signal processor is communicatively connected to the communication interface 130 in an electrically insulated state.
  • the second digital signal processor 122 includes an analog / digital converter and a second signal processor for digital processing of the bio signals measured by the blood pressure measuring unit 112 and the oxygen saturation measuring unit 113, respectively.
  • the processor is connected to the communication interface 130 and provides control signals of infrared LEDs for measuring blood oxygen saturation and blood pressure, control of a pressure pump, and a control valve of an exhaust valve to each measurement unit.
  • the first digital signal processor 121 and the second digital signal processor 122 are included in the operation processor 200.
  • the first digital signal processor and the second digital signal processor are equipped with a digital signal processing experiment process using the measured bio signals.
  • This digital signal processing experiment process includes various signal processing processes such as convolution, FFT, IFFT, sampling, FIR filter, IIR filter, modulation, and demodulation on measured biosignals.
  • the digital signal processing experiment process may be an arithmetic processing unit (not shown) mounted on the external computer 590.
  • the communication interface unit 130 performs a communication interface for wired / wireless transmission of the bio signals digitally processed by the digital signal processing unit.
  • the communication interface 130 may provide a communication protocol for transmitting the measured bio signals to a personal computer through serial communication. It consists of interface means such as a USB port to perform.
  • the communication interface unit 130 is the same as the communication interface unit 500 of FIG. 1.
  • Kit output unit 140 is the same as the kit output unit 400 of FIG.
  • the output unit 600 processes and analyzes the biometric information transmitted through the communication interface 130 in various forms in real time and displays the results of the analysis.
  • the display unit may further include a storage unit to be implemented to store the real-time analysis results.
  • the display unit of such a configuration may be implemented as display means of an external device connected to serial communication, or may be implemented as display means provided in the experiment kit itself.
  • a state implemented as a display means of an external device is described as an example, but is not limited thereto.
  • FIG. 6 is an operation flowchart schematically illustrating a digital signal processing experiment process using a biosignal in an experiment kit according to the present invention
  • FIG. 7 is a reference view illustrating digital signal processing methods that can be performed as an experiment kit according to the present invention. to be.
  • the experiment kit 90 for digital signal processing is connected to an external computer 590 through a communication interface 500 such as a USB port (S101), and the driving program of the experiment kit 90 for digital signal processing is performed. That is, the digital signal processing experiment program is executed (S102). That is, the arithmetic processing unit 200 of the digital signal processing experiment kit 90 reads a drive program, that is, a digital signal processing experimental program, from the memory unit 300 and initializes it to a state necessary for practice.
  • an analysis method for a biological signal to be measured is selected (S103). That is, the arithmetic processing unit 200 of the digital signal processing experimental kit 90 waits for one of the analysis methods of the digital signal processing experimental kit 90 to be selected as shown in FIG. 7.
  • the sensor is attached to the human body (S104), and the measurement of the desired biosignal is performed in the biosignal measuring unit 100 and the measured biosignal is measured. Receive the data (S105).
  • the ECG, blood pressure (NIBP), respiration (RESP), blood oxygen saturation (SpO2), body temperature (TEMP) of the appropriate sensor according to the desired biosignals to attach the appropriate biosignals can be detected. That is, the calculation processing unit 200 of the digital signal processing experiment kit 90 receives the biosignal detected from the biosignal measuring unit 100.
  • the calculation processing unit 200 of the digital signal processing experiment kit 90 performs the digital signal processing experiment according to the analysis method set in the analysis method setting step S103 by using the received biosignal (S106). ).
  • the arithmetic processing unit 200 of the experiment kit 90 for digital signal processing outputs and plots the data of the result analyzed in the digital signal processing step S106 to the output unit 600 or the kit output unit 400 (S107).
  • the digital signal processing methods that can be performed as the experimental kit of the present invention include obtaining a biosignal (S201), a Fourier series and a Fourier transform (S202), a time domain representation of a signal (S203), and a frequency domain representation of a signal (S204).
  • S201 a biosignal
  • S202 Fourier series and a Fourier transform
  • S203 a time domain representation of a signal
  • S204 a frequency domain representation of a signal
  • Sampling Discrete-Time Convolution S205
  • Digital Filter Design Basics S206
  • FIR Filter Precision Design FIR Filter Using Window Function
  • IIR Filter Precision Design IIR Filter Using Window Function
  • Filter application peak detection
  • S210 signal modulation / demodulation data communication
  • S211 data communication
  • the time domain representation of the signal refers to the practice performed in the signal of the processing unit 200 and the time domain representation practice mode unit 230 of the system, the frequency of the signal
  • the sampling discrete time convolution (S205) is a sampling training mode of the computation processing unit 200
  • the basic (S206) of the digital filter design refers to the practice performed in the filter basic training mode unit 260 of the calculation processing unit 200
  • FIR filter precision design using the window function FIR filter (S207) refers to the practice to be performed in the FIR filter training mode unit 270 of the calculation processing unit 200
  • IIR filter precision design IIR filter using a window function
  • the practice performed in the training mode unit 280, and the filter application (peak detection) refers to the practice performed in the
  • FIG. 8 is an explanatory diagram for describing a biosignal detection training mode unit of the arithmetic processing unit of FIG. 2.
  • the biosignal detection training mode unit 210 receives the biosignal detected from the biosignal measurement unit 100, outputs the biosignal to the kit output unit 400, and outputs the biosignal to the output unit 600 through the communication interface 500. Output to the memory unit 300 in some cases.
  • FIG. 9 is an explanatory diagram for illustrating a signal principle training mode unit of the arithmetic processing unit of FIG. 2.
  • Fourier series can be performed through harmonic experiments or speech synthesis experiments.
  • harmonic experiments combine the sine waves linearly by setting the value of n (order of harmonics) after the user enters the coefficients, after the user enters the Fourier series coefficients of the square wave. By forming a predetermined waveform, it is output to the kit output unit 400 or output unit 600.
  • a predetermined waveform is formed by setting the number of data, the value of F, the coefficients a and b, and combining the sinusoids linearly.
  • the kit output unit 400 or the output unit 600 outputs to the kit output unit 400 or the output unit 600 to check the frequency of the synthesized regular polarized wave, and to check the sound of the synthesized wave.
  • Fourier transform can be practiced through frequency component identification experiments and experiments by measuring biological signals.
  • a frequency component checking button is selected to output a graph or data for checking the frequency component for checking the square frequency equation of the square wave to the kit output unit 400 or the output unit 600, and the user sets the unit.
  • Receive the values of the time (T) and the frequency range (f) input through the 700 and outputs the Fourier transformed graph or data according to the setting to the kit output unit 400 or output unit 600 to the user Check the frequency domain. As a result, the frequency component over time and frequency range can be identified.
  • FIG. 10 is an explanatory diagram for describing a signal of the arithmetic processing unit of FIG. 2 and a time domain representation training mode unit of the system.
  • the time domain representation training mode 230 of the signal and the system may be used to perform continuous signal convolution or circuit analysis-related exercises using convolution.
  • the time domain representation training mode unit 230 of the signal and the system receives the system signal of the black box set by the setting unit 700, and the user selects an input signal (memory unit 300). Stored in the signal, a measured bio-signal, a signal defined by a user, etc.) and the output by convolution as shown in Equation (2).
  • the mode unit is driven, a black box is displayed on the output unit 600, and the input signal is set at the black box input terminal through the setting unit 700.
  • the time domain representation training mode unit 230 of the signal and the system may analyze the RL circuit or the RC circuit.
  • FIG. 11 is an explanatory diagram for explaining a signal processing unit of FIG. 2 and a frequency domain representation training mode unit of the system;
  • Frequency domain representation training mode of the signal and the system 240 is configured to practice the frequency domain representation of the various signals and systems, and can perform a square wave signal and system-related exercises or bio-signal and system-related exercises.
  • the square wave signal and system related exercises are designed to perform the experiment of understanding the transformation of the pre-transformation and the system understanding experiment using the virtual signal.
  • the frequency domain representation training mode 240 of the signal and the system includes a synthesized wave formation mode and a synthesized wave spectrum confirmation mode, and each of the synthesized wave formation modes has different predetermined frequency ranges.
  • the synthesized wave button is selected, the first signal, the second signal, the third signal, etc. are synthesized and represented by a graph or a formula to output the kit output unit 400 or output. Output to the unit 600, output to the memory unit 300.
  • the synthesized wave generated in the composite wave forming mode is read and the spectrum is output.
  • One of the filter types one of LPF and BPF, is selected and the cutoff frequency is selected. If set, the filtered output to the kit output unit 400 or output unit 600.
  • the virtual signal is generated, and when the frequency button is selected, the frequency component of the virtual signal is output, and one of the low pass filter, the high pass filter, the band pass filter, and the band stop filter.
  • filtering is performed and the result is output to the kit output unit 400 or the output unit 600.
  • Bio signal and system related exercises are experiments to understand the system using various biosignals, and include the practice of removing noise from voice and other biological signals, and using recorded voice.
  • one signal selected from electrocardiogram, respiratory rate, and oxygen saturation is a signal including noise (for example, power supply noise and moving artifact) through the biosignal measurement unit 100.
  • noise for example, power supply noise and moving artifact
  • the frequency button is selected, the frequency component of the signal is output, and one filter type and a cutoff frequency of the low pass filter, the high pass filter, the band pass filter, and the band stop filter are input to filter the filter. And output the result to the kit output unit 400 or the output unit 600.
  • the practice using the recorded voice has a predetermined sampling frequency and receives a voice signal through the biosignal measuring unit 100 for a predetermined recording time, and includes one of a low pass filter, a high pass filter, a band pass filter, and a band stop filter.
  • the filter type and the cutoff frequency are input and the filter is set, the received voice signal is filtered and the result is output to the kit output unit 400 or the output unit 600, and also filtered through a speaker (not shown). Outputs the previous voice and the filtered voice.
  • FIG. 12 is an explanatory diagram for illustrating a sampling training mode unit of the arithmetic processing unit of FIG. 2.
  • the sampling training mode unit 250 includes training related to sampling theorem and training related to convolution.
  • Sampling theorem-related exercises include sampling and quantization-related exercises.
  • Sampling exercises include the pre-stored sine wave waveform or biosignal received through the biosignal measurement unit 100 as an input signal.
  • the sampling frequency is set differently in consideration of the Nyquist sampling rate, and when the sampling button is input, the spectrum of each input signal having a different sampling frequency is output. For example, a sampling frequency of 28, 64, 32, 16 Hz data can be compared. In this mode, you can enter the sampling rate, frequency, and amplitude values and look at the distortion level of the input signal.
  • a pre-stored waveform or a biosignal received through the biosignal measuring unit 100 is read as an input signal, and when a signal button is selected, a spectrum of the input signal is output. If the sampling rate, frequency, and amplitude are set differently, and the sampling button is input, the spectrum of each input signal having a different sampling frequency is output. Select the bit button and check the result. Here you can compare 2, 3, 6, and 8 bit data.
  • Convolution related exercises include experiments on understanding discrete signal convolution flows and sampling signal restoration experiments using convolution.
  • the experiment of understanding the discrete signal convolution flow includes the convolution practice using the shift operation and the convolution practice using the impulse function.
  • the convolution result is obtained.
  • the first step to the fourth stem described in the impulse response training mode of the time domain representation training mode unit 230 of the signal and the system can be performed step by step to check the result.
  • we also design a lowpass filter whose received pleated biosignal and highpass frequency are 1/2 of the sampling rate, and convolution when the convolution button is selected, and combines the result with the biosignal before filtering. Output it for comparison.
  • the sampling signal restoration experiment using convolution produces a sampled input signal by selecting a sampling button, designs a lowpass filter with a high-pass cutoff frequency 1/2 of the sampling rate, and selects a system button to select a system in the time domain.
  • a sampling button designs a lowpass filter with a high-pass cutoff frequency 1/2 of the sampling rate, and selects a system button to select a system in the time domain.
  • convolution button is selected, convolution is performed to output the result and the bio signal before filtering together for comparison.
  • FIG. 13 is an explanatory diagram for illustrating a filter basic training mode unit of the arithmetic processing unit of FIG. 2.
  • the filter basic training mode unit 260 includes a basic training of the FIR filter and a basic training of the IIR filter.
  • the basic practice of the FIR filter is to perform a moving average filter experiment using a virtual signal or a biosignal.
  • the basic practice of the FIR filter is based on a signal pre-stored in the memory unit 300 or a signal detected by the biosignal measurement unit 100 as an input signal. If you select the FIR filter and input the coefficients of the differential equation of the FIR filter, the spectrum of the input signal is output, the spectrum of the set filter is output, and if the convolution button is selected, the convolutional result, that is, the input signal is filtered Output the result.
  • the basic practice of the IIR filter is to use the virtual signal or to perform the IIR filter practice using the differential equation using the biosignal as an input signal.
  • the basic practice of the IIR filter is to use the signal pre-stored in the memory unit 300 or the signal detected from the biosignal measurement unit 100 as an input signal.
  • the coefficient is input, the spectrum of the set filter is output to check the frequency response.
  • the convolution button is selected, the convolutional result, that is, the result of filtering the input signal is output.
  • FIG. 14 is an explanatory diagram for explaining an FIR filter training mode unit of the calculation processing unit of FIG. 2.
  • the FIR filter training mode unit 270 uses a virtual signal or a biosignal as an input signal, and performs a training on an FIR filter using a window function, and includes a window function design mode and an input signal filtering mode.
  • the window function design mode of the FIR filter training mode unit 270 is designed to design a FIR filter using a window, and when a cutoff frequency is input and a filter button is selected, LPF is implemented, and then a filter IFFT If the button is selected, the filter designed in the frequency domain can be checked in the time domain, and the desired window functions in the window type category are: rectangular window function, triangle (bartlet) window function, hanning window function, hamming window function, blackman window function, One of Kaiser's window functions is determined and the window width is determined by the window width, the window function is checked when the window button is selected, and then the windowing button is selected to apply the window function to the designed filter. If the window FFT button is selected, the window function designed in the frequency domain can be checked.
  • a signal pre-stored in the memory unit 300 or a signal detected by the biosignal measurement unit 100 is used as an input signal, and a signal frequency button is selected. You can check this by outputting the spectrum of the signal, and select the filter frequency button to read the LPF designed in the window function design mode, and if the filter button is selected, the input signal is filtered by the LPF, and the filtered result in the frequency domain is displayed. You can check this by outputting the result, and by filtering the result in the time domain by selecting the IFFT button.
  • FIG. 15 is an explanatory diagram for explaining an IIR filter training mode unit of the calculation processing unit of FIG. 2.
  • the IIR filter training mode unit 280 includes filter design training using poles and zeros and training using IIR filters.
  • the basic method of designing an IIR filter is, firstly, designing an analog lowpass filter, specifying the specification of an analog filter with desired characteristics, and second, applying a filter transformation (s ⁇ z) to obtain a digital lowpass filter. Third, frequency band conversion is applied to obtain another filter from the digital lowpass filter.
  • Filter design practice using pole and zero inputs coefficient values of (first- or second-order) differential equations indicates poles and zeros on Z domain graphs, outputs frequency response curves as magnitude spectrum graphs, or the differential equations.
  • the poles and zeros are inputted to indicate the poles and zeros on the Z domain graph, the frequency response curve is output as a magnitude spectrum graph, and the coefficients of the differential equation can be obtained using the poles and zeros by selecting the coefficient button. .
  • a signal pre-stored in the memory unit 300 or a signal detected from the biosignal measuring unit 100 is used as an input signal, and when a signal frequency button is selected, the spectrum of the signal is output and confirmed. Coefficients of differential or quadratic differential equations are input, the filter's frequency response curve is output when the filter button is selected, and the filtered result is output and confirmed when the result button is selected.
  • 16 is an explanatory diagram for explaining a filter application training mode unit of the arithmetic processing unit of FIG. 2.
  • the filter application training mode unit 285 includes a peak detection training using a filter, and includes a peak detection training using an ECG signal and a peak detection training using a pulse wave signal.
  • Peak detection practice using an ECG signal includes an ECG preprocessing mode, an ECG differential mode, and an ECG peak detection mode.
  • the ECG preprocessing mode stores the detected signal from the biosignal measuring unit 100, reads the stored ECG signal, and when the frequency button is selected, outputs the frequency component of the waveform of the ECG signal, inputs the filter type, and the low pass frequency or Input the high frequency cutoff frequency and select the filter button to output the filtered result.
  • the ECG derivative mode selects one of the two-point differential algorithm mode, the three-point central differential algorithm mode, and the least-squares polynomial differential approximation mode to the ECG signal filtered (bandpassed) in the ECG preprocessing mode. Input the coefficient of the differential filter in the filter coefficient input window and output the result, and output the result of calculating the squared value of the differential waveform.
  • the ECG peak detection mode reads the squared data output in ECG differential mode, sets the number in the sample number input window, sets the moving average filter, performs moving average filtering, and outputs the result.
  • Electrocardiogram peak (QRS) A threshold for detection is set, and when a QRS exceeding the threshold is detected, a beep sound is displayed on the graph. In addition, the number of electrocardiogram peaks (QRS) during one second is counted and output as a heart rate.
  • Peak detection practice using the pulse wave signal includes a pulse wave preprocessing mode, a pulse wave differential mode, and a pulse wave peak detection mode.
  • the pulse wave preprocessing mode stores the pulse wave signal detected from the biosignal measurement unit 100, reads the stored pulse wave signal, and when the frequency button is selected, outputs the frequency component of the waveform of the pulse wave signal, inputs a filter type, and a low pass cutoff frequency. Alternatively, if the filter button is selected by entering the high frequency cutoff frequency, the filtered result is output.
  • the pulse wave differential mode selects one of the two-point differential algorithm mode, the three-point central differential algorithm mode, and the least-squares polynomial differential approximation mode to the ECG signal filtered (bandpassed) in the pulse wave preprocessing mode. Input the coefficient of the differential filter in the filter coefficient input window and output the result, and output the result of calculating the squared value of the differential waveform.
  • the pulse wave peak detection mode reads the squared data output in the pulse wave differential mode, sets the number in the sample number input window, sets the moving average filter, performs moving average filtering, and outputs the result.
  • a threshold is detected and a pulse wave peak exceeding the threshold is detected, a beep sound is displayed on the graph.
  • the number of pulse wave peaks during one second is counted and output as heart rate.
  • FIG. 17 is an explanatory diagram for explaining a demodulation training mode unit of a signal of the arithmetic processing unit of FIG. 2.
  • the signal demodulation demodulation mode 290 is configured to perform a demodulation demodulation of a virtual signal or a biosignal, and includes a demodulation demodulation exercise using a virtual signal and a demodulation demodulation exercise using a biosignal.
  • Virtual signal modulation and demodulation practice can be performed by demodulating and demodulating a virtual signal, modulating a sine wave as a virtual signal, outputting a sound through a speaker, recording the output sound on another computer, and detecting and filtering the recorded sound by demodulating it.
  • the demodulated sound is compared with the pre-modulated sound through the speaker.
  • Modulation and demodulation practice using biosignals involves experiments that measure biosignals, modulate measured biosignals, and demodulate and compare the modulated biosignals, simultaneously measure two biosignals and modulate both signals. The demodulation may then be performed to compare with the original biosignals.
  • the signal demodulation training mode unit 290 includes a modulation mode unit, a demodulation mode unit, a double modulation mode unit, and a dual demodulation mode unit.
  • the modulation mode unit is a signal pre-stored in the memory unit 300, or a signal made by the signal or the signal measured by the chanchae signal measuring unit 100 as an input signal, when a carrier button is selected, a predetermined sine wave is set as a carrier signal.
  • a predetermined sinusoidal signal carrier signal
  • the demodulation mode unit uses a pre-stored signal (modulated and stored signal) as an input signal, selects a detection button, and sets a signal smaller than zero among the modulated signals to zero. Design a filter (eg low pass filter) by setting the cutoff frequency, count value and (sampling frequency), and select the OK button to output the filtered signal and check the filtered signal through the speaker.
  • a filter eg low pass filter
  • the dual modulation mode unit simultaneously measures and stores two biosignals, for example, ECG and oxygen saturation, reads the two biosignals thus measured, modulates the two biosignals with a predetermined sine wave signal, and outputs the modulated biosignals. do.
  • two biosignals for example, ECG and oxygen saturation
  • the dual demodulation mode unit sequentially reads and demodulates a dual modulated signal in a first manner, and uses a signal (modulated and stored signal) previously stored in the memory unit 300 as an input signal, and selects a frequency button to Output the spectrum, design the bandpass filter by setting the high cutoff frequency, low cutoff frequency and the coefficient value, and select the IFFT button to check the filtered signal.
  • the dual demodulation mode unit simultaneously demodulates the signal in a second manner.
  • the dual demodulation mode unit uses a pre-stored signal (double modulated and stored signal) as an input signal, selects a detection button, and selects zero (zero) among the modulated signals. Signals smaller than) are set to 0 (zero) .
  • Design the filter e.g., lowpass filter
  • FIG. 18 is an explanatory diagram for explaining a data communication training mode unit of the arithmetic processing unit of FIG. 2.
  • the data communication training mode unit 295 includes communication protocol training of arbitrary signals and communication protocol training of live signals.
  • Arbitrary signal communication protocols practice sawtooth wave experiments to understand the structure of the protocol, and confirm the waveform and protocol
  • biological protocol communication protocols measure the biosignals such as electrocardiogram, respiration, oxygen saturation, and confirm the waveform and protocol.
  • the data communication training mode unit 295 includes a sawtooth protocol mode and a biosignal protocol mode.
  • a sawtooth transmission button is selected, and when an open button is selected, a communication port is opened and data is received. If no message is displayed, reset the port.
  • the open button is selected, the sawtooth wave data is outputted and confirmed, and the noise on button is pressed to confirm that the data including the noise is transmitted.
  • the stop button is selected, communication stops and the protocol is displayed in the protocol data window. Error Bit Use the ON / OFF button to adjust the Error Bit and use the File Save button to save the displayed protocol as a file.
  • the biosignal protocol mode is a means configured to transmit a signal while measuring a biosignal.
  • a communication port is opened and a biosignal detected by the biosignal measuring unit 100 is input.
  • the stop button is selected, communication stops and the protocol is displayed in the protocol data window. Analyze and print the displayed protocol.
  • HR heart rate
  • RESP is analyzed when the biosignal is respiratory signal
  • SPO2 is analyzed when the biosignal is oxygen saturation.
  • the present invention relates to a signal processing experiment kit integrating a digital signal processing experiment process based on a bio-signal into a single kit, and real-time digital signal processing and experiment process through a bio-signal measuring device and a computer interface in a university or laboratory. It can be used as an experiment kit for digital signal processing that can be directly applied to the

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Abstract

La présente invention concerne un kit expérimental destiné à traiter le signal numérique d'un signal biologique, ledit kit permettant de mesurer en temps réel différents signaux biologiques générés par le corps, par exemple un signal d'électrocardiogramme, un signal de mesure de la saturation en oxygène, un signal de mesure de la pression artérielle, un signal de mesure de la température corporelle et autres, en soumettant lesdits signaux biologiques à un traitement de signal numérique en temps réel ; un tel kit permet d'améliorer le degré de compréhension physique des procédures de traitement du signal numérique lors d'expériences utilisant des signaux biologiques. Pour ce faire, la présente invention comporte une partie de mesure des signaux biologiques, une partie de traitement des opérations, une partie mémoire, une partie d'envoi en sortie et une partie de réglage. La partie de traitement des opérations comprend une partie (210) expérimentale de détection des signaux biologiques, une partie (220) de test des principes des signaux, une partie (230) de détermination de la région temporelle d'expression d'un signal et d'un système, une partie (240) de détermination de la région fréquentielle d'expression d'un signal et d'un système, une partie (250) d'échantillonnage, une partie (260) de filtrage de base, une partie (270) de filtrage FIR, une partie (280) de filtrage IIR, une partie (285) d'application de filtre, une partie (290) de modulation/démodulation du signal et une partie (295) de communication des données.
PCT/KR2011/006828 2010-09-16 2011-09-16 Kit expérimental pour le traitement numérique de signaux biologiques WO2012036490A2 (fr)

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CN103542883A (zh) * 2012-07-15 2014-01-29 江苏锡光科教设备有限公司 实现化学实验数字化的传感系统
CN106205305A (zh) * 2016-06-24 2016-12-07 南京工程学院 一种电力电子及电机拖动实验平台及运行方法
CN114431848A (zh) * 2022-01-17 2022-05-06 厦门大学 一种无线水生生物监测系统及方法

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KR101417591B1 (ko) * 2013-02-08 2014-07-08 건양대학교산학협력단 생체신호측정모듈용 멀티스마트 교육실습 시스템
KR101508436B1 (ko) * 2013-10-10 2015-04-07 재단법인대구경북과학기술원 신호 검출 최적화 장치 및 방법
US11064906B2 (en) 2018-01-29 2021-07-20 Electronics And Telecommunications Research Institute Method and apparatus for determining respiration state based on plurality of biological indicators calculated using bio-signals
KR102379766B1 (ko) * 2020-05-06 2022-03-29 재단법인 아산사회복지재단 인공지능 기반의 심박출량 관련데이터 분석 방법, 프로그램 및 시스템
KR102368146B1 (ko) * 2020-06-30 2022-03-02 (주)메디코슨 산소포화도 측정 장치 및 그 제어 방법

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CN106205305A (zh) * 2016-06-24 2016-12-07 南京工程学院 一种电力电子及电机拖动实验平台及运行方法
CN114431848A (zh) * 2022-01-17 2022-05-06 厦门大学 一种无线水生生物监测系统及方法

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