Wang et al., 2012 - Google Patents
Performance evaluation for epileptic electroencephalogram (EEG) detection by using Neyman–Pearson criteria and a support vector machineWang et al., 2012
- Document ID
- 10381782207132076513
- Author
- Wang C
- Zhang C
- Zou J
- Zhang J
- Publication year
- Publication venue
- Physica A: Statistical Mechanics and its Applications
External Links
Snippet
The diagnosis of several neurological disorders is based on the detection of typical pathological patterns in electroencephalograms (EEGs). This is a time-consuming task requiring significant training and experience. A lot of effort has been devoted to developing …
- 238000001514 detection method 0 title abstract description 86
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00496—Recognising patterns in signals and combinations thereof
- G06K9/00536—Classification; Matching
- G06K9/0055—Classification; Matching by matching signal segments
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Savadkoohi et al. | A machine learning approach to epileptic seizure prediction using Electroencephalogram (EEG) Signal | |
| Liang et al. | An unsupervised EEG decoding system for human emotion recognition | |
| Patidar et al. | Detection of epileptic seizure using Kraskov entropy applied on tunable-Q wavelet transform of EEG signals | |
| Ahmadi et al. | EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features | |
| Ackermann et al. | EEG-based automatic emotion recognition: Feature extraction, selection and classification methods | |
| Al Ghayab et al. | Epileptic seizures detection in EEGs blending frequency domain with information gain technique | |
| Polat et al. | Artificial immune recognition system with fuzzy resource allocation mechanism classifier, principal component analysis and FFT method based new hybrid automated identification system for classification of EEG signals | |
| Gokgoz et al. | Comparison of decision tree algorithms for EMG signal classification using DWT | |
| Pachori et al. | Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions | |
| Kumar et al. | Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine | |
| Übeyli | Lyapunov exponents/probabilistic neural networks for analysis of EEG signals | |
| Giannakakis et al. | Methods for seizure detection and prediction: an overview | |
| Exarchos et al. | EEG transient event detection and classification using association rules | |
| Yan et al. | Automatic seizure detection using Stockwell transform and boosting algorithm for long-term EEG | |
| Rasekhi et al. | Epileptic seizure prediction based on ratio and differential linear univariate features | |
| Wang et al. | Feature extraction and recognition of epileptiform activity in EEG by combining PCA with ApEn | |
| Jing et al. | Classification and identification of epileptic EEG signals based on signal enhancement | |
| Wang et al. | Performance evaluation for epileptic electroencephalogram (EEG) detection by using Neyman–Pearson criteria and a support vector machine | |
| Rajaguru et al. | KNN classifier and K-means clustering for robust classification of epilepsy from EEG signals. A detailed analysis | |
| Nanthini et al. | Epileptic seizure detection and prediction using deep learning technique | |
| Bongiorni et al. | Evaluation of recurrent neural networks as epileptic seizure predictor | |
| Jumaah et al. | Epileptic seizures detection using DCT-II and KNN classifier in long-term EEG signals | |
| Asemi et al. | Improving EEG signal-based emotion recognition using a hybrid GWO-XGBoost feature selection method | |
| Kumar et al. | Current trends in feature extraction and classification methodologies of biomedical signals | |
| Mehla et al. | An efficient classification of focal and non-focal EEG signals using adaptive DCT filter bank |