Wang et al., 2017 - Google Patents
Integrating channel selection and feature selection in a real time epileptic seizure detection systemWang et al., 2017
- Document ID
- 4434777310538708144
- Author
- Wang H
- Shi W
- Choy C
- Publication year
- Publication venue
- 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
External Links
Snippet
Automated real time seizure detection is difficult since detection sensitivity, false detection rate and seizure onset detection latency need to be considered simultaneously. Traditional pattern recognition and classification system usually suffers huge performance variation due …
- 238000001514 detection method 0 title abstract description 39
Classifications
-
- 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
- G06K9/6279—Classification techniques relating to the number of classes
-
- 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
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
-
- 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
- G06K9/00503—Preprocessing, e.g. filtering
-
- 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
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- 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
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
- G06K9/00268—Feature extraction; Face representation
- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- 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
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
- A61B5/0402—Electrocardiography, i.e. ECG
- A61B5/0452—Detecting specific parameters of the electrocardiograph cycle
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ghosh et al. | Automatic eyeblink and muscular artifact detection and removal from EEG signals using k-nearest neighbor classifier and long short-term memory networks | |
| CN112244873B (en) | Electroencephalogram space-time feature learning and emotion classification method based on hybrid neural network | |
| Peng et al. | OGSSL: A semi-supervised classification model coupled with optimal graph learning for EEG emotion recognition | |
| Page et al. | Utilizing deep neural nets for an embedded ECG-based biometric authentication system | |
| Zhao et al. | An explainable attention-based TCN heartbeats classification model for arrhythmia detection | |
| Faziludeen et al. | ECG beat classification using wavelets and SVM | |
| Sahu et al. | An efficient method for detection and localization of myocardial infarction | |
| CN117503057B (en) | Epileptic seizure detection device and medium for constructing brain network based on high-order tensor decomposition | |
| Pandey et al. | Epileptic seizure classification using battle royale search and rescue optimization-based deep LSTM | |
| Wang et al. | Integrating channel selection and feature selection in a real time epileptic seizure detection system | |
| Barhatte et al. | QRS complex detection and arrhythmia classification using SVM | |
| Hwaidi et al. | A noise removal approach from eeg recordings based on variational autoencoders | |
| Mehta et al. | Support Vector Machine for Cardiac Beat Detection in Single Lead Electrocardiogram. | |
| Gupta et al. | Atrial fibrillation detection using electrocardiogram signal input to LMD and ensemble classifier | |
| Kumari et al. | Optimization of multi-layer perceptron neural network using genetic algorithm for arrhythmia classification | |
| Salafian et al. | Efficient epileptic seizure detection using CNN-aided factor graphs | |
| CN116401619B (en) | Emotion recognition method, device and equipment suitable for multi-mode physiological signals | |
| Singh et al. | Extreme gradient boosting algorithm for automatic cardiac arrhythmia classification | |
| Durga et al. | Cardiac arrhythmia classification using sequential feature selection and decision tree classifier method | |
| Chen et al. | Parametric canonical correlation analysis | |
| Schetinin et al. | A Bayesian model averaging methodology for detecting EEG artifacts | |
| Khan et al. | EEG signal based schizophrenia recognition by using VMD rose spiral curve butterfly optimization and machine learning | |
| Salah et al. | Cardiac arrhythmia classification by wavelet transform | |
| García-Aquino et al. | Classification of cardiac arrhythmias using machine learning algorithms | |
| Liu et al. | Multi-scale wavelet kernel extreme learning machine for EEG feature classification |