A strategy combining intrinsic time-scale decomposition and a feedforward neural network for automatic seizure detection
| Autor: | Lijun Yang, Sijia Ding, Xiaohui Yang, Hao-Min Zhou |
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| Rok vydání: | 2019 |
| Předmět: |
Time Factors
Physiology Computer science Feature vector 0206 medical engineering Feature extraction Biomedical Engineering Biophysics 02 engineering and technology Electroencephalography Instantaneous phase 03 medical and health sciences Automation 0302 clinical medicine Discriminative model Seizures Physiology (medical) medicine Humans Ictal Artificial neural network medicine.diagnostic_test business.industry Pattern recognition Signal Processing Computer-Assisted 020601 biomedical engineering Feedforward neural network Artificial intelligence Neural Networks Computer business 030217 neurology & neurosurgery |
| Zdroj: | Physiological measurement. 40(9) |
| ISSN: | 1361-6579 |
| Popis: | Epilepsy is a common neurological disorder which can occur in people of all ages globally. For the clinical treatment of epileptic patients, the detection of epileptic seizures is of great significance. Objective Electroencephalography (EEG) is an essential component in the diagnosis of epileptic seizures, from which brain surgeons can detect important pathological information about patient epileptiform discharges. This paper focuses on adaptive seizure detection from EEG recordings. We propose a new feature extraction model based on an adaptive decomposition method, named intrinsic time-scale decomposition (ITD), which is suitable for analyzing non-linear and non-stationary data. Approach Firstly, using the ITD technique, every EEG recording is decomposed into several proper rotation components (PRCs). Secondly, the instantaneous amplitudes and frequencies of these PRCs can be calculated and then we extract their statistical indices. Furthermore, we combine all these statistical indices of the corresponding five PRCs as the feature vector of each EEG signal. Finally, these feature vectors are fed into a feedforward neural network (FNN) classifier for EEG classification. The whole process of feature extraction proposed in this paper only involves one parameter and the role of the ITD method is based on a piecewise linear function, which makes the computation of the model simple and fast. More useful information for classification can be obtained since we take advantage of both instantaneous amplitude and instantaneous frequency for feature extraction. Main results We consider the 17 classification problems which contain normal versus epileptic, non-seizure versus seizure and normal versus interictal versus ictal using a FNN classifier which only contains one hidden layer. Experimental results show that the proposed method can catch the discriminative features of EEG signals and obtain comparable results when compared with state-of-the-art detection methods. Significance Therefore, the proposed system has a great potential in real-time seizure detection and provides physicians with a real-time diagnostic aid in their practice. |
| Databáze: | OpenAIRE |
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