Key result
A strategy combining intrinsic time-scale decomposition and a feedforward neural network obtained comparable results to state-of-the-art methods for automatic seizure detection from EEG signals.
Why the study?
Detection of epileptic seizures from EEG recordings is of great clinical significance, motivating the development of adaptive feature extraction and classification methods suitable for non-linear and non-stationary data.
Does an ITD-based feature extraction model with an FNN classifier improve automatic seizure detection from EEG recordings compared to state-of-the-art methods?
Does an ITD-based feature extraction model with an FNN classifier improve automatic seizure detection from EEG recordings compared to state-of-the-art methods?
The proposed ITD and FNN-based system has potential as a real-time diagnostic aid for automatic seizure detection from EEG recordings.
May aid EEG seizure detection in epilepsy; leaves open prospective validation before clinical use.
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.
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Yang et al. (2019) studied Epileptic seizures. Intrinsic time-scale decomposition (ITD) and feedforward neural network (FNN) vs. State-of-the-art detection methods was evaluated on Seizure detection classification (normal vs epileptic, non-seizure vs seizure, normal vs interictal vs ictal). A strategy combining intrinsic time-scale decomposition and a feedforward neural network obtained comparable results to state-of-the-art methods for automatic seizure detection from EEG signals.
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