Abstract
Electroencephalography (EEG) signals are frequently used for the detection of epileptic seizures. In this chapter, advanced signal analysis methods such as Empirical Mode Decomposition (EMD), Ensembe (EMD), Dynamic mode decomposition (DMD), and Synchrosqueezing Transform (SST) are utilized to classify epileptic EEG signals. EMD and its derivative, EEMD are recently developed methods used to decompose nonstationary and nonlinear signals such as EEG into a finite number of oscillations called intrinsic mode functions (IMFs). In this study multichannel EEG signals collected from epilepsy patients are decomposed into IMFs, and then essential IMFs are selected. Finally, time- and spectral-domain, and nonlinear features are extracted from selected IMFs and classified. DMD is a new matrix decomposition method proposed as an iterative solution to problems in fluid flow analysis. We present single-channel, and multi-channel EEG based DMD approaches for the analysis of epileptic EEG signals. As a third method, we use the SST representations of seizure and pre-seizure EEG data. Various features are calculated and classified by Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Naive Bayes (NB), Logistic Regression (LR), Boosted Trees (BT), and Subspace kNN (S-kNN) to detect pre-seizure and seizure signals. Simulation results demonstrate that the proposed approaches achieve outstanding validation accuracy rates.
Highlights
Epilepsy, affecting approximately 4 and 10 per 1000 people of the world’s population, is one of the most common acute neurological diseases
For the SCDMD and PSD approaches, the classification results of the feature set created by combining the features obtained from the Left Hemisphere (Fp1-F7, F7-T1, T1-T3, T3-T5, Fp1-F3), Right hemisphere (Fp2-F8, F8-T2, T2-T4, T4-T6, Fp2-F4), and both hemisphere (Fp1-F7, F7-T1, T1-T3, T3-T5, Fp1-F3, Fp2-F8, F8-T2, T2-T4, T4T6, Fp2-F4) channels separately are denoted with “Left Hems“, “Right Hems “and “Two Hems“, while the same components show the classification results of dynamic mode decomposition (DMD) features obtained from the EEG data matrix created using the respective hemisphere channels in the MC-DMD approach
While the k-Nearest Neighbor (kNN) classifier is yield to highest classification accuracy 94.1% and F1-score 95.5% for subband power-based feature set obtained from the Left Hems of single-channel DMD approach (SC-DMD) approach, the maximum 92.2% ACC and 93.9% F1-score values are achieved with the Support Vector Machine (SVM) classifier using the moment-based feature set of the SCDMD approach
Summary
Epilepsy, affecting approximately 4 and 10 per 1000 people of the world’s population, is one of the most common acute neurological diseases. EEG is the most frequently used technique for the diagnosis of epilepsy, prediction, detection, and classification of epileptic seizures owing to cost, safety, and easy applicability [1, 2]. In order to detect or monitor epilepsy patients, long-term electroencephalogram (EEG) signals, which are records of the electrical activity generated by the brain, should be inspected visually by expert neurologists. This examination method is very time-consuming, bothersome, not efficient, and subjective process. Epilepsy - Update on Classification, Etiologies, Instrumental Diagnosis and Treatment methods for automatic seizure prediction and detection from epileptic EEG signals has become an active research field [2–5]. Seizure prediction and detection studies have been carried out using successful signal processing approaches in which many spectral, temporal, nonlinear, and statistical properties are calculated
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