Different combinations of discrete wavelet transform features and machine learning classifiers produced varying results for 16 different 2-class and 3-class epileptic seizure classification challenges.
Does a machine learning approach using DWT and various classifiers improve the detection of epileptic seizures from EEG signals?
The study identifies combinations of DWT features and machine learning classifiers for automated epileptic seizure detection from EEG signals.
Epileptic patients suffer from an epileptic brain seizure caused by the temporary and unpredicted electrical interruption. Conventionally, the electroencephalogram (EEG) signals are manually studied by medical practitioners as it records the electrical activities from the brain. This technique consumes a lot of time, and the outputs are unreliable. In a bid to address this problem, a new structure for detecting an epileptic seizure is proposed in this study. The EEG signals obtained from the University of Bonn, Germany, and real-time medical records from the Senthil Multispecialty Hospital, India, were used. These signals were disintegrated into six frequency subbands that employed discrete wavelet transform (DWT) and extracted twelve statistical functions. In particular, seven best features were identified and further fed into k-Nearest Neighbor (kNN), naïve Bayes, Support Vector Machine (SVM), and Decision Tree classifiers for two-type and three-type classifications. Six statistical parameters were employed to measure the performance of these classifications. It has been found that different combinations of features and classifiers produce different results. Overall, the study is a first attempt to find the best combination feature set and classifier for 16 different 2-class and 3-class classification challenges of the Bonn and Senthil real-time clinical dataset.
Kavitha et al. (2022) studied Epileptic seizure. Machine learning classifiers (kNN, naïve Bayes, SVM, Decision Tree) with DWT features vs. Different combinations of features and classifiers was evaluated on Classification performance for 2-class and 3-class classification challenges. Different combinations of discrete wavelet transform features and machine learning classifiers produced varying results for 16 different 2-class and 3-class epileptic seizure classification challenges.