Seizures are caused by irregular electrical pulses in the brain. Epileptic seizure detection on EEG signals is a long process, which is done manually by epileptologists. The aim of the study is automatically detecting the seizures of the brain, given the electroencephalogram signals by feature extraction and processing through different machine learning algorithms. Machines can be trained to do this type of observation and predict the output with high accuracy. In this chapter, the classification study of individual and ensemble classifier is performed for epileptic seizure detection. The proposed method consists of two phases: extraction of data from EEG signals and development of an individual and ensemble models. Bagging ensemble is developed to achieve better results. The development of the ensemble using various classification algorithms contributes towards increasing the diversity of the ensemble. An extensive comparative study with existing benchmark algorithm is performed for epileptic seizure detection.
EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms
