Machine Learning and IoT Based EEG Signal Classification for Epileptic Seizures Detection

Abstract

Epilepsy is one of the most common chronic neurological disorders, affecting approximately 50 million people worldwide according to the World Health Organization. This disorder mainly presents four kinds of events: pre-ictal, ictal, post-ictal, and inter-ictal. Epilepsy can be diagnosed through an electroencephalogram (EEG). Inter-ictal activity is one of the widely accepted epilepsy symptoms on an EEG. However, the differentiation between normal and inter-ictal EEG segments is difficult because they can have similar patterns. Also, EEG from patients with epilepsy can contain normal events. In this work, we built classifiers to differentiate between normal, ictal, and inter-ictal EEG. Using Discrete Wavelet Transform multilevel decomposition of the signal is done. At each stage, the vital features are collected from the approximation and detailed coefficients belonging to a certain frequency range where the epilepsy is identifiable. Some of the features are directly extracted from the EEG signal. The machine learning algorithm is used to train and test the wide range of classifiers that suits the signal. This proposed method is implemented and tested with 98 percent accuracy. Here an emergency mail is sent to the doctor if any abnormality is found in the EEG signal using the Internet of Things (IoT).