Novel Features Extraction From EEG Signals for Epilepsy Detection Using Machine Learning Model

Epilepsy is a neurological disorder that affects the brain, as well as the human body's nerves and spinal cord, adversely causing unusual and uncontrollable behavior. This letter presents the prediction of epilepsy based on machine learning modeling on the Children's Hospital Boston and Massachusetts Institute of Technology EEG Scalp database. The novel features are proposed, known as root mean square (RMS) of RMS, mean absolute value, and waveform length (RRWM) and generalized method of moments (GMM). This letter also presents finding the best machine learning model by comparing accuracies for a given dataset by varying window size and strides with various classification algorithms. The best model is observed using K-nearest neighbour (KNN) algorithm, Window size 300, and processed through hyper-parameter tuning deployed with the proposed features. Dataset preprocessing and conditioning are done using numpy, pandas, Keras, and SciKit-Learn libraries. The proposed feature RRWM performs the highest accuracy with 99.0256% and time complexity as 13 ms, whereas GMM being the fastest, has a single computation time of 8.99 ms for epilepsy detection.