Federated Machine Learning for Epileptic Seizure Detection using EEG

Abstract

Early seizure detection is difficult with epilepsy. This use of Electroencephalography (EEG) data has proven transformational, however standard centralized machine learning algorithms have privacy and generalization issues. A decentralized approach to epileptic seizure detection using Federated Machine Learning (FML) is presented in this research. The concentration of critical EEG data in conventional models may compromise patient confidentiality. The proposed FML technique trains models using local datasets without sharing raw EEG recordings. Hence the data set used for the model is devoid of noise thus rendering preprocessing unnecessary. Training using decentralized data sources broadens the model’s seizure pattern repertoire, improving its adaptability to case heterogeneity. The Federated Machine Learning (FML) model shows that the suggested method for EEG-based epileptic seizure identification is promising for healthcare implementation and deployment. The proposed approach obtains sensitivity, specificity, and accuracy of 98.24%, 99.23%, 99% respectively. The proposed study is validated with the existing literature and the developed model outperforms the existing study.