Cloud Based Prediction For Epiliptic Seizure Using Machine Learning

Abstract

We can bring in ease and comfort in the life of a patient suffering from epileptic seizure by developing a potential and efficient Brain-Computer Interface (BCI), but this is a challenging task due to the fluctuating nature of ElectroEncephaloGram (EEG) signals that vary with a great deviation among different patients, due to which identification of manually extracted features for prediction becomes impractical, further the use of implanted electrodes for brain signal recordings generate huge amount of data that brings in the need for the use of big data framework  and cloud based approach for efficient storage and real time processing. Due to this a cloud oriented Brain-Computer Interface (BCI) system for analysis of EEG data is proposed in this paper which demonstrates a seizure prediction system on real-time data analysis of EEG. The proposed system also uses a machine learning methods for seizure prediction with the capability of pervasive data collection and analysis that could lead to high sensitivity and specificity in prediction which has been a challenging task for years. In order to evaluate the performance of the proposed system it is compared with existing standard epilepsy dataset.