Scalable Hypothesis Tests for Detection of Epileptic Seizures

Epilepsy is a disease that affects a large part of the population and the monitoring of the cerebral electrical activity using Electroencephalogram sensors provides a real time graphical representation of the brain function that can be used to detect the epileptic seizures. The detection of epileptic seizures is challenging because the brain activity is very complex, unique for each person and can not be completely captured even though the technology advanced very much in the last years and the sensors that capture this data are very complex in the present. In this research article the detection of epileptic seizures is approached using a machine learning methodology that is based on a combination of some of the latest technological advancements in the fields of big data and machine learning in which the features are extracted using a method based on scalable hypothesis tests and then the data is predicted using three machine learning classifiers namely, AdaBoost, Gradient Boosted Trees and Random Forest.

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