Machine Learning Based Model For Seizure Detection Using Eeg Signals

This is the authors' abstract. We don't add key points for this paper.

Epilepsy is a disease of grave concern these days due to the negligence in its treatment in many parts of theworld. Its detection and diagnose requires high skill, large amount of time and money. Thus, due to lack oftreatment, epilepsy which can be diagnosed with simple epileptic drugs turn refractory. This can be avoided ifit is detected at an early stage. Also, the data received after a patient undergo EEG is quite complex. Visualizingthat data in an effective way and knowing important timestamps in a recorded EEG signal can help one savetime and increase accuracy of detection. An automated system utilizing conventional machine learning is thusproposed in this study that uses features extracted from EEG signals. We have used a seizure detection modeland visualized data and the result using various python libraries. Seizure detection is a model which is able toidentify the presence of abnormal activities in the brain. Seizure prediction is a model which is able to predictin advance if he/she is going to face seizures in coming time by just studying the EEG signals of present stateof that patient. Supervised Machine learning (random forest classifier) was employed to analyze recorded EEGsignals for epilepsy detection. Data in the datasets was visualized using matplotlib. Classifier was visualizedusing Graphviz and pydot. Random forest model predicted epilepsy with a good accuracy of 96.87 %,Sensitivity came out to be 98.4 % and Specificity was 90.7 %.