Analyzing Brain Signals to Predict Seizure Events using Machine Learning Techniques
AUTHORS
Jinan Fiaidhi*,Department of Computer Science, Lakehead University, Canada
Tejas Wadiwala,Department of Computer Science, Lakehead University, Canada
Vikas Trikha,Department of Computer Science, Lakehead University, Canada
ABSTRACT
This paper attempts to perform a comparative analysis on brain signals datasets to predict seizure events using various machine learning classifiers such as random forest, gradient boosting, support vector machine and extra trees classifier. The experimentation on these classifiers has been performed using the Rochester Institute of Technology EEG Dataset. The comparative analysis is measured based on the classifiers performance parameters such as accuracy, area under the ROC curve (AUC), specificity, recall, and precision. EEG signals are usually captivated to diagnose the problems related to the electrical activities of the brain as it tracks and records brain wave patterns to produce a definitive brain seizure activities. While exercising machine learning practices, various data preprocessing techniques were implemented to attain cleansed and organized data to predict better results and higher accuracy. Section II gives a comprehensive survey of existing work performed so far, while section III sheds light on the dataset used for this research.
KEYWORDS
Electroencephalogram Signals, Brain activities, Machine learning, Support Vector Machine, Binary classification, Extra trees classifier
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