Electroencephalogram (EEG) is one of the most
commonly used tools for epilepsy detection. In this paper we have
presented two methods for the diagnosis of epilepsy using machine
learning techniques.EEG waveforms have five different kinds of
frequency bands. Out of which only two namely theta and gamma
bands carry epileptic seizure information. Our model determines
the statistical features like mean, variance, maximum, minimum,
kurtosis, and skewness from the raw data set. This reduces the
mathematical complexities and time consumption of the feature
extraction method. It then uses a Logistic regression model and
decision tree model to classify whether a person is epileptic or not.
After the implementation of the machine learning models,
parameters like accuracy, sensitivity, and recall have been found.
The results for the same are analyzed in detail in this paper.
Epileptic seizures cause severe damage to the brain which affects
the health of a person. Our key objective from this paper is to help
in the early prediction and detection of epilepsy so that preventive
interventions can be provided and precautionary measures are
taken to prevent the patient from suffering any severe damage
A Comparative Evaluation of Machine Learning Deployment Approaches in Real Term Environments using the Example of the Detection of Epileptic Seizures
