CC BY-NC-ND 4.0 · International Journal of Epilepsy 2018; 05(02): 092-098
DOI: 10.1055/s-0039-1693072
Original Article
Indian Epilepsy Society

Detection of Startle-Type Epileptic Seizures using Machine Learning Technique

Pushpa Balakrishnan
1  Department of Electronics and Instrumentation Engineering, BSA Crescent Institute of Science and Technology, Chennai, Tamil Nadu, India
,
S. Hemalatha
1  Department of Electronics and Instrumentation Engineering, BSA Crescent Institute of Science and Technology, Chennai, Tamil Nadu, India
,
Dinesh Nayak Shroff Keshav
2  Department of Neurology, Gleneagles Global Hospital, Chennai, Tamil Nadu, India
› Author Affiliations
Further Information

Publication History

Received: 26 February 2019

Accepted after revision: 21 May 2019

Publication Date:
31 July 2019 (online)

  

Abstract

Background Epilepsy is a common neurological disorder characterized by seizures and can lead to life-threatening consequences. The electroencephalogram (EEG) is a diagnostic test used to analyze brain activity in various neurological conditions including epilepsy and interpreted by the clinician for appropriate diagnosis. However, the process of EEG analysis for diagnosis can be automated using machine learning algorithms (MLAs) to aid the clinician. The objective of the study was to test different algorithms that could be used for the detection of seizures.

Materials and Methods Video EEG (vEEG) was collected from subjects diagnosed to have episodes of seizures. The epilepsy dataset thus obtained was subjected to empirical mode decomposition (EMD) and the signal was decomposed into intrinsic mode functions (IMFs). The first five levels of decomposition were considered for analysis as per the established protocol. Statistical features such as interquartile range (IQR), entropy, and mean absolute deviation (MAD) were extracted from these IMFs.

Results In this study, different MLAs such as nearest neighbor (NN), naïve Bayes (NB), and support vector machines (SVMs) were used to distinguish between normal (interictal) and abnormal (ictal) states. The demonstrated accuracy rates were 97.32% for NN, 99.02% for NB, and 93.75% for SVM.

Conclusion Based on this accuracy and sensitivity, it may be posited that the NB classifier provides significantly better results for the detection of abnormal signals indicating that MLA can detect the seizure with better accuracy.