Unsupervised EEG analysis for automated epileptic seizure detection

Javad Birjandtalab; Maziyar Baran Pouyan; Mehrdad Nourani

doi:10.1117/12.2243622

11 July 2016 Unsupervised EEG analysis for automated epileptic seizure detection

Javad Birjandtalab, Maziyar Baran Pouyan, Mehrdad Nourani

Proceedings Volume 10011, First International Workshop on Pattern Recognition; 100110M (2016) https://doi.org/10.1117/12.2243622
Event: First International Workshop on Pattern Recognition, 2016, Tokyo, Japan

Abstract

Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic pattern recognition and data mining techniques to detect the onset of seizures and inform care-givers to help the patients. EEG signals are the preferred biosignals for diagnosis of epileptic patients. Most of the existing pattern recognition techniques used in EEG analysis leverage the notion of supervised machine learning algorithms. Since seizure data are heavily under-represented, such techniques are not always practical particularly when the labeled data is not sufficiently available or when disease progression is rapid and the corresponding EEG footprint pattern will not be robust. Furthermore, EEG pattern change is highly individual dependent and requires experienced specialists to annotate the seizure and non-seizure events. In this work, we present an unsupervised technique to discriminate seizures and non-seizures events. We employ power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non-seizure events. The experimental results tried so far indicate achieving more than 90% accuracy in clustering seizure and non-seizure events without having any prior knowledge on patient's history.

Citation Download Citation

Javad Birjandtalab, Maziyar Baran Pouyan, and Mehrdad Nourani "Unsupervised EEG analysis for automated epileptic seizure detection", Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100110M (11 July 2016); https://doi.org/10.1117/12.2243622

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available