Bayesian approach to identify spike and sharp waves in EEG data of epilepsy patients. (May 2017)

• Record Type: Journal Article
• Authors: Puspita, Juni Wijayanti; Gunadharma, Suryani; Indratno, Sapto Wahyu; Soewono, Edy
• Is Part Of: Biomedical signal processing and control. Volume 35(2017)
• Page Start: 63
• Page End: 69
• Publication Date: 2017-05
• Subjects: Bayesian classification method -- EEG data -- Sharp waves -- Spike waves -- Walsh transformation method; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.02.016
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A method to classify Interictal Epileptiform Discharges (IEDs) using a Bayesian model that derived from the original EEG data and the Walsh Transformation results are proposed. The Bayesian models are constructed based on the dependence of the IEDs profiles. The classification model with dependent features assumption gives better results than the model with independent features assumption. The method achieve better classification results for attribute of original data and attribute of first order Walsh transformation than attribute of second order Walsh transformation. Abstract: Electroencephalography (EEG) is the most common test being used to diagnose epilepsy. Most abnormal EEG patterns in epilepsy are interictal epileptiform discharges (IEDs), which consist of spike and sharp waves. These two types of waves can be detected in detail by using the Walsh transformation. In this technique, training data consisting of the original data from EEGs and the results of the first- and second-order Walsh transformation are collected to construct IED profiles. In this paper we propose two Bayesian classification models based on the dependence of the IED profiles. Bayesian classification is applied to classify spike and sharp waves resulting from the Walsh transformation. In our case study, the classification model with dependent features assumption gave better results than the model with independent features assumption.