Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4973584 | Biomedical Signal Processing and Control | 2017 | 7 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Juni Wijayanti Puspita, Suryani Gunadharma, Sapto Wahyu Indratno, Edy Soewono,