Automated detection of interictal epileptiform discharges in scalp electroencephalogram of patients with epilepsy

• Main Author: Bagheri, Elham
• Other Authors: Justin Dauwels
• Format: Theses and Dissertations
• Language: English
• Published: 2019
• Subjects: DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
• Online Access: https://hdl.handle.net/10356/89750; http://hdl.handle.net/10220/48049
• Institution: Nanyang Technological University

Finding interictal epileptiform discharges (IED) or spikes in the electroencephalogram (EEG) is a part of diagnosing epilepsy. Automated annotation of interictal EEG is important for epilepsy diagnosis and management. Manual annotation of IEDs is tedious and there is substantial disagreement among experts. In the present thesis, various methods contributing to automated IED detection are proposed and investigated. We develop a feature-based method, in the form of a cascade of simple fast classifiers to reject most background waveforms and to speed up IED detection. We can eliminate 78.41% of background waveforms while retaining 96.93% of IEDs on our cross-validated dataset comprising 156 subjects. Rejecting background by our proposed method, speeds up the classification by a factor ranging from 4.69 to 1.76 for the considered classifiers. Next, we propose an ensemble algorithm comprising a cascade of SVM classifiers to enhance the precision of IED detection. At a fixed sensitivity, we are able to improve precision by 6.78%; and at a fixed false positive rate, the sensitivity improves by 2.83%. Additionally, we utilize a dataset annotated by several neurologists to analyze inter-rater agreement and signal characteristics. We determine features correlating with expert scoring and contributing to agreement or disagreement among experts, and fit models to predict expert scores. The features that best predict inter-rater agreement among experts regarding the presence of IED are certain wavelet features, using locally standardized EEG. Our models for predicting expert opinion based on EEGer’s scores perform best with a large group of EEGers (more than 10). Finally, we develop and implement several statistical measures for evaluating the performance of a IED detector and comparing it with human experts. These measures include the mean pairwise sensitivity and false positive rate performance of the P13 algorithm versus 19 academic EEGers, and a statistical Turing test using accelerated bootstrap analysis.