Abnormal EEG detection using time-frequency images and convolutional neural network

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Type
Book chapter
Citation
Rishabh Bajpai, Yuvaraj Rajamanickam, Prince, A. A., & Murugappan, M. (2022). Abnormal EEG detection using time-frequency images and convolutional neural network. In M. Murugappan & Yuvaraj Rajamanickam (Eds.), Biomedical signals based computer-aided diagnosis for neurological disorders (pp. 1–22). Springer. https://doi.org/10.1007/978-3-030-97845-7_1
Author
Yuvaraj Rajamanickam 
•
Rishabh Bajpai
•
Prince, A. Amalin
•
Murugappan, M.
Abstract
In the process of diagnosing neurological disorders, neurologists often study the brain activity of the patient recorded in the form of an electroencephalogram (EEG). Identifying an abnormal EEG serves as a preliminary indicator before specialized testing to determine the neurological disorder. Traditional identification methods involve manual perusal of the EEG signals. This method is relatively slow and tedious, requires trained neurologists, and delays the treatment plan. Therefore, the development of an automated abnormal EEG detection system is essential. In this study, we propose a method based on short-time Fourier transform (STFT), which is a time-frequency (TF) representation, and deep convolutional neural network (CNN) to detect abnormal EEGs. First, the filtered time-series EEG signals are converted into TF images by applying STFT. Then, the images are fed to three popular configurable CNN structures, namely, DenseNet, SeizureNet, and Inception-ResNet-V2, to extract deep learned features. Finally, an extreme learning machine (ELM)-based classifier detects the input TF images. The proposed STFT-based CNN method is evaluated using the Temple University Hospital (TUH) abnormal EEG corpus, which is available under the public domain. The experiment showed that the combination of the SeizureNet-ELM model achieved an average (fivefold cross-validation) accuracy, specificity, sensitivity, and F1-score of 85.87%, 88.43%, 83.23%, and 0.858, respectively. The results demonstrate that the proposed framework may aid clinicians in abnormal EEG detection for the early treatment plan.
Keywords
Date Issued
2023
ISBN
9783030978440 (print)
9783030978457 (online)
Publisher
Springer
DOI
10.1007/978-3-030-97845-7_1