Automated EEG pathology detection based on different convolutional neural network models: Deep learning approach

脑电图 卷积神经网络 计算机科学 人工智能 支持向量机 模式识别(心理学) 分类器(UML) 深度学习 机器学习 语音识别 医学 精神科
作者
Rishabh Bajpai,Rajamanickam Yuvaraj,A. Amalin Prince
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:133: 104434-104434 被引量:27
标识
DOI:10.1016/j.compbiomed.2021.104434
摘要

The brain electrical activity, recorded and materialized as electroencephalogram (EEG) signals, is known to be very useful in the diagnosis of brain-related pathology. However, manual examination of these EEG signals has various limitations, including time-consuming inspections, the need for highly trained neurologists, and the subjectiveness of the evaluation. Thus, an automated EEG pathology detection system would be helpful to assist neurologists to enhance the treatment procedure by making a quicker diagnosis and reducing error due to the human element. This work proposes the application of a time-frequency spectrum to convert the EEG signals onto the image domain. The spectrum images are then applied to the Convolutional Neural Network (CNN) to learn robust features that can aid the automatic detection of pathology and normal EEG signals. Three popular CNN in the form of the DenseNet, Inception-ResNet v2, and SeizureNet were employed. The extracted deep-learned features from the spectrum images are then passed onto the support vector machine (SVM) classifier. The effectiveness of the proposed approach was assessed using the publicly available Temple University Hospital (TUH) abnormal EEG corpus dataset, which is demographically balanced. The proposed SeizureNet-SVM-based system achieved state-of-the-art performance: accuracy, sensitivity, and specificity of 96.65%, 90.48%, and 100%, respectively. The results show that the proposed framework may serve as a diagnostic tool to assist clinicians in the detection of EEG pathology for early treatment.
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