Real-time epilepsy seizure detection based on EEG using tunable-Q wavelet transform and convolutional neural network. (April 2023)

• Record Type: Journal Article
• Authors: Shen, Mingkan; Wen, Peng; Song, Bo; Li, Yan
• Is Part Of: Biomedical signal processing and control. Volume 82(2023)
• Publication Date: 2023-04
• Subjects: CNN -- EEG -- Real-time -- Seizure detection -- Tunable-Q wavelet transform; 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.2022.104566
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Proposed tunable-Q wavelet method to extract approximate and details of signal and remove redundant information. Improved the robustness of EEG based epilepsy detection using the deep learning method with CNN model. Proposed method can achieve 97.57% accuracy, 98.90% sensitivity, 2.10% false positive rate and 10.46-second delay in the real-time seizure detection in testing data. Combined signal processing method and image classification in this experiment. Implemented an automatic real-time seizure detection implementation. Abstract: Epilepsy is a chronic disease caused by sudden abnormal discharge of brain neurons, leading to transient brain dysfunctions. This paper proposed an EEG based real-time approach to detect epilepsy seizures using tunable-Q wavelet transform and convolutional neural network (CNN). Statistical moments and spectral band power were used to reveal the time domain and frequency domain features in EEG, and then were converted into imaged-like data fed into CNN. The proposed approach was evaluated using the database CHB-MIT. The proposed algorithm achieved 97.57% in accuracy, 98.90% in sensitivity, 2.13% in false positive rate and 10.46-second delay. In addition, the proposed method is suitable in real-time implementation. The outcomes indicate that the proposed method can applied to real-time seizure detection in clinical applications.