Automatic seizure detection by convolutional neural networks with computational complexity analysis. (February 2023)

• Record Type: Journal Article
• Authors: Cimr, Dalibor; Fujita, Hamido; Tomaskova, Hana; Cimler, Richard; Selamat, Ali
• Is Part Of: Computer methods and programs in biomedicine. Volume 229(2023)
• Publication Date: 2023-02
• Subjects: CNN -- CAD -- EEG -- Seizures; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107277
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Early diagnosis, including the combination of high accuracy and low computational complexity, is important in real applications of computer-aided diagnosis systems. We present an approach to seizure detection from electroencephalogram (EEG) signals with complexity analysis and comparison. The methodology was tested on the short-term Bonn EEG dataset and the long-term CHB-MIT EEG dataset. The results lead to the conviction that the designed solution represents a suitable tool. The proposed solution should be implemented in all clinical or home environments for decision support. Abstract: Background and Objectives: Nowadays, an automated computer-aided diagnosis (CAD) is an approach that plays an important role in the detection of health issues. The main advantages should be in early diagnosis, including high accuracy and low computational complexity without loss of the model performance. One of these systems type is concerned with Electroencephalogram (EEG) signals and seizure detection. We designed a CAD system approach for seizure detection that optimizes the complexity of the required solution while also being reusable on different problems. Methods: The methodology is built-in deep data analysis for normalization. In comparison to previous research, the system does not necessitate a feature extraction process that optimizes and reduces system complexity. The data classification is provided by a designed 8-layer deep convolutional neural network. Results: Depending on used data, we have achieved the accuracy, specificity, and sensitivity of 98%, 98%, and 98.5% on the short-term Bonn EEG dataset, and 96.99%, 96.89%, and 97.06% on the long-term CHB-MIT EEG dataset. Conclusions: Through the approach to detection, the system offers an optimized solution for seizure diagnosis health problems. The proposed solution should be implemented in all clinical or home environments for decision support.