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.

Faculty of Informatics and Management University of Hradec Kralove Hradec Kralove Czech Republic

Faculty of Information Technology HUTECH University Ho Chi Minh City Vietnam; DaSCI Andalusian Institute of Data Science and Computational Intelligence University of Granada Granada Spain; Malaysia Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia; Regional Research Center Iwate Prefectural University Iwate Japan

Faculty of Science University of Hradec Kralove Rokitanskeho 62 Hradec Kralove 50003 Czech Republic

Malaysia Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia

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