Abstract
Seizure detection from EEG signal plays important role in diagnosing and treating the Epilepsy disease. Development of Low complexity detection algorithms is needed in order to design efficient automatic epilepsy detection devices. In this paper, an automatic seizure detection algorithm proposed using Discrete Wavelet Transform and Cluster-based Nearest Neighborhood machine learning algorithm. The Electroencephalogram signals decomposed by Daubechies Wavelet transform. Temporal features extracted from decomposed Wavelet sub-bands. A new distance-based feature selection method introduced for an optimal feature selection. The proposed Cluster-based KNN algorithm reduces the number of computations required for conventional KNN method. The performance of proposed algorithm is validated by publically available benchmark EEG database. This proposed Classification method obtained 100% accuracy between seizure and normal EEG signals; 98% of accuracy between Inter-ictal and seizure signals, 91% of accuracy between Normal and Inter-ictal signals. This proposed cluster nearest neighborhood classifier requires less number of training samples and less number of calculation steps to detect seizure events. The analysis on classification performance between the various frequency bands confirms that, the EEG signal frequency band of 2.6–5.5 Hz reveals better classification results in adults. Due to less complexity of algorithm, the proposed algorithm is well suited for hardware implementation of automatic seizure detection systems.
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For Wavelet decomposition MATLAB commands are used. For Cluster –KNN algorithm, Custom Code is used.
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Acknowledgements
Authors would like to thank Department of Epileptology, University of Bonn (Germany), CHB-MIT data base, Bern-Barcelona data base for providing EEG datasets for the proposed study.
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This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.
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Syed Rafiammal S designed the study and performed the experiments. Najumnissa Jamal D and Kaja Mohideen S, Syed Rafiammal S contributed to the implementation of the research, to the analysis of the results and to the writing of the manuscript.
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Syed Rafiammal, S., Najumnissa Jamal, D. & Kaja Mohideen, S. Detection of Epilepsy Seizure in Adults Using Discrete Wavelet Transform and Cluster Nearest Neighborhood Classifier. Iran J Sci Technol Trans Electr Eng 45, 1103–1115 (2021). https://doi.org/10.1007/s40998-021-00437-6
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DOI: https://doi.org/10.1007/s40998-021-00437-6