Abstract
Epilepsy or seizure is a prevalent neurological disorder present among all age of people. However, there is still a lack of precise automated detection methods for epilepsy. We present a computer-aided diagnosis system using nature-inspired algorithms for automatic detection of epilepsy from EEG data. Unlike the traditional approaches, we propose to employ nature-inspired algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for feature optimization before the detection process. Time–frequency domain features have been extracted using discrete wavelet transform from the time-series EEG data. Out of these features, discriminatory and optimized set of features are obtained using GA and PSO, which are used further to diagnose a person into an epileptic and non-epileptic class. As compared to the non-optimization-based approaches, the proposed method performs better in terms of improved detection accuracy.
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Singh, A., Sharma, S., Mukundan, V., Kumar, T., Pusarla, N. (2020). Nature-Inspired Algorithm-Based Feature Optimization for Epilepsy Detection. In: Agarwal, S., Verma, S., Agrawal, D. (eds) Machine Intelligence and Signal Processing. MISP 2019. Advances in Intelligent Systems and Computing, vol 1085. Springer, Singapore. https://doi.org/10.1007/978-981-15-1366-4_21
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DOI: https://doi.org/10.1007/978-981-15-1366-4_21
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