Abstract
Epileptic seizures constitute an important group of neurological disorders in brain that affect many people globally each year. Complexity of EEG signals due to their high-dimensional nature, as well as artifacts in data due to equipment flaws, pose significant challenges to physicians in diagnosing epileptic seizures directly and manually from EEG signals. In this paper, a method is proposed to combine signal processing and machine learning for diagnosing epileptic seizures and tested on the Bonn University database. We used Tunable Q-Factor wavelet transform (TQWT) method to transform signals. Subsequently, various statistical properties, frequency, chaotic and fractional were extracted from the TQWT sub-bands. Subset selection techniques were used in order to reduce the features of the methods used and their results were compared. Finally, a SVM method with different kernels were tested, where the empirical results show the high efficiency of the proposed method in the diagnosis of epileptic seizures.
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Tokhmpash, A., Hadipour, S., Shafai, B. (2021). Epileptic Seizure Detection Using Tunable Q-Factor Wavelet Transform and Machine Learning. In: Ayaz, H., Asgher, U., Paletta, L. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2021. Lecture Notes in Networks and Systems, vol 259. Springer, Cham. https://doi.org/10.1007/978-3-030-80285-1_10
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DOI: https://doi.org/10.1007/978-3-030-80285-1_10
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