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EEG-based seizure prediction with machine learning

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Abstract

Epilepsy is a well-recognized neurological illness which affects millions of people worldwide. This illness has long been considered important in biomedical research because of the threats it poses to the quality of human life. This paper presents a novel methodology that combines signal processing and machine learning techniques for patient-specific seizure prediction. The electroencephalogram (EEG) data per patient is first segmented, followed by wavelet packet decomposition to decompose the segmented data into the delta, theta, alpha and beta EEG bands. Four features are then extracted from each of these bands. The feature matrix thus obtained is fed into the support vector machine (SVM) classifier to classify the pre-ictal and inter-ictal seizure phases. Once the pre-ictal state has been detected by the SVM classifier, an alarm is generated using the Kalman filtering technique. False-positive rate, sensitivity and accuracy were measured as performance indicators, with achieved values of 0.138/h, 94.9%, 97.43%, respectively. The proposed method uses only 1 h of EEG data from one to two channels, thereby resulting in a computationally efficient technique.

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Authors and Affiliations

  1. Department of Electrical Engineering, University of Management and Technology, Lahore, Pakistan

    Muhammad Mateen Qureshi & Muhammad Kaleem

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  1. Muhammad Mateen Qureshi
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  2. Muhammad Kaleem
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Correspondence to Muhammad Mateen Qureshi.

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Qureshi, M.M., Kaleem, M. EEG-based seizure prediction with machine learning. SIViP 17, 1543–1554 (2023). https://doi.org/10.1007/s11760-022-02363-4

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