A new epileptic seizure prediction model based on maximal overlap discrete wavelet packet transform, homogeneity index, and machine learning using ECG signals

Perez-Sanchez, Andrea V.; Amezquita-Sanchez, Juan P.; Valtierra-Rodriguez, Martin; Adeli, Hojjat

doi:10.1016/j.bspc.2023.105659

Biomedical Signal Processing and Control

2024

DOI: 10.1016/j.bspc.2023.105659

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A new epileptic seizure prediction model based on maximal overlap discrete wavelet packet transform, homogeneity index, and machine learning using ECG signals

Andrea V. Perez-Sanchez,

Juan P. Amezquita-Sanchez,

Martin Valtierra-Rodriguez

et al.

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2024

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Cited by 10 publications

References 69 publications

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Deep learning based automatic seizure prediction with EEG time-frequency representation

Dong,

He,

et al. 2024

Biomedical Signal Processing and Control

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Deep learning based automatic seizure prediction with EEG time-frequency representation

Dong,

He,

et al. 2024

Biomedical Signal Processing and Control

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AI in ECG: Validating an ambulatory semiology labeller and predictor

Muralidharan,

Sankaran,

Bendapudi

et al. 2024

Epilepsy Research

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An approach to detect and predict epileptic seizures with high accuracy using convolutional neural networks and single-lead-ECG signal

Ghaempour,

Hassanli,

Abiri

2024

Biomed. Phys. Eng. Express

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One of the epileptic patients' challenges is to detect the time of seizures and the possibility of predicting. This research aims to provide an algorithm based on deep learning to detect and predict the time of seizure from one to two minutes before its occurrence. The proposed Convolutional Neural Network (CNN) can detect and predict the occurrence of focal epilepsy seizures through single-lead-ECG signal processing instead of using EEG signals. The structure of the proposed CNN for seizure detection and prediction is the same. Considering the requirements of a wearable system, after a few light pre-processing steps, the ECG signal can be used as input to the neural network without any manual feature extraction step. The desired neural network learns purposeful features according to the labelled ECG signals and then performs the classification of these signals. Training of 39-layer CNN for seizure detection and prediction has been done separately. The proposed method can detect seizures with an accuracy of 98.84% and predict them with an accuracy of 94.29%. With this approach, the ECG signal can be a promising indicator for the construction of portable systems for monitoring the status of epileptic patients.

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A new epileptic seizure prediction model based on maximal overlap discrete wavelet packet transform, homogeneity index, and machine learning using ECG signals

Cited by 10 publications

References 69 publications

Deep learning based automatic seizure prediction with EEG time-frequency representation

Deep learning based automatic seizure prediction with EEG time-frequency representation

AI in ECG: Validating an ambulatory semiology labeller and predictor

An approach to detect and predict epileptic seizures with high accuracy using convolutional neural networks and single-lead-ECG signal

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