ECG-Based Prediction of Epileptic Seizures Using Machine Learning Methods

Seifi, Bahare; Barfi, Mahsa; Esmaeilpour, Mansour

doi:10.1109/cfis54774.2022.9756421

Cited by 4 publications

(1 citation statement)

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“…These changes indicate high sympathetic activity and occur especially 5 min before the onset of seizures. Therefore, ECG signal analysis can be a beneficial tool for predicting epileptic seizures ( Seifi et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Patient-specific approach using data fusion and adversarial training for epileptic seizure prediction

Yang

Qin

Wen

et al. 2023

Front. Comput. Neurosci.

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Epilepsy is the second common neurological disorder after headache, accurate and reliable prediction of seizures is of great clinical value. Most epileptic seizure prediction methods consider only the EEG signal or extract and classify the features of EEG and ECG signals separately, the improvement of prediction performance from multimodal data is not fully considered. In addition, epilepsy data are time-varying, with differences between each episode in a patient, making it difficult for traditional curve-fitting models to achieve high accuracy and reliability. In order to improve the accuracy and reliability of the prediction system, we propose a novel personalized approach based on data fusion and domain adversarial training to predict epileptic seizures using leave-one-out cross-validation, which achieves an average accuracy, sensitivity and specificity of 99.70, 99.76, and 99.61%, respectively, with an average error alarm rate (FAR) of 0.001. Finally, the advantage of this approach is demonstrated by comparison with recent relevant literature. This method will be incorporated into clinical practice to provide personalized reference information for epileptic seizure prediction.

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Section: Introductionmentioning

confidence: 99%