Automatic and manual prediction of epileptic seizures based on ECG

This study presents a new attempt to quantify and predict changes in the ECG signal in the pre-ictal period. In the proposed approach, threshold techniques were applied to the standard deviation of two heart rate variability features (The number of heartbeats per two minutes and approximate entropy)...

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Bibliographic Details
Published in:Signal, image and video processing image and video processing, 2024-07, Vol.18 (5), p.4175-4190
Main Authors: Ben Mbarek, Manef, Assali, Ines, Hamdi, Salah, Ben Abdallah, Asma, David, Olivier, Aissi, Mouna, Carrere, Marcel, Bedoui, Mohamed Hedi
Format: Article
Language:English
Subjects:
Accuracy
Computer Imaging
Computer Science
Convulsions & seizures
Electroencephalography
Epilepsy
Heart rate
Image Processing and Computer Vision
Multimedia Information Systems
Original Paper
Pattern Recognition and Graphics
Seizures
Sensitivity
Signal,Image and Speech Processing
Vision
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Summary:This study presents a new attempt to quantify and predict changes in the ECG signal in the pre-ictal period. In the proposed approach, threshold techniques were applied to the standard deviation of two heart rate variability features (The number of heartbeats per two minutes and approximate entropy) computed to ensure prediction and quantification of the pre-ictal state. We analyzed clinical data taken from two epileptic public databases, Siena scalp EEG and post-ictal heart rate oscillations in partial epilepsy and a local database. By testing the proposed approach on the Siena scalp EEG database, we achieved a sensitivity of 100%, specificity of 95%, and an accuracy of 96.4% whereas using acquisitions from the post-ictal database, we achieved a sensitivity of 100%, specificity of 91% and an accuracy of 94% and using the local database we achieved a sensitivity of 100%, a specificity of 97% and an accuracy of 97.5%. Furthermore, the proposed approach predicted 58.7%, 57.2, and 40% of the seizures before the onset by more than 10 min for the data taken from post-ictal, local and Siena database, respectively. Using the automatic threshold technique, we were able to achieve a sensitivity, specificity, and accuracy of 85%, 81%, 82% using our local database, respectively, whereas using acquisitions take from the Siena scalp EEG database, we achieved a sensitivity of 75%, specificity of 85% and an accuracy of 82%. Besides, using the post-ictal database, we achieved a sensitivity of 90%, a specificity of 83% and an accuracy of 85%.
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-024-03063-x