Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals

Zambrana-Vinaroz, David; Vicente-Samper, Jose Maria; Manrique-Cordoba, Juliana; Sabater-Navarro, Jose Maria

doi:10.3390/s22239372

Open AccessArticle

Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals

by

David Zambrana-Vinaroz

,

Jose Maria Vicente-Samper

,

Juliana Manrique-Cordoba

and

Jose Maria Sabater-Navarro

^*

Neuroengineering Biomedical Research Group, Miguel Hernández University of Elche, 03202 Elche, Spain

^*

Author to whom correspondence should be addressed.

Sensors 2022, 22(23), 9372; https://doi.org/10.3390/s22239372

Submission received: 19 October 2022 / Revised: 26 November 2022 / Accepted: 27 November 2022 / Published: 1 December 2022

(This article belongs to the Special Issue AI for Biomedical Sensing and Imaging)

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Abstract

Epileptic seizures have a great impact on the quality of life of people who suffer from them and further limit their independence. For this reason, a device that would be able to monitor patients’ health status and warn them for a possible epileptic seizure would improve their quality of life. With this aim, this article proposes the first seizure predictive model based on Ear EEG, ECG and PPG signals obtained by means of a device that can be used in a static and outpatient setting. This device has been tested with epileptic people in a clinical environment. By processing these data and using supervised machine learning techniques, different predictive models capable of classifying the state of the epileptic person into normal, pre-seizure and seizure have been developed. Subsequently, a reduced model based on Boosted Trees has been validated, obtaining a prediction accuracy of 91.5% and a sensitivity of 85.4%. Thus, based on the accuracy of the predictive model obtained, it can potentially serve as a support tool to determine the status epilepticus and prevent a seizure, thereby improving the quality of life of these people.

Keywords: ear EEG; ECG; epilepsy; HRV; machine learning; PPG; PTT; outdoors seizure prediction; wearable

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MDPI and ACS Style

Zambrana-Vinaroz, D.; Vicente-Samper, J.M.; Manrique-Cordoba, J.; Sabater-Navarro, J.M. Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals. Sensors 2022, 22, 9372. https://doi.org/10.3390/s22239372

AMA Style

Zambrana-Vinaroz D, Vicente-Samper JM, Manrique-Cordoba J, Sabater-Navarro JM. Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals. Sensors. 2022; 22(23):9372. https://doi.org/10.3390/s22239372

Chicago/Turabian Style

Zambrana-Vinaroz, David, Jose Maria Vicente-Samper, Juliana Manrique-Cordoba, and Jose Maria Sabater-Navarro. 2022. "Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals" Sensors 22, no. 23: 9372. https://doi.org/10.3390/s22239372

APA Style

Zambrana-Vinaroz, D., Vicente-Samper, J. M., Manrique-Cordoba, J., & Sabater-Navarro, J. M. (2022). Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals. Sensors, 22(23), 9372. https://doi.org/10.3390/s22239372

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Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals

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