Neuroengineering Biomedical Research Group, Miguel Hernández University of Elche, 03202 Elche, Spain.
Sensors (Basel). 2022 Dec 1;22(23):9372. doi: 10.3390/s22239372.
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.
癫痫发作极大地影响了患者的生活质量，并进一步限制了他们的独立性。因此，一种能够监测患者健康状况并在可能发生癫痫发作时发出警告的设备将提高他们的生活质量。为此，本文提出了第一个基于耳 EEG、ECG 和 PPG 信号的癫痫预测模型，这些信号是通过一种可在静态和门诊环境中使用的设备获得的。该设备已在临床环境中对癫痫患者进行了测试。通过处理这些数据并使用监督机器学习技术，开发了不同的预测模型，能够将癫痫患者的状态分类为正常、发作前和发作。随后，基于提升树的简化模型进行了验证，得到了 91.5%的预测准确率和 85.4%的灵敏度。因此，基于所获得的预测模型的准确性，它可能可以作为一种支持工具来确定癫痫持续状态并预防癫痫发作，从而提高这些人的生活质量。
