Behbahani Soroor
Department of Electrical Engineering, Garmsar Branch, Islamic Azad University, Garmsar, Iran.
Turk Kardiyol Dern Ars. 2018 Jul;46(5):414-421. doi: 10.5543/tkda.2018.64928.
Epilepsy is a brain disorder that many people struggle with all over the world. Despite extensive research, epilepsy is still an important challenge without a clear solution. There may be confusion about providing a specific approach due to the variety of epileptic seizures and the effectiveness in different environmental conditions. Some patients with epilepsy undergo treatment through medication or surgery. Epileptic patients suffer from unpredictable conditions that may occur at any moment. Given the origins of these seizures, researchers have focused on predicting epileptic seizures via electroencephalogram (EEG). The results indicate some success in this regard. This success led to a focus on optimizing these methods and the evaluation of epilepsy seizure prediction through other vital signals. Both sympathetic and parasympathetic inhibitory effects are undeniable during epileptic seizures. This conflict is visible in the change in heart rate. In recent years several investigations have focused on a behavioral study of heart rate changes before the seizures. The results have led to the development of algorithms for classifying and predicting epileptic seizures using the electrocardiogram (ECG) and the more distinct heart rate variability (HRV). This article presents an overview of seizure detection and prediction methods and discusses their potential to improve the quality of life of epileptic patients.
癫痫是一种脑部疾病，世界各地许多人都深受其困扰。尽管进行了广泛研究，但癫痫仍然是一个重大挑战，尚无明确的解决办法。由于癫痫发作的种类繁多以及在不同环境条件下的有效性，在提供具体治疗方法方面可能存在困惑。一些癫痫患者通过药物或手术进行治疗。癫痫患者会遭受随时可能发生的不可预测的状况。鉴于这些发作的起因，研究人员一直专注于通过脑电图（EEG）来预测癫痫发作。结果表明在这方面取得了一些成功。这一成功促使人们专注于优化这些方法，并通过其他重要信号评估癫痫发作预测。在癫痫发作期间，交感神经和副交感神经的抑制作用都是不可否认的。这种冲突在心率变化中可见。近年来，几项研究专注于癫痫发作前心率变化的行为研究。这些结果促使人们开发出利用心电图（ECG）和更明显的心率变异性（HRV）来分类和预测癫痫发作的算法。本文概述了癫痫发作检测和预测方法，并讨论了它们改善癫痫患者生活质量的潜力。
