Preoperative Heart Rate Variability During Sleep Predicts Vagus Nerve Stimulation Outcome Better in Patients With Drug-Resistant Epilepsy - Fang, Xi; Liu, Hong-Yun; Wang, Zhi-Yan; Yang, Zhao; Cheng, Tung-Yang; Hu, Chun-Hua; Hao, Hong-Wei; Meng, Fan-Gang; Guan, Yu-Guang; Ma, Yan-Shan; Liang, Shu-Li; Lin, Jiu-Luan; et al. (Frontiers in Neurology, 2021)
Objective: Vagus nerve stimulation (VNS) is an adjunctive and well-established treatment for patients with drug-resistant epilepsy (DRE). However, it is still difficult to identify patients who may benefit from VNS surgery. Our study aims to propose a VNS outcome prediction model based on machine learning with multi-dimensional preoperative heart-rate variability (HRV) indices. Methods: The preoperative electrocardiography (ECG) of 59 patients with DRE were analyzed. Responders were defined as having at least 50% average monthly seizure frequency at one-year follow-up. Time domain, frequency domain and nonlinear indices of HRV were compared between 30 responders and 29 non-responders in awake and sleep states respectively. For feature selection, univariate filter and recursive feature elimination (RFE) algorithms were performed to assess the importance of different HRV indices to VNS outcome prediction and improve the classification performance. Random Forest (RF) was used to train the classifier, and leave-one-out (LOO) cross-validation was performed to evaluate the prediction model. Results: Among 52 HRV indices, 35 shown significant differences between responders and non-responders in sleep state while 16 of them shown the same differences in awake state. S and LF ranked first in the importance ranking results by univariate filter and RFE methods respectively. With HRV indices in sleep state, our model achieved 74.6% accuracy, 80% precision, 70.6% recall and 75% f1 for VNS outcome prediction, which better than the optimal performance in awake state (65.3% accuracy, 66.4% precision, 70.5% recall and 68.4% f1). Significance: With the ECG during sleep state and machine learning techniques, the statistical model based on preoperative HRV could achieve a better performance of VNS outcome prediction, and therefore help patients who are not suitable for VNS to avoid the high cost of surgery and possible risks of long-term stimulation.