本文根据能够反映药物难治性癫痫患者自主神经功能状态的HRV分析指标以及表征心率复杂性的MSE分析指标的VNS术前基线值与VNS术后疗效相关的基本结论，进行了基于HRV分析的癫痫患者VNS术后疗效预测过程的算法研究与软件实现。首先，介绍了ECG数据预处理的方法，实现多种格式的24小时ECG数据的一次性读取。从检测速度和准确率两个角度，采用四种不同的算法：滤波-差分-阈值检测、快速算法、改进的滤波-差分-阈值检测、支持向量机（SVM）进行R波自动检测。为实现软件的自动化，对于4h清醒状态RR间期进行了自动检测的研究。其次，为了基于HRV分析评估自主神经功能并建立其VNS术前基线值与VNS术后疗效的关联，对包括时域分析、频域分析、非线性分析等在内的传统HRV分析方法进行了探讨，并对能够反映心率复杂性的MSE分析方法以及能够分别表征迷走神经和交感神经张力状态的PRSA分析方法进行了研究。利用上述多种HRV分析方法得到的指标，将能够对VNS术后是否有疗效进行显著区分的指标RMSSD、SD1、Area 6-20day作为疗效预测因子，构建了VNS疗效预测模型。最后，将上述算法研究成果集成，设计并开发了基于HRV分析的癫痫患者VNS疗效预测软件（HRV Analysis for VNS，简称HRVAFV）。论文研究成果能够帮助医生对VNS术前适应症患者进行快速准确的自动化筛选，从而间接提高VNS临床疗效，有利于VNS疗法推广。

According to the basic understanding that (i) indexes calculated from HRV analysis can reflect the function of autonomic nervous system of patients with drug-refractory epilepsy and (ii) baseline values of indexes derived from MSE analysis used to characterize the complexity of heart rate are related to the postoperative efficacy of VNS, we performed algorithm research and software implementation for the entire process of predicting the efficacy of postoperative VNS in patients with drug-refractory epilepsy.Firstly, the ECG data preprocessing method was introduced to implement one-time reading of 24h ECG data in multiple formats. From the perspective of detection speed and accuracy, four different algorithms were used that included filtering differential threshold detection, fast algorithm, improved filtering differential threshold detection, and support vector machine (SVM) for automatic detection of R-waves. In order to automate the software, the automatic detection of the 4h awake RR interval was studied.Secondly, in order to evaluate the autonomic nervous function based on HRV analysis and to establish the correlation between the VNS preoperative baseline value and the postoperative efficacy of VNS, we discuss the traditional HRV analysis methods including time domain, frequency domain and nonlinear analysis. In particular, the MSE analysis method that reflects the complexity of the heart rate and the PRSA analysis method that separately represents the vagal and sympathetic nervous function were introduced. Indicators derived from multiple HRV analysis methods, particularly RMSSD, SD1, and Area 6-20day, which can clearly distinguish the therapeutic effect after VNS implantation, were used as predictors of efficacy to construct a predictive model of VNS therapy.Finally, research results into the algorithm were programmed and implemented. A software called “HRV Analysis for VNS” (HRVAFV) was designed and developed which can predict the efficacy of postoperative VNS based on HRV analysis for patients with drug-refractory epilepsy. This software has contributed to i. the rapid and accurate automated screening of patients with drug-refractory epilepsy prior to VNS implantation, ii. the overall improvement of VNS clinical efficacy in patient populations, and iii. promotion of VNS therapy.