癫痫是一种常见的神经系统疾病。全世界目前大约有5000万癫痫患者，每年大约有240万人被确诊患癫痫。当前，由于对癫痫认识并不充分，癫痫尚不能被彻底治愈，其确诊时间较长，准确率也不是特别高。研究表明，癫痫发作会对自主神经系统产生重要影响，而心率变异性能在一定程度上反映自主神经系统的活性。研究癫痫患者的心率变异性特征，有助于加深对癫痫的认识。由于心率变异性的测量和计算非常快速和便捷，一旦掌握了癫痫患者的心率变异性特征，就可以将其作为确诊的重要指标，进而缩短确诊周期，提高诊断的准确性。此外，心率变异性还能用来评估癫痫的治疗效果，进而对治疗方案提出反馈和改进。论文以51名癫痫患者和50名健康对照组的原始心电数据为基础，围绕心电数据的采集、处理和分析过程展开工作，系统地分析和总结了癫痫患者和健康对照组心率变异性及其昼夜节律的差异。论文首先调研了国际上较为权威的心电数据库、心率变异性公认的计算方法和国内外学者对癫痫患者心率变异性的研究进展，找到了前人研究中存在的不足，明确了研究目标和研究方法。其次，根据实际需求确认了心电数据的采集方案，包括患者和对照组的甄选、采集时长的确定和心电导联的选择。接下来，针对几种常见噪声进行了心电数据预处理，完成了R波检测工作。在得到心率变异性数据之后，设计了有关数据库来存储这些数据，以便在分析过程中能够根据研究条件进行检索。最后，以分析所得数据为基础，在时域、频域和非线性域找到了癫痫患者和对照组存在显著统计学差异的心率变异性指标，并分析了其昼夜节律特征，为下一步的研究工作奠定了基础。

Epilepsy is a common disease of nervous system, affecting approximately 50 million people worldwide with an estimated 2.4 million people diagnosed with epilepsy each year. While there are no available cure for epilepsy, and the diagnosis of epilepsy is time consuming and may sometimes be incorrect.The majority of research indicates that epileptic seizure has a significant impact on the autonomic nervous system, which can be reflected in heart rate variability (HRV). The research of HRV in epileptic patients may allow a better understanding of the pathophysiological mechanisms in epilepsy. The convenience of assessing HRV may allow an efficient way to diagnose patients, once HRV has been characterized in this disease. In addition, HRV may be used to assess the treatment results, providing feedback and improvement for the treatment of epilepsy.This paper is based on the electrocardiogram (ECG) data of 51 epileptic patients and 50 healthy matched controls. It describes the collection, processing and analysis of the ECG data. The differences of heart rate variability and its circadian rhythm between epileptic patients and healthy matched controls have been discussed.In this first study, we evaluated the most authoritative ECG databases, the measurement of HRV and current research progress in the field of HRV in epileptic patients. We described the limitation in this research area. Therefore we define our research method and object in the research of HRV in epilepsy.In the second study, according to the demand of the research, we defined a suitable scheme for the collection of ECG data. This includes the selection of epileptic patients and control groups, the duration of ECG data and the leads for ECG sampling.In our third study, according to the feature of the ECG signal noise, the ECG data were pre-processed. The R wave non-real-time detection was fulfilled and the variables measuring HRV were calculated. A database was then designed to store the variables, for creating a powerful research tool.