Dong Hongsheng, Zhang Aihua, Qiu Tianshuang, Hao Xiaohong
College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2011 Apr;28(2):248-54.
The signal analysis of heart rate variability (HRV) has been very significant for heart disease of aided diagnosis, monitoring and evaluation. We proposed a new method of HRV signal analysis based on the Hilbert spectrum entropy dividing frequency range. According to Hilbert spectrum characteristics of the multi-resolution and the characteristic of HRV signal frequency spectrum, the Hilbert time-frequency spectrum entropy of HRV signal in different frequency range and the full frequency Hilbert time-frequency spectrum entropy with weighting factor were calculated. This approach was analyzed after the appropriate separation for various physiological factors based on the frequency range and it is more conducive to reflect the physiological and the pathological characteristics. Applying the new approach to the actual HRV signal of the MIT-BIH standard database, we obtained the results which showed that this method could effectively differentiate from the sample group for the young, the elder and the patients with atrial fibrillation, and for the sample group for the healthy persons and CHF patients, the performance in statistical analysis was superior to those of the general time-frequency entropy method. The approach could provide an effective analysis method for clinical HRV signal.
心率变异性（HRV）的信号分析对于心脏病的辅助诊断、监测和评估具有重要意义。我们提出了一种基于希尔伯特谱熵划分频率范围的HRV信号分析新方法。根据希尔伯特谱的多分辨率特性以及HRV信号频谱的特点，计算了不同频率范围内HRV信号的希尔伯特时频谱熵以及带加权因子的全频希尔伯特时频谱熵。该方法在基于频率范围对各种生理因素进行适当分离后进行分析，更有利于反映生理和病理特征。将该新方法应用于MIT - BIH标准数据库的实际HRV信号，我们得到的结果表明，该方法能够有效区分青年、老年和房颤患者的样本组，并且对于健康人和慢性心力衰竭（CHF）患者的样本组，其在统计分析中的性能优于一般时频熵方法。该方法可为临床HRV信号提供一种有效的分析方法。
