On Bio-Signal Processing Algorithms for Heart Rate Variability:Accuracy Comparison and Performance Analysis

• Main Authors: Shih-Han Lee, 李詩涵
• Other Authors: Huan Chen
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/65559667816551482771

碩士 === 國立中正大學 === 通訊工程研究所 === 99 === With the rapid development of society, people can live in a more convenient material life derived from depression, anxiety and many about emotional disorders of civilization. There is a close relationship between the autonomic nervous system and many of the disorders on the clinical observation. Autonomic nervous system can be divided into the sympathetic and the parasympathetic. Heart rate variability (HRV) is a phenomenon where the interval between heartbeats varies. The well-known SDNN (Standard deviation of Normal to Normal R Wave) of HRV in medical field can represent the strength of autonomic nervous system activity (sympathetic and parasympathetic) to a person. Therefore, the measurements of HRV can truly show variation of a person's health status.[25] We adopted MIT-BIH arrhythmia database to analyze integer-coefficients, median and N-point moving average digital filter, and then to verify So-and-Chan algorithms simulating with MATLAB. This paper compared Lagrange and Cubic Spline interpolation for the frequency distortion in the frequency domain of HRV analysis. And then we used in C # .NET as to verify the frequency domain and time domain of HRV analysis. Simulation results suggested that the digital signal enter the integer coefficients digital filter and the moving average digital filter. After removing noise, we used So-and-Chan as to detects QRS complex. In particular, Cubic Spline interpolation is the most suitable algorithm for the frequency domain of HRV analysis.