A Wireless Monitoring and Analysis System for Heart Sound Signal

• Main Authors: Jen-Chieh Huang, 黃仁杰
• Other Authors: Ta-Te Lin
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/01558211293882955095

碩士 === 國立臺灣大學 === 生物產業機電工程學研究所 === 95 === The purpose of this research is to develop a wireless monitoring system of heart sound signals based on an electronic stethoscope. The wireless module that uses the ZigBee communication protocol serves as the wireless platform that transmits the heart sound signals to a desktop computer for further signal processing and analyses, taking the advantage of its computation and memory storage capability. The components of this system include an analog signal processing module, a digital signal sampling module, a wireless module, and a real-time display software. The analog signal processing module comprises amplifying and filtering circuits while the digital signal sampling module and wireless module are the wireless module of MICAz and the MIB510 interface board. The electronic stethoscope was successfully integrated with the wireless module to achieve a hand-held and modular design. An algorithm as well as a software was developed using C++ Builder which provides the functionality of heart sound analysis for the integrated wireless monitoring system. Since the first and second heart sound are the two most important features for heart sound analyses in time domain, we focused on developing an algorithm that automatically detects the positions of first and second heart sounds in continuous heart sound signals. The algorithm was tested with many heart sound signals and the accuracy of detection was 92.5%. Several simple heart sound features were then derived from a heart sound signal based on the first and second heart sound detected. Two sets of experiments were carried out to acquire heart sounds from various subjects. These heart sound samples together with the samples from internet databases were analyzed and compared using the developed software. We found that abnormal heart sounds such as the pulmonic insufficiency and aortic insufficiency can be classified with the algorithm.