A Portable Intelligent Auscultation System or Heart Sound Signals
Date Issued
2008
Date
2008
Author(s)
Liu, Tzu-Cheng
Abstract
The objective of this research is to improve the previously developed wireless stethoscope and to develop a newer portable intelligent auscultation system for heart sound signals. The system which includes a wireless auscultation device and a heart sound signal processing software is designed and implemented to diagnose valvular heart disease automatically. The auscultation device is based on a MICAz wireless sensor network module, a heart sound sensing module, and a power management module. For the new auscultation system, real-time heart sound auscultation is added, the bandwidth of signal processing is improved, and the parameters of wireless transmission are also optimized. Heart sound signals can be sent to personal computer via ZigBee communication protocol. The diagnosis system witch is developed with Borland C++ Builder provides some functions, such as displaying real-time heart sound signal in time-frequency domain, and saving and loading different file formats. Besides, two algorithms for abnormal heart sound detection and heart sound classification are constructed in MATLAB by analyzing heart sound samples collected from internet databases. Normalized average Shannon energy with sliding window is applied to extract heart sound envelope, and then the continuous heart sound signal is segmented to individual heart cycles by heart cycle segmentation based on auto-threshold with accuracy of 93.37%. After above procedures, those envelopes are transferred to frequency domain and compared with a pattern constructed from frequency feature of normal heart sounds by pattern matching and correlation coefficient operation for detecting abnormal heart sounds. In this study, receiver operating characteristic curve (ROC curve) is used to analyze the identification ability of two methods. The correlation coefficient operation and pattern matching are similar in the identification capability, and accuracies of abnormal detection are 82.46% and 81.20%, respectively, based on suitable threshold recommended by the ROC curve. Furthermore, the multiple layer perceptron back-propagation (MLP-BP) neural network is used as a heart sound classification algorithm. The input of this neural network is heart sound envelope feature which combines statistic information of time-frequency domain based on short-time Fourier transform (STFT). The neural network can classify normal heart sound, diastolic murmur, and systolic murmur, with accuracies of 96.67%, 94.19%, and 95.56%, respectively.
Subjects
intelligent auscultation system
wireless sensor network
heart sound signal processing
feature extraction
Type
thesis
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