NONINVASIVE ACOUSTICAL DETECTION OF CORONARY-ARTERY DISEASE - A COMPARATIVE-STUDY OF SIGNAL-PROCESSING METHODS

Previous studies have indicated heart sounds may contain information u seful in the detection of occluded coronary arteries [1]-[12]. During diastole, coronary blood flow is maximum, and the sounds associated wi th turbulent blood flow through partially occluded coronary arteries s hould be detectable [1]-[12]. In order to detect such sounds, recordin gs of diastolic heart sound segments were analyzed by using four signa l processing techniques; the Fast Fourier Transform (FFT), the Autoreg ressive (AR), the Autoregressive Moving Average (ARMA), and the Minimu m-Norm (Eigenvector) methods. To further enhance the diastolic heart s ounds and reduce background noise, an Adaptive filter was used as a pr eprocessor [12]. The power ratios of the FFT method and the poles of t he AR, ARMA, and Eigenvector methods were used to diagnose patients as diseased or normal arteries using a blind protocol without prior know ledge of the actual disease states of the patients to guard against hu man bias. Results showed that normal and abnormal records were correct ly distinguished in 56 of 80 cases using the Fast Fourier Transform (F FT), in 63 of 80 cases using the AR, in 62 of 80 cases using the ARMA method, and in 67 of 80 cases using the Eigenvector method. Among all four methods, the Eigenvector methods showed the best diagnostic perfo rmance when compared with the FFT, AR, and ARMA methods. These results confirm that high frequency acoustic energy between 300 and 800 Hz is associated with coronary stenosis [2]-[12].