Noninvasive acoustical detection of coronary artery disease: a comparative study of signal processing methods

Previous studies have indicated that, during diastole, the sounds associated with turbulent blood flow through partially occluded coronary arteries should be detectable. To detect such sounds, recordings of diastolic heart sound segments were analyzed using four signal processing techniques: the fast Fourier transform (FFT) autoregressive (AR), autoregressive moving-average (ARMA), and minimum-norm (eigenvector) methods. To further enhance the diastolic heart sounds and reduce background noise, an adaptive filter was used as a preprocessor. The power ratios of the FFT method and the poles of the AR, ARMA, and eigenvector methods were used to diagnose patients as having diseased or normal arteries using a blind protocol without prior knowledge of the actual disease states of the patients to guard against human bias. Of 80 cases, results showed that normal and abnormal records were correctly distinguished in 56 using the fast Fourier transform (FFT), in 63 using the AR, in 62 using the ARMA method, and in 67 using the eigenvector method. These results confirm that high-frequency acoustic energy between 300 and 800 Hz is associated with coronary stenosis.