Wavelet processing of systolic murmurs to assist with clinical diagnosis of heart disease.

Despite advances in imaging technologies for the heart, screening of patients for cardiac pathology continues to include the use of traditional stethoscope auscultation. Detection of heart murmurs by the primary care physician often results in the ordering of additional expensive testing or referral to cardiology subspecialists, although many of the patients are eventually found to have no pathologic condition. In contrast, auscultation by an experienced cardiologist is highly sensitive and specific for detecting heart disease. Although attempts have been made to automate screening by auscultation, no device is currently available to fulfill this function. Multiple indicators of pathology are nonetheless available from heart sounds and can be elicited using certain signal processing techniques such as wavelet analysis. Results presented here show that a signal of pathology, the systolic murmur, can reliably be detected and classified as pathologic using a portable electrocardiogram and heart sound measurement unit combined with a wavelet-based algorithm. Wavelet decomposition holds promise for extending these results to detection and evaluation of other audible pathologic indicators.