Computer-assisted detection of systolic murmurs associated with hypertrophic cardiomyopathy: a pilot study.

ABSTRACT

A pilot study was conducted to ascertain the level of agreement between auscultatory findings derived from heart sound recordings by a cardiologist and the results of a computer-based heart sound analysis algorithm. Heart sound recordings were obtained from volunteer subjects previously diagnosed with hypertrophic cardiomyopathy. Twenty-second recordings were obtained at each of 4 standard auscultatory locations on the precordium in 2 postures standing and reclining. Detailed auscultatory findings were derived by a cardiologist, who listened to the heart sound recordings and was blinded to the study design. The recordings were analyzed by an algorithm that detects heart sounds and murmurs, and derives associated timing and energy parameters. The algorithm results were compared with the auscultatory findings provided by the cardiologist and correlated with the medical histories provided by the volunteer subjects. A high degree of concordance between the medical histories, auscultatory findings, and computer analyses was obtained. The 1st and 2nd heart sounds were detected with high sensitivity (90.7%) and positive predictivity (93.0%). Systolic murmurs were detected with a sensitivity that increased from 50% for grade 1 to 100% for grades 2-3 and 3. The signal energy in the mid-frequency range correlated well with murmur grade judgments, and also agreed well with the cardiologist's judgments of the relative loudness of murmurs in standing versus reclining postures. The computer analysis algorithm thus instantiates the objective detection and identification of apical systolic murmurs that are louder in standing than in reclining postures; such murmurs are a cardinal sign of hypertrophic obstructive cardiomyopathy.