Automatic cardiac phase detection of mitral and aortic valves stenosis and regurgitation via localization of active valves. (July 2017) - Saeidi, Azra; Almasganj, Farshad; Shojaeifard, Maryam
Highlights: A proper approach for automatic cardiac sound phase detection has been developed. Phase detection of common heart sound murmurs; aortic and mitral stenosis and regurgitation. The spatial information of active heart valves were achieved by subspace based method. A microphone array has been used to record normal heart sound from healthy subjects and abnormal heart sounds. Abstract: Heart auscultation is a primary way of heart diagnosis and monitoring. A critical step of heart sound analysis is dividing the sound into its basic phases, known as S1, systole, S2 and diastole. Heart sound phase detection becomes a challenging issue when more than one heart valve problems are simultaneously existed. In this paper, a powerful approach is proposed that properly determine phases of heart sounds with Mitral and Aortic stenosis and Regurgitation murmurs. In this approach, heart valves spatial-temporal information investigated by a multiple channel data recording system is employed to determine different heart phases. Since heart valves are very near to each other and are located in the reverberant environment of chest, according to its anatomy, the group-delay information of Recursively Applied and Projected-Multiple Signal Classification spectra (RAP-MUSIC) is utilized to localize active heart valves. The proposed segmentation algorithm is applied to some normal and abnormal (mitral and aortic valve malfunctions) heart sounds recorded by a rectangular microphone array consisted of six sensors. To evaluate the benefits of the proposed method, it is compared with the basic and Tunable Q wavelet and S-transform based segmentation methods, by conducting some proper experiments. The obtained results show that the proposed algorithm is considerably superior to the mentioned methods The average positive predictive value, sensitivity and accuracy results from the proposed segmentation algorithm are 97.4%, 95.5%, and 93%, respectively.