3D heart sound source localization via combinational subspace methods for long-term heart monitoring

Heart sound is a key mean for heart monitoring and a common known early diagnosis modality. In this paper, by employing a multiple channel sound recording system and implementing a spatiotemporal information extraction process, a powerful approach is proposed for heart monitoring. Use of the extracted spatiotemporal information of the heart sound, which in turn could lead to detection of the time duration and the heart abnormalities source locations, gives a clinical contribution to the diagnosis of heart valvular diseases. Since the heart valves have very small distances from each other and are located in a reverberant environment of chest and its surrounding muscles, we take the advantages of the group-delay information of the Recursively Applied and Projected-MUltiple SIgnal Classification (RAP-MUSIC) method to localize the active heart valves in the S1, systole, S2, and diastole phases of the heart sound. The proposed algorithm is applied to a simulated database and compared with the MUltiple SIgnal Classification (MUSIC), RAPMUSIC and group delayed MUSIC algorithms. The comparison shows the proposed combinational method has the highest resolution in the simulated environment and more accuracy when sources are very close. In the following, the proposed algorithm is applied to some normal and abnormal heart sounds recorded by the employed microphone array. The obtained results indicate that the proposed method can localize the heart valves in real conditions. Moreover, it is shown that via this approach, the extra abnormal sound sources in the heart of the patients, if exist, could be properly detected and localized. This 3D heart valve localization method is an appropriate approach suitable to be employed in long-term heart monitoring instruments.