Validation of a BodyConducted Sound Sensor for Respiratory Sound Monitoring and a Comparison with Several Sensors The ideal respiratory sound sensor exhibits high sensitivity, wideband frequency characteristics, and excellent antinoise properties. We investigated the bodyconducted sound sensor (BCS) and verified its usefulness in respiratory sound monitoring through comparison with an aircoupled microphone (ACM) and acceleration sensor (B u0026 K: 8001). We conducted four experiments for comparison: (1) estimation by equivalent circuit model of sensors and measurement by a sensitivity evaluation system; (2) measurement of tissueborne sensitivitytoairnoise sensitivity ratio (SRTA); (3) respiratory sound measurement through a simulator; and (4) actual respiratory sound measurement using human subjects. For (1), the simulation and measured values of all the sensors showed good agreement; BCS demonstrated sensitivity 10 dB higher than ACM and higher sensitivity in the highfrequency segments compared with 8001. In (2), BCS showed high SRTA in the 600xe2x80x931000 and 1200xe2x80x932000Hz frequency segments. In (3), BCS detected wheezes in the highfrequency segments of the respiratory sound. Finally, in (4), the sensors showed similar characteristics and features in the highfrequency segments as the simulators, where typical breathing sound detection was possible. BCS displayed a higher sensitivity and antinoise property in highfrequency segments compared with the other sensors and is a useful respiratory sound sensor.

Proposal of BSS method to separate the respiratory sound and the heart sound Conventionally, there is a method to use auscultation when medical doctors diagnose a disease from heartbeats with heart noise. However, the respiratory sound and the heart sound are mixed. Further, it is difficult for the desired heart sound to be heard. For solving this problem, we propose a method to separate respiratory sound and heart sound based on blind source separation (BSS). In BSS method, we use a nonlinear function corresponding to the probability distribution of source signals in order to separate. A distribution of the respiratory sound is a highorder subGaussian distribution close to uniform distribution, a distribution of the heart sound is a super Gaussian distribution. In the conventional method, there is no nonlinear function corresponding to both distribution. Therefore, in this paper, in order to separate the respiratory sound and the heart sound, we propose new nonlinear function accurately separated from super Gaussian distribution through subGaussian distribution to uniform distribution. Then, we build the BSS method based on the proposed nonlinear function. We evaluate the proposed method by computer simulation.