A weighted least mean squares algorithm achieved complete attenuation for tone noises but minor or no attenuation for hospital and breathing noise, which was predicted by the coherence function.
The coherence function can predict the amount of attenuation achievable by linear adaptive noise-cancellation techniques like WLMS in acoustic sensor systems for detecting coronary artery disease.
Adaptive noise cancellation is a useful linear technique to attenuate unwanted background noise that cannot be removed using traditional frequency-selective filters. Usually, this is due to the signal and noise co-existing in the same frequency band. This paper tests a weighted least mean squares (WLMS) algorithm on a stethoscope system for use in detecting coronary artery disease in the presence of background noise. Each stethoscope is equipped with two microphones: one used to detect heart signals and one used to detect background noise. The WLMS method was used for four different sources of background noise whilst measuring a heartbeat, including a single tone, multiple tones, hospital/clinic noise, and breathing noise. The magnitude-squared coherence between both microphones was unity for the tone scenarios, resulting in complete attenuation. For the other background noise sources, a less-than-unity magnitude-squared coherence resulted in minor and no attenuation. Thus, the coherence function is a tool that can be used to predict the amount of attenuation achievable by linear adaptive noise-cancellation techniques, such as WLMS, as presented in this article.
Fynn et al. (Wed,) conducted a other in Coronary artery disease. Weighted least mean squares (WLMS) algorithm on a stethoscope system was evaluated on Attenuation of background noise. A weighted least mean squares algorithm achieved complete attenuation for tone noises but minor or no attenuation for hospital and breathing noise, which was predicted by the coherence function.