Separation Algorithm for Biosignals as Preprocess in Detecting Circulatory Disease

Abstract

A method of separating heart sounds, breathing sounds, and bloodstream sounds (intended signals) from the sounds in the human body (biosignals) using microphone sensors is described as a preprocess for detecting circulatory disease such as irregular heart beat (IHB), arterial sclerosis and sleep apnea syndrome (SAS). In this paper, breathing sounds are defined as bronchial sounds. To separate intended signals from biosignals, the independent component analysis (ICA) algorithm and time-frequency masking by the expectation-maximization (EM) algorithm have been used. However, the separation filter in ICA does not work well if the recording environment has considerable reverberation. In addition, time-frequency masking of the EM algorithm is a noise and local solution problem depending on the initial value. Thus, we propose a new algorithm to solve these problems. Experimental results show that our method performs better than ICA and time-frequency masking of the EM algorithm.