Robust heart sound detection in respiratory sound using LRT with maximum a posteriori based online parameter adaptation

This paper investigates the utility of a likelihood ratio test (LRT) combined with an efficient adaptation procedure for the purpose of detecting the heart sound (HS) with lung sound and the lung sound only (non-HS) segments in a respiratory signal. The proposed detection method has four main stages: feature extraction, training of the models, detection, and adaptation of the model parameter. In the first stage, the logarithmic energy features are extracted for each frame of respiratory sound. In the second stage, the probabilistic models for HS and non-HS segments are constructed by training Gaussian mixture models (GMMs) with an expectation maximization algorithm in a subject-independent manner, and then the HS and non-HS segments are detected by the results of the LRT based on the GMMs. In the adaptation stage, the subject-independent trained model parameter is modified online using the observed test data to fit the model parameter of the target subject. Experiments were performed on the database from 24 healthy subjects. The experimental results indicate that the proposed heart sound detection algorithm outperforms two well-known heart sound detection methods in terms of the values of the normalized area under the detection error trade-off curve (NAUC), the false negative rate (FNR), and the false positive rate (FPR).