Adaptive segmentation and normalization of breathing acoustic data of subjects with obstructive sleep apnea

Breath sounds in patients with obstructive sleep apnea are very dynamic and variable signals due to their versatile nature. In this paper, we present an adaptive segmentation algorithm for these sounds. The algorithm divides the breath sounds into segments with similar amplitude levels. As the first step, the proposed scheme creates an envelope of the signal characterizing its long term amplitude variations. Then, K-means clustering is iteratively applied to detect borders between different segments in the envelope, which will then be used to segment and normalize the original signal.