An Improved Algorithm for Segmentation and Determination of Respiratory Phase Times Based on Temporal Processing of Nasal and Mouth Breathing Sound Signals

This work proposes a computational algorithm to improve the determination of the timing of the respiratory phases. The algorithm was developed using a database of breathing sound signals acquired through properly positioned face masks and electret microphones. Most of the proposed works use the frequency domain and decimation in time to detect the respiratory period and phases, as well as some specific pathology. In this work the processing applied is only in time without applying decimation, thus improving the detection of a greater number of respiratory periods. The segmentation is very important since it allows the isolation of phases of the signal to later detect some pathology or to estimate the volume of inspired and exhaled air. The proposed algorithm involves the extraction of signal envelopes with the use of high selectivity filters without decimation and adaptive normalization processes that aim to achieve an adequate detection. In the validation process, the algorithm detection results were compared with the timing of respiratory periods and phases marked by visual inspection. The results show a maximum error of 4.36% for the respiratory period and 3.23% and 3.09% for the expiration and inspiration times, respectively.