Automated Detection System for Acoustic Signal of Breath

Abstract

The breathing signal itself contains rich information. By observing breathing signals, we can analyze many physical functions. However, most of the old breathing measurement methods such as straps and stethoscopes require the assistance of others and are easy to restrain the patient, so that it is difficult to get close to real life situations.In this study, We first use the microphone of the personal mobile phone to record the breathing signal, and use Mel-Frequency Cepstral Coefficient to obtain the characteristics. Then, with DNN, it can successfully achieve automatic classification of exhale, inhale and silence phases in human breathing behavior. The accuracy rate is as high as 94.66% when there are 90 subjects. In addition, DNN is also used to do recognition of respiratory symptoms. Combined with the analysis of the breathing rate, we complete an integrated system for judging symptoms and fatigue detection based on the respiratory signal.