Case Classification Processing and Analysis Method for Respiratory Belt Data

Abstract

Human respiratory signal is the important physiological indicator to reflect the physical condition. The respiratory belt, compared with the other human respiratory data measurement methods, has the advantages of being portable, cheap, non-invasive, etc. However, it is unclear which features of the breathing data can effectively classify the normal/abnormal state of breathing state. To solve the problem, we proposed a novel approach based on long-short-term-memory (LSTM) and breathing features of respiratory data. First, LSTM structure were used, then compared the result with the traditional method which extract the feature to experiment (in our paper which is RIE (ratio of inspiratory time to expiratory time)). In the end, a novel methodology proposed which combined the RIE feature with the LSTM structure. Experiment the three methods above using 342 normal and abnormal 24-h breathing data, the results show that the third method has higher classification accuracy.