Abstract

Info

Title:: A new feature extraction method of respiration signal and its application

Author(s):: CUI Xingxing; SU Zhijian; College of Mechanical Engineering, Zhengzhou University, Zhengzhou 450001, China

Keywords:: ; respiratory sound signals; short-time energy; short-time zero crossing rate; characteristics extraction; characteristics recognition

Abstract:: Objective To extract and classify the respiratory sound characteristics by studying the internal relationship between respiratory sound signals and respiratory diseases and provide technical preparations for developing a portable household breathing sound remote monitoring mobile device. Methods After the pretreatment analysis of the collected respiratory sounds, we extracted the characteristic values of short-time energy and short-time zero crossing rate from the processed respiratory sound original data. Results The characteristic differences were displayed by the energy changes of different time quantum respiratory signals, and the results indicated that the high-and low-frequency abnormal signals of respiratory sounds have trivial influence on the feature extraction method. Conclusion The proposed method can be used to extract characteristic values simply and effectively, not only simplifying the characteristics recognition and data processing, but also obtaining the extracted feature parameters which meet the basic characteristics, including otherness, uniformity and correlation. The study provides a theoretical basis and actual data support for establishing the input of neural network.