Academy & Industry Research Collaboration Center (AIRCC)

Volume 9, Number 3, March 2019

Similarity Based Classification and Detection of Respiratory Status in Frequency Domain


Suyeol Kim, Chaehwan Hwang, Jisu Kim, Cheolhyeong Park and Deokwoo Lee, Keimyung University, Daegu, Republic of Korea


Sleep apnea is considered one of the most critical problems of human health, and it is also considered one of the most important bio-signals in the area of medicine. In this paper, we propose the approach to detection and classification of respiratory status based on cross correlation between normal respiration and apnea, and on the characteristics of respiratory signals. The characteristics of the signals are extracted by analyzing frequency analysis. The proposed method is simple and straightforward so that it can be workable in practice. To substantiate the proposed algorithm, the experimental results are provided.


Respiration, Apnea, Fourier transform, Detection, Classification