文章基本信息

标题：Similarity Based Classification and Detection of Respiratory Status in Frequency Domain
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作者：Suyeol Kim ; Chaehwan Hwang ; Jisu Kim 等
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2019
卷号：9
期号：3
页码：1-9
DOI：10.5121/csit.2019.90301
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：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.
关键词：Respiration; Apnea; Fourier transform; Detection; Classification