Affiliation:
1. School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, P. R. China
2. School of Control Science and Engineering, Shandong University, Jinan 250061, P. R. China
3. School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
4. School of Science, Shandong Jianzhu Uniersity, Jinan 250101, P. R. China
Abstract
This paper develops a time-saving, simple, and comfortable method for detecting Sleep Apnea Syndrome (SAS). Seventy SAS patients and 17 healthy persons were randomly selected in this study, and nine analytical parameters (i.e., [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] of healthy persons and SAS patients during five sleep stages (i.e., W, R, N1, N2, and N3) were obtained to construct a SAS classification model based on logarithmic normal analytical parameters using the Support Vector Machine (SVM) method to fit Photoplethysmographic (PPG) signals. The results show that there were no statistical differences among the five sleep stages for either the healthy or SAS patients. However, there were significant differences in the measured logarithmic normal analytical parameters between the healthy persons and the SAS patients in each of the five sleep stages. The accuracies of the SAS classification model were 95.00%, 90.00%, 84.00%, 94.67%, and 90.77%, corresponding to the five sleep stages, respectively. The SAS classification model based on the SVM method of logarithmic normal analysis parameters can achieve higher classification accuracy for each of the five sleep stages. It can be considered to collect the patient’s pulse wave during the awake period, but not necessarily during the sleep period to classify and identify the SAS; it provides an idea for a convenient and comfortable SAS detection.
Publisher
World Scientific Pub Co Pte Lt
Cited by
2 articles.
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