Key result
PPG-based SVM model detects sleep apnea syndrome with up to ~95% accuracy across sleep stages.
Why the study?
A time-saving, simple, and comfortable method for detecting Sleep Apnea Syndrome (SAS) was needed to improve classification accuracy using photoplethysmographic signals.
Does a Support Vector Machine model using photoplethysmographic morphology accurately detect Sleep Apnea Syndrome?
Observational (n=87)
Does a Support Vector Machine model using photoplethysmographic morphology accurately detect Sleep Apnea Syndrome?
A machine learning model using photoplethysmographic signals can accurately detect Sleep Apnea Syndrome even during the awake period, offering a convenient diagnostic approach.
May support convenient PPG-based SAS screening; hypothesis-generating and requires prospective validation before practice change.
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.
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Jiang et al. (2021) conducted an observational in Sleep Apnea Syndrome (n=87). Photoplethysmographic (PPG) signal analysis using Support Vector Machine vs. Healthy controls was evaluated on Classification accuracy for Sleep Apnea Syndrome across five sleep stages. A Support Vector Machine classification model using photoplethysmographic signals detected Sleep Apnea Syndrome with accuracies ranging from 84.00% to 95.00% across five sleep stages.
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