Application of artificial intelligence in the diagnosis of sleep apnea
- S-Med
- Aug 19
- 1 min read
This review shows that the ML techniques can play a crucial role in diagnosis and management of sleep apnea. Currently, the diagnosis of sleep apnea is time-consuming and cumbersome process, often requiring an overnight stay in the sleep laboratory. PSG remains the gold standard method for the diagnosis and severity of sleep apnea, during which clinicians can obtain important information pertinent to a patient’s underlying physiological state. However, the main limitation of this approach is the disruption of the patients’ sleep caused by the multiple sensors and the hospital environment. Although home sleep apnea tests are widely utilized, the accuracy of the diagnosis or severity estimation of OSA with these devices is reduced.
Overall, ML-based diagnosis of sleep apnea is feasible and has demonstrated good performance; furthermore, many wearable technologies have been introduced to address the sleep apnea diagnosis. Different input features have been used, including ECG data, SpO2 signals, respiratory signals, EEG data, sound data, and data from pressure sensor In some models, clinical information is incorporated as well. Huo et al developed a ML-based questionnaire consisting of 2 logistic regression classifiers using clinical information from 2 large observational cohort studies. The model outperformed three commonly used OSA screening questionnaires (4 variable, STOP-BANG and Berlin). ML can be used to predict adverse outcomes associated with OSA. Recently, Li et al demonstrated that in a large cohort of individuals with OSA, RF modeling using AHI, clinical, anthropometric, and demographic information was able to predict 10-year cardiovascular disease mortality.
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