Obstructive sleep apnea is often associated with cardiovascular diseases (CVD). Early CVD detection would enhance patient selection for diagnosis and treatment prioritization, to avert development of aggravating CVD. Therefore, the aim of this study is to find markers of CVD risk factors in the pulse photoplethysmography signal, as this enables wearable assessment. To avoid the influence of apneic events on the PPG signal and the requirement to retain the correct sensor positioning for a full night, a method based on wakefulness was investigated.
From the PPG, the wake period in the evening before falling asleep and the wake period after waking up in the morning were extracted. A set of 148 features characterized the PPG waveform, using window sizes of 5s to 85s in steps of 5s. A stratified 10-fold cross validation was repeated 100 times for feature selection and CVD classification of 78 subjects. The mean diastolic width at 10% of pulse amplitude ("mean DT 10%") was overall the most distinctive feature for CVD risk detection as it showed a significant decrease for CVD patients. Pre-sleep pulse width features extracted over 45s resulted in Kappa = 0.46, a sensitivity of 72.1% and specificity of 74.3%.
Overall, the PPG feature "mean DT 10%" contained CVD risk information, but the result requires further validation on larger datasets and wearable sensors.