SleepBP-Net: A Time-Distributed Convolutional Network for Nocturnal Blood Pressure Estimation from Photoplethysmogram.

Nocturnal blood pressure (BP) monitoring offers valuable insights into various aspects of human wellbeing, particularly cardiovascular health. Despite recent advancements in medical technology, there remains a pressing need for a non-invasive, cuffless, and less burdensome method for overnight BP measurements. A range of machine learning models have been developed to estimate daytime BP using photoplethysmography (PPG), a readily available sensor embedded in modern wearable devices. However, investigations into nocturnal BP estimation, especially concerning long-term data patterns during sleep, are still lacking. This paper investigates the estimation of nocturnal BP from overnight PPG signals collected in a clinical-grade sleep laboratory setting. To address this, we propose SleepBP-Net, a lightweight time-distributed convolutional recurrent network. This novel model leverages long-term patterns within PPG waveforms to estimate systolic and diastolic BP (SBP and DBP), considering Portapres BP measurements as a reference. Our experiments, based on leave-one-subject-out validation on 1-minute sequences of PPG, resulted in a mean absolute error (MAE) of 15.7 mmHg (SBP) and 12.1 mmHg (DBP). Model personalization improved the results to 7.8 mmHg (SBP) and 5.9 mmHg (DBP). Further enhancements were observed when extending the sequence length to 30 minutes, resulting in MAE values of 7.2 mmHg (SBP) and 5.7 mmHg (DBP). These findings underscore the significance of learning long-term temporal patterns from sleep PPG data. Additionally, we demonstrate the superiority of hybrid convolutional recurrent networks over their convolutional network counterparts. Based on our results, SleepBP-Net holds promise for unobtrusive real-world nocturnal BP estimation, particularly in scenarios where computational efficiency is crucial.