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Objective: Evaluate a method for the estimation of the nocturnal systolicblood pressure (SBP) dip from 24-hour blood pressure trends using a wrist-wornphotoplethysmography (PPG) sensor and a deep neural network in free-livingindividuals, comparing the deep neural network to traditional machine learningand non-machine learning baselines.  Approach: A wrist-worn PPG sensor was worn by 106 healthy individuals for 226days during which 5111 reference values for blood pressure (BP) were obtainedwith a 24-hour ambulatory BP monitor and matched with the PPG sensor data.Features based on heart rate variability and pulse morphology were extractedfrom the PPG waveforms. Long- and short term memory (LSTM) networks, densenetworks, random forests and linear regression models were trained andevaluated in their capability of tracking trends in BP, as well as theestimation of the SBP dip.  Main results: Best performance for estimating the SBP dip were obtained witha deep LSTM neural network with a root mean squared error (RMSE) of3.12$\pm$2.20 $\Delta$mmHg and a correlation of 0.69 $(p=3*10^{-5})$. This dipwas derived from trend estimates of BP which had an RMSE of 8.22$\pm$1.49 mmHgfor systolic and 6.55$\pm$1.39 mmHg for diastolic BP (DBP). While other modelshad similar performance for the tracking of relative BP, they did not performas well as the LSTM for the SBP dip.  Significance: The work provides first evidence for the unobtrusive estimationof the nocturnal SBP dip, a highly prognostic clinical parameter. It is alsothe first to evaluate unobtrusive BP measurement in a large data set ofunconstrained 24-hour measurements in free-living individuals and providesevidence for the utility of LSTM models in this domain.

Estimating blood pressure trends and the nocturnal dip from photoplethysmograph