Estimating blood pressure trends and the nocturnal dip from photoplethysmography

Mustafa Radha, Koen De Groot, Nikita Rajani, Cybele CP Wong, Nadja Kobold, Valentina Vos, Pedro Fonseca, Nikolaos Mastellos, Petra A Wark, Nathalie Velthoven, Reinder Haakma, Ronald M Aarts

Research output: Contribution to journalArticlepeer-review

51 Citations (Scopus)
58 Downloads (Pure)


Objective. Evaluate a method for the estimation of the nocturnal systolic blood pressure (SBP) dip from 24 h blood pressure trends using a wrist-worn photoplethysmography (PPG) sensor and a deep neural network in free-living individuals, comparing the deep neural network to traditional machine learning and non-machine learning baselines. Approach. A wrist-worn PPG sensor was worn by 106 healthy individuals for 226 d during which 5111 reference values for blood pressure (BP) were obtained with a 24 h ambulatory BP monitor and matched with the PPG sensor data. Features based on heart rate variability and pulse morphology were extracted from the PPG waveforms. Long- and short term memory (LSTM) networks, dense networks, random forests and linear regression models were trained and evaluated in their capability of tracking trends in BP, as well as the estimation of the SBP dip. Main results. Best performance for estimating the SBP dip were obtained with a deep LSTM neural network with a root mean squared error (RMSE) of 3.12 2.20 mmHg and a correlation of 0.69 . This dip was derived from trend estimates of BP which had an RMSE of 8.22 1.49 mmHg for systolic and 6.55 1.39 mmHg for diastolic BP (DBP). While other models had similar performance for the tracking of relative BP, they did not perform as well as the LSTM for the SBP dip. Significance: The work provides first evidence for the unobtrusive estimation of the nocturnal SBP dip, a highly prognostic clinical parameter. It is also the first to evaluate unobtrusive BP measurement in a large data set of unconstrained 24 h measurements in free-living individuals and provides evidence for the utility of LSTM models in this domain.

Original languageEnglish
Article number025006
JournalPhysiological Measurement
Issue number2
Publication statusPublished - 25 Feb 2019
Externally publishedYes

Bibliographical note

This is an author-created, un-copyedited version of an article accepted for
publication/published in Physiological Measurement. IOP Publishing Ltd is not
responsible for any errors or omissions in this version of the manuscript or any
version derived from it. The Version of Record is available online at 10.1088/1361-


  • ambulatory blood pressure
  • free-living protocol
  • neural networks
  • photoplethysmography

ASJC Scopus subject areas

  • Biophysics
  • Physiology
  • Biomedical Engineering
  • Physiology (medical)


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