Estimating blood pressure trends and the nocturnal dip from photoplethysmography

Abstract

Objective: Evaluate a method for the estimation of the nocturnal systolic blood pressure (SBP) dip from 24-hour 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 days during which 5111 reference values for blood pressure (BP) were obtained with a 24-hour 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 (p = 3 * 10 −5 ). 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 24hour measurements in free-living individuals and provides evidence for the utility of LSTM models in this domain.

Estimation and Validation of Arterial Blood Pressure Using Photoplethysmogram Morphology Features in Conjunction With Pulse Arrival Time in Large Open Databases

“…In order to find alternative predictors associated with BP to improve the estimation accuracy, many researchers have attempted to analyze the morphology of the PPG waveform [25][26][27][28][29][30][31], since it theoretically could reflect both the ejection of blood pulse from the heart and the conditions of the peripheral artery [32]. However, most of these studies have analyzed [33][34][35][36][37][38], the database had issues with inter-waveform alignments due to unspecified filtering delays or channel delays, which made the data unsuitable for traditional inter-waveform analysis [39].…”

Non-Invasive Risk Stratification of Hypertension: A Systematic Comparison of Machine Learning Algorithms

“…In ref. [20], Radha and colleagues focus on the problem of estimating the nocturnal systolic blood pressure (SBP) dip taking place at night starting from 24-h blood pressure data. This problem is faced by them through the use of a PPG sensor and a deep neural network.…”

Towards wearable blood pressure measurement systems from biosignals: a review

“…In machine learning methods, it is provided to produce output values for different inputs by training the corresponding output values of certain input values. Models such as random forest [115,116], regression tree [117], support vector machines [118], K-nearest neighbors, and deep learning [119] are used in the analysis of blood pressure measurements. There is a standard of IEEE on wearable blood pressure gauges [120].…”