Comparison of Left and Right Fingertip PPG Signals Using Signal Power Estimates and Poincare Indexes

Bozkurt, Surhan; Ertaş, Gökhan

doi:10.11648/j.ajbls.s.2015030202.12

Cited by 2 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Participants were instructed on how to wear the band and at what tightness (based on arm circumference). It was decided to standardise the location of the PPG sensor in this study to the right wrist as differences in PPG are minimal across wrists [36,37]. Participants were asked to wear the devices for 24-hours and carry on with their regular working patterns and routine.…”

Section: Datamentioning

confidence: 99%

Estimating blood pressure trends and the nocturnal dip from photoplethysmography

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Section: Datamentioning

confidence: 99%