Back

High-Fidelity Measurement of Pulse Arrival Time in Critically Ill Children Using Standard Bedside Monitoring Equipment

Ruffolo, I.; Siddiqui, A.; Nguyen, B.; Dixon, W.; Assadi, A.; Greer, R.; Schwartz, S.; Brudno, M.; Mariakakis, A.; Goodwin, A.

2025-04-01 health informatics
10.1101/2025.03.31.25324979 medRxiv
Show abstract

Pulse arrival time (PAT) is known to be correlated with blood pressure. Although PAT can be measured using electrocardiography (ECG), photoplethysmography (PPG), and other signals commonly available in clinical settings, recent literature has noted that devices recording these waveforms are often subject to many hardware-specific factors related to digital filtering, clock synchronization, temporal resolution, and latency. These factors can introduce relative timing errors between the ECG and PPG signals, resulting in a situation where traditional approaches for PAT measurement will not work as intended. In this work, we propose a methodology that accounts for these confounding factors and generates precise measurements of PAT using standard bedside monitoring equipment. This technique involves using heart rate variability to match heartbeats across waveforms and experimentally profiling the timing systems of bedside medical devices to correct various timing-related artifacts. To improve the precision of the resulting PAT measurements, we model temporal uncertainties stemming from the finite temporal resolution of the waveform samples. We apply this approach to a dataset with roughly 1.6 million hours of continuous ECG and PPG data from over 10,000 unique patients at a pediatric intensive care unit (ICU). After demonstrating that the observed timing artifacts are consistent across the entire dataset, we show that accounting for them results in more reasonable distributions of PAT measurements across age groups. It is our hope that this work will spur discussion around the standardization of PAT measurement using routinely collected signals in a clinical environment.

Published in Physiological Measurement (predicted rank #1) · training set

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

Physiological Measurement · published here
14 papers in training set
Top 0.1%
23.0%
2
Scientific Reports
3612 papers in training set
Top 4%
10.1%
3
Neurophotonics
42 papers in training set
Top 0.1%
8.1%
4
PLOS Digital Health
106 papers in training set
Top 0.6%
8.1%
5
Frontiers in Physiology
106 papers in training set
Top 0.2%
5.6%
50% of probability mass above
6
Computer Methods and Programs in Biomedicine
28 papers in training set
Top 0.1%
5.3%
7
PLOS ONE
5266 papers in training set
Top 33%
4.2%
8
Frontiers in Neuroscience
256 papers in training set
Top 1%
3.3%
9
IEEE Transactions on Biomedical Engineering
40 papers in training set
Top 0.3%
3.3%
10
JAMIA Open
42 papers in training set
Top 0.5%
3.3%
11
Sensors
43 papers in training set
Top 0.4%
2.7%
12
npj Digital Medicine
118 papers in training set
Top 2%
1.8%
13
IEEE Journal of Biomedical and Health Informatics
37 papers in training set
Top 0.6%
1.8%
14
Frontiers in Digital Health
24 papers in training set
Top 1%
1.2%
15
Journal of the American Medical Informatics Association
71 papers in training set
Top 2%
1.2%
16
IEEE Transactions on Neural Systems and Rehabilitation Engineering
49 papers in training set
Top 0.7%
1.2%
17
iScience
1154 papers in training set
Top 24%
1.2%
18
Computers in Biology and Medicine
128 papers in training set
Top 4%
1.1%
19
Royal Society Open Science
214 papers in training set
Top 7%
0.6%
20
Diagnostics
50 papers in training set
Top 3%
0.6%
21
Critical Care Explorations
14 papers in training set
Top 0.4%
0.6%