Cited by 274Non-Invasive Vital Sign MonitoringECG Monitoring and AnalysisHeart Rate Variability and Autonomic Control

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Dynamic time warping and machine learning for signal quality assessment of pulsatile signals

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Li, Q, Clifford, G D·2012·Physiological Measurement

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Abstract

In this work, we describe a beat-by-beat method for assessing the clinical utility of pulsatile waveforms, primarily recorded from cardiovascular blood volume or pressure changes, concentrating on the photoplethysmogram (PPG). Physiological blood flow is nonstationary, with pulses changing in height, width and morphology due to changes in heart rate, cardiac output, sensor type and hardware or software pre-processing requirements. Moreover, considerable inter-individual and sensor-location variability exists. Simple template matching methods are therefore inappropriate, and a patient-specific adaptive initialization is therefore required. We introduce dynamic time warping to stretch each beat to match a running template and combine it with several other features related to signal quality, including correlation and the percentage of the beat that appeared to be clipped. The features were then presented to a multi-layer perceptron neural network to learn the relationships between the parameters in the presence of good- and bad-quality pulses. An expert-labeled database of 1055 segments of PPG, each 6 s long, recorded from 104 separate critical care admissions during both normal and verified arrhythmic events, was used to train and test our algorithms. An accuracy of 97.5% on the training set and 95.2% on test set was found. The algorithm could be deployed as a stand-alone signal quality assessment algorithm for vetting the clinical utility of PPG traces or any similar quasi-periodic signal.

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Authors: Li, Q., & Clifford, G. D.
Affiliations: Shandong University (CN)
University of Oxford (GB)
ORCID: Li, Q
Clifford, G D
Venue: Physiological Measurement
Publisher: IOP Publishing
Published: 2012-08-17
Volume: 33
Issue: 9
Pages: 1491-1501
ISSN: 0967-3334 (ISSN-L)
1361-6579
DOI: 10.1088/0967-3334/33/9/1491
OpenAlex: W1989977532
Cited by: 274
References: 31
Citations by year: 2026: 18 · 2025: 13 · 2024: 17 · 2023: 26 · 2022: 28 · 2021: 25 · 2020: 22 · 2019: 18 · 2018: 25 · 2017: 20 · 2016: 25 · 2015: 15 · 2014: 9 · 2013: 8 · 2012: 5
FWCI: 6.74
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: Physiological Measurement (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
Keywords: photoplethysmogram, signal quality assessment, dynamic time warping, template matching, multilayer perceptron, arrhythmia detection, intensive care monitoring
MeSH terms: Algorithms, Artificial Intelligence, Humans, Quality Control, Time Factors, Photoplethysmography

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Dynamic time warping and machine learning for signal quality assessment of pulsatile signals

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