Paperdoi:10.1109/jbhi.2026.3708184
Unmixing the neck: accurate jugular venous pulse detection from wearable PPG
IEEE JBHI (2026, early access)
TL;DR
Dual-channel neck PPG framework that separates jugular venous from arterial beats: derivative-enhanced DTW with SQI-adaptive weighting plus stability-adjusted feature ranking, 73 candidate features, k-NN under LOSO on 15 healthy volunteers. 28 features, k=19: accuracy 98.65%; with a distance-ratio rejection rule (τ=1.005) arterial-beat sensitivity 99.82%, JVP F1 98.73%, rejects 93.88% of ambiguous out-of-distribution beats while keeping 99.74% precision. Healthy cohort only.
- Form factor: neck
Abstract
ObjectiveNon-invasive monitoring of the jugular venous pulse (JVP) supports heart-failure management, but neck photoplethysmography (PPG) suffers from source ambiguity, in which arterial pulsations contaminate the venous waveform. We present a dual-channel wearable framework that automatically separates jugular venous from arterial waveforms.MethodsThe pipeline pairs a derivative-enhanced dynamic time warping (deDTW) algorithm with a stability-adjusted, distance-based feature-ranking scheme. deDTW quantifies the morphological and timing distance between candidate beats using a signal-quality-adaptive weighting of amplitude and derivative information. From dual-channel neck recordings of 15 volunteers we extracted 73 candidate features and trained a k-nearest-neighbor classifier under leave-one-subject-out (LOSO) cross-validation.ResultsOptimization selected 28 features at k=19, yielding a classification accuracy of 98.65%. When combined with a nearest-neighbor distance-ratio rejection rule ( = 1.005), the system reached 99.82% sensitivity for arterial beats and a 98.73% F1-score for JVP detection, and rejected 93.88% of out-of-distribution ambiguous beats while retaining 99.74% precision for valid JVP signals.ConclusionThe algorithm resolves venous-arterial source ambiguity, addressing a key limitation of wearable JVP analysis.SignificanceBy acting as an automated source-verification gate, the method reduces inter-observer variability and supports continuous, non-invasive JVP monitoring for remote cardiovascular assessment. Validation here is in a healthy cohort; evaluation in patients with cardiovascular disease is the subject of future work.
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Cite this paper
Cao, Y., & Rodriguez-Villegas, E. (2026). Unmixing the neck: accurate jugular venous pulse detection from wearable PPG. IEEE JBHI. https://doi.org/10.1109/jbhi.2026.3708184
Cao, Yichen and Esther Rodriguez-Villegas. 2026. "Unmixing the neck: accurate jugular venous pulse detection from wearable PPG." IEEE JBHI. https://doi.org/10.1109/jbhi.2026.3708184.
Cao, Y. and Rodriguez-Villegas, E. (2026) 'Unmixing the neck: accurate jugular venous pulse detection from wearable PPG', IEEE JBHI. doi: 10.1109/jbhi.2026.3708184.
Cao, Yichen, and Esther Rodriguez-Villegas "Unmixing the neck: accurate jugular venous pulse detection from wearable PPG." IEEE JBHI, 2026. https://doi.org/10.1109/jbhi.2026.3708184.
Y. Cao and E. Rodriguez-Villegas, "Unmixing the neck: accurate jugular venous pulse detection from wearable PPG," IEEE JBHI, 2026, doi: 10.1109/jbhi.2026.3708184.
@article{cao2026unmixing,
title = {{Unmixing the neck: accurate jugular venous pulse detection from wearable PPG}},
author = {Cao, Yichen and Rodriguez-Villegas, Esther},
year = {2026},
journal = {IEEE JBHI},
doi = {10.1109/jbhi.2026.3708184}
}Added to the ChatPPG research hub
