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Peer-reviewedCC BY 4.0PLOS One

Time and frequency characteristics of various noninvasive heartbeat sensors

  • Pierre Charlier1
  • Mathieu Jeanne2
  • Maxence Hureau3
  • Julien De Jonckheere4
4 affiliations
  1. 1CHU Lille, Inserm, CIC 1403 - Centre d’investigation clinique, Lille, France
  2. 2CHU Lille, Department of Anesthesiology and Critical Care, CHU de Lille, Lille, France
  3. 3University of Lille, ULR 7365 - GRITA - Groupe de Recherche sur les formes Injectables et les Technologies Associées, University of Lille, Lille, France
  4. 4University of Lille, ULR 2694 – METRICS, Lille, France
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Abstract

Heart rate and cardiac dynamics can be monitored using several physiological signals, each with its own biophysical basis and signal characteristics. However, while individual properties of electrocardiogram (ECG), phonocardiogram (PCG), seismocardiogram (SCG), photoplethysmogram (PPG), and piezoplethysmogram (PiPG) are documented, systematic cross-modal comparisons remain scarce. This study aims to consolidate both the time and frequency domain features of these widely accessible technologies to provide practical guidelines for signal processing and multimodal integration. We simultaneously recorded ECG, PCG, SCG, PPG, and PiPG under standardized resting conditions and aligned all modalities on the ECG R-peak. Results highlight clear consistencies (e.g., reproducible morphology and spectra for ECG, PPG, and PiPG) as well as higher variability in PCG and SCG due to sensor coupling and anatomical factors. Temporal latencies relative to the ECG confirm known physiological conduction delays, while frequency analysis identifies modality-specific bands: ~ 30 Hz for PCG, 11–14 Hz for SCG, and 1–2 Hz for PPG/PiPG. Bandwidth analysis further emphasizes differences in spectral richness across modalities. By presenting these benchmarks in a unified framework, this work addresses the current gap between isolated characterizations and integrated signal knowledge. The outcome is a reference dataset and interpretation map that can guide filtering strategies, feature extraction, and the design of multimodal cardiac monitoring systems.

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Cite

BibTeXRIS
BibTeX
@article{charlier2026time,
  title = {{Time and frequency characteristics of various noninvasive heartbeat sensors}},
  author = {Pierre Charlier and Mathieu Jeanne and Maxence Hureau and Julien De Jonckheere},
  journal = {PLOS One},
  year = {2026},
  month = sep,
  doi = {10.1371/journal.pone.0357922},
  url = {https://doi.org/10.1371/journal.pone.0357922},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Time and frequency characteristics of various noninvasive heartbeat sensors
AU  - Pierre Charlier
AU  - Mathieu Jeanne
AU  - Maxence Hureau
AU  - Julien De Jonckheere
PY  - 2026
DA  - 2026/09/03
JO  - PLOS One
DO  - 10.1371/journal.pone.0357922
UR  - https://doi.org/10.1371/journal.pone.0357922
AB  - Heart rate and cardiac dynamics can be monitored using several physiological signals, each with its own biophysical basis and signal characteristics. However, while individual properties of electrocardiogram (ECG), phonocardiogram (PCG), seismocardiogram (SCG), photoplethysmogram (PPG), and piezoplethysmogram (PiPG) are documented, systematic cross-modal comparisons remain scarce. This study aims to consolidate both the time and frequency domain features of these widely accessible technologies to provide practical guidelines for signal processing and multimodal integration. We simultaneously recorded ECG, PCG, SCG, PPG, and PiPG under standardized resting conditions and aligned all modalities on the ECG R-peak. Results highlight clear consistencies (e.g., reproducible morphology and spectra for ECG, PPG, and PiPG) as well as higher variability in PCG and SCG due to sensor coupling and anatomical factors. Temporal latencies relative to the ECG confirm known physiological conduction delays, while frequency analysis identifies modality-specific bands: ~ 30 Hz for PCG, 11–14 Hz for SCG, and 1–2 Hz for PPG/PiPG. Bandwidth analysis further emphasizes differences in spectral richness across modalities. By presenting these benchmarks in a unified framework, this work addresses the current gap between isolated characterizations and integrated signal knowledge. The outcome is a reference dataset and interpretation map that can guide filtering strategies, feature extraction, and the design of multimodal cardiac monitoring systems.
SN  - 1932-6203
ER  -