---
title: "Time and frequency characteristics of noninvasive heartbeat sensors: ECG, PCG, SCG, PPG, PiPG"
id: "plos-one-0-time-and-frequency-characteristics-of-various-noninvasive-heartbeat-sensors"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-0-time-and-frequency-characteristics-of-various-noninvasive-heartbeat-sensors"
content_type: "clinical_feed_article"
specialty: "Cardiology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922"
published_at: "2026-09-03T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Time and frequency characteristics of noninvasive heartbeat sensors: ECG, PCG, SCG, PPG, PiPG
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-0-time-and-frequency-characteristics-of-various-noninvasive-heartbeat-sensors
- **Specialty:** [Cardiology](https://medichelpline.com/clinical-feed/cardiology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922)
- **Published At:** 2026-09-03T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This simultaneous, synchronized study recorded **ECG**, **PCG**, **SCG**, **PPG**, and **PiPG** at 1000 Hz in resting human subjects to produce a unified comparative reference for time- and frequency-domain features. - The acquisition used a custom system with surface Ag/AgCl electrodes for ECG, an electret microphone for PCG, a high-resolution accelerometer (Z-axis) for SCG, a piezoelectric finger sensor for PiPG, and a reflective fingertip PPG (infrared) sensor. - Signals were aligned on the ECG R-peak to compare temporal latencies and morphology across modalities, minimizing inter-system bias by using identical hardware and simultaneous recording. - Findings show reproducible waveform morphology and stable spectral content for **ECG**, **PPG**, and **PiPG**, while **PCG** and **SCG** exhibited higher variability attributed to sensor coupling and anatomical factors. - Temporal latencies relative to the ECG matched expected physiological conduction and mechanical delays; these latency maps can guide multimodal feature alignment and fusion. - Frequency-domain analysis identified modality-specific dominant bands: approximately 30 Hz for PCG, 11–14 Hz for SCG, and 1–2 Hz for PPG and PiPG; bandwidths differ markedly across modalities, reflecting spectral richness. - The study provides practical recommendations for filtering and feature extraction by exporting temporal and spectral content into a single comparative framework rather than validating hardware per se. - A minimal dataset is provided as supporting information (Supporting_Information_file.xlsx), intended as a benchmark for signal-processing, multimodal integration, and wearable cardiac monitoring system design. - The work emphasizes the suitability of these five noninvasive signals for ambulatory monitoring given low-power, compact sensors, and highlights limits of more complex modalities that remain confined to specialized clinical settings.
## Clinical Analysis & Structured Key Points
[ Skip to main content ](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#main-content) Advertisement * [plos.org](https://plos.org/) * [Create account](https://community.plos.org/registration/new) * [Sign in](https://journals.plos.org/user/secure/login?page=%2Fplosone%2Farticle%3Fid%3D10.1371%2Fjournal.pone.0357922) * * About * Browse * Publish * [](https://journals.plos.org/plosone/ "PLOS One") * Search [advanced search](https://journals.plos.org/plosone/search) * [Browse Topics](https://journals.plos.org/plosone/subjectAreaBrowse) Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click [here](https://github.com/PLOS/plos-thesaurus/blob/master/README.md "Link opens in new window"). [](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922) [](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922) * 0 [Save](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357922#savedHeader) [Total Mendeley and Citeulike bookmarks.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357922#savedHeader) * 0 [Citation](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357922#citedHeader) [Paper's citation count computed by Dimensions.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357922#citedHeader) * 32 [View](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357922#viewedHeader) [PLOS views and downloads.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357922#viewedHeader) * 0 [Share](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357922#discussedHeader) [Sum of Facebook, Twitter, Reddit and Wikipedia activity.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357922#discussedHeader) Open Access Peer-reviewed Research Article # Time and frequency characteristics of various noninvasive heartbeat sensors * Pierre Charlier , Roles Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing * E-mail: pierre1.charlier@chu-lille.fr Affiliation CHU Lille, Inserm, CIC 1403 - Centre d’investigation clinique, Lille, France [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0001-5986-3415 ](https://orcid.org/0000-0001-5986-3415 "ORCID Registry") ⨯ * Mathieu Jeanne, Roles Validation Affiliations CHU Lille, Inserm, CIC 1403 - Centre d’investigation clinique, Lille, France, CHU Lille, Department of Anesthesiology and Critical Care, CHU de Lille, Lille, France, University of Lille, ULR 7365 - GRITA - Groupe de Recherche sur les formes Injectables et les Technologies Associées, University of Lille, Lille, France [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0002-3400-5788 ](https://orcid.org/0000-0002-3400-5788 "ORCID Registry") ⨯ * Maxence Hureau, Roles Validation Affiliations CHU Lille, Inserm, CIC 1403 - Centre d’investigation clinique, Lille, France, CHU Lille, Department of Anesthesiology and Critical Care, CHU de Lille, Lille, France, University of Lille, ULR 7365 - GRITA - Groupe de Recherche sur les formes Injectables et les Technologies Associées, University of Lille, Lille, France [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0002-8545-9923 ](https://orcid.org/0000-0002-8545-9923 "ORCID Registry") ⨯ * Julien De Jonckheere Roles Writing – review & editing Affiliations CHU Lille, Inserm, CIC 1403 - Centre d’investigation clinique, Lille, France, University of Lille, ULR 2694 – METRICS, Lille, France [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0002-3727-9434 ](https://orcid.org/0000-0002-3727-9434 "ORCID Registry") ⨯ # Time and frequency characteristics of various noninvasive heartbeat sensors * Pierre Charlier, * Mathieu Jeanne, * Maxence Hureau, * Julien De Jonckheere ![PLOS](https://journals.plos.org/resource/img/logo-plos-full-color.svg) x * Published: September 3, 2026 * * [Article](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922) * [Authors](https://journals.plos.org/plosone/article/authors?id=10.1371/journal.pone.0357922) * [Metrics](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357922) * [Comments](https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0357922) * [Media Coverage](http://plos.altmetric.com/details/doi/10.1371/journal.pone.0357922) * [Peer Review](https://journals.plos.org/plosone/article/peerReview?id=10.1371/journal.pone.0357922) * [Abstract](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#abstract0) * [Introduction](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#sec001) * [Methods](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#sec002) * [Results](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#sec009) * [Discussion](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#sec012) * [Conclusion](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#sec013) * [Supporting information](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#sec014) * [References](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#references) * [Reader Comments](https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0357922) * [Figures](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922) ## 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. ## Figures ![Table 4](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.t004) ![Table 5](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.t005) ![Fig 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.g001) ![Fig 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.g002) ![Fig 3](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.g003) ![Fig 4](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.g004) ![Fig 5](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.g005) ![Table 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.t001) ![Fig 6](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.g006) ![Fig 7](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.g007) ![Fig 8](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.g008) ![Table 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.t002) ![Table 3](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.t003) ![Fig 9](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.g009) ![Table 4](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.t004) ![Table 5](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.t005) ![Fig 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.g001) ![Fig 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.g002) ![Fig 3](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.g003) **Citation:** Charlier P, Jeanne M, Hureau M, De Jonckheere J (2026) Time and frequency characteristics of various noninvasive heartbeat sensors. PLoS One 21(9): e0357922. https://doi.org/10.1371/journal.pone.0357922 **Editor:** Ming Zhang, Hubei University, CHINA **Received:** March 11, 2026; **Accepted:** August 24, 2026; **Published:** September 3, 2026 **Copyright:** © 2026 Charlier et al. This is an open access article distributed under the terms of the [Creative Commons Attribution License](http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. **Data Availability:** The minimal data set is available at Supporting_Information_file.xlsx. **Funding:** The author(s) received no specific funding for this work. **Competing interests:** The authors have declared that no competing interests exist. ## Introduction Instantaneous heart rate (HR) in human beings is commonly assessed through physiological signals related to the electrical, magnetic or mechanical characteristics of the heart through its physiological cycle. Several distinct biophysical principles can be implemented for measuring the changes related to the heart cycle: electromagnetic, mechanical and volumetric ([Fig 1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#pone-0357922-g001)). [![thumbnail](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.g001)](https://journals.plos.org/plosone/article/figure/image?size=medium&id=10.1371/journal.pone.0357922.g001 "Click for larger image") Download: * [PNG larger image](https://journals.plos.org/plosone/article/figure/image?download&size=large&id=10.1371/journal.pone.0357922.g001) * [TIFF original image](https://journals.plos.org/plosone/article/figure/image?download&size=original&id=10.1371/journal.pone.0357922.g001) Fig 1. Example of various biophysical principles for heart cycle characterization. [ https://doi.org/10.1371/journal.pone.0357922.g001](https://doi.org/10.1371/journal.pone.0357922.g001) These signal sources can be exploited to detect the cardiac cycle and subsequently measure instantaneous heart rate (HR). For instance, the electromagnetic activity of the heart can be captured through electrocardiography (ECG) or magnetocardiography (MCG); mechanical activity through phonocardiography (PCG), ballistocardiography (BCG), or seismocardiography (SCG); and volumetric changes via Doppler probes (ultrasound or ultra-wideband), photoplethysmography (PPG), impedance cardiography (ICG), or piezoplethysmography (PiPG). Several cardiac signals are widely accessible, noninvasive and compatible with off-the-shelf sensors, making them well suited for ambulatory and real-world physiological monitoring: ECG, PCG, SCG, PPG, and PiPG, which can be easily integrated into wearable or portable systems due to their low power requirements, compact form factor and simple hardware implementation [[1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#pone.0357922.ref001)–[4](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#pone.0357922.ref004)]. By contrast, more advanced modalities, such as magnetocardiography, impedance cardiography and Doppler-based techniques offer deeper insights into cardiac cycle dynamics but are generally limited to specialized clinical environments because of their technical complexity, higher cost or lack of miniaturized hardware. Numerous studies have demonstrated the feasibility of extracting heart rate variability (HRV) from various physiological signals [[5](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#pone.0357922.ref005),[6](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#pone.0357922.ref006)]. The characterization of their time- and frequency-domain contents has been extensively described for the ECG signal [[2](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#pone.0357922.ref002),[7](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#pone.0357922.ref007)–[12](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#pone.0357922.ref012)], while description and characteristics of other signals are scarce: PCG [[13](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#pone.0357922.ref013)–[15](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#pone.0357922.ref015)], SCG [[16](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#pone.0357922.ref016)–[18](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#pone.0357922.ref018)], PPG [[19](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#pone.0357922.ref019),[20](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#pone.0357922.ref020)] and BCG [[20](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#pone.0357922.ref020)–[22](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#pone.0357922.ref022)]. The objective of this study is to present a unified cross-modal characterization of ECG, PCG, SCG, PPG, and PiPG signals acquired simultaneously in human beings under standardized resting conditions and export their temporal and spectral content into a single comparative framework. By exporting their temporal and spectral content into a single comparative framework using identical hardware, this work aims to provide a signal-processing benchmark and physiological comparison that eliminates the inter-system biases often found in separate studies, rather than a hardware-validation study. We aim to provide practical guidelines for filtering, signal analysis and feature extraction in order to contribute to the standardization and optimization of multimodal, noninvasive, wearable cardiac monitoring technologies and facilitate future sensor fusion strategies. Furthermore, while this study focuses on biomedical sensor fusion, these approaches can benefit from and contribute to broader developments in multimodal integrated sensing systems [[23](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#pone.0357922.ref023)–[27](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#pone.0357922.ref027)]. ## Methods ### Acquisition system Physiological data were acquired using a custom acquisition system developed at the CICIT 1403 (Lille, France) operating at a sampling rate of 1000 Hz and enabling the simultaneous and synchronized recording of five signal modalities: ECG, PCG, SCG, PPG and PiPG ([Fig 2](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#pone-0357922-g002)). [![thumbnail](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357922.g002)](https://journals.plos.org/plosone/article/figure/image?size=medium&id=10.1371/journal.pone.0357922.g002 "Click for larger image") Download: * [PNG larger image](https://journals.plos.org/plosone/article/figure/image?download&size=large&id=10.1371/journal.pone.0357922.g002) * [TIFF original image](https://journals.plos.org/plosone/article/figure/image?download&size=original&id=10.1371/journal.pone.0357922.g002) Fig 2. Schematic display of the various waveforms obtained simultaneously: Electrocardiogram (ECG), Phonocardiogram (PCG), Seismocardiogram (SCG), Piezoplethysmogram (PiPG) and Photoplethysmogram (PPG). [ https://doi.org/10.1371/journal.pone.0357922.g002](https://doi.org/10.1371/journal.pone.0357922.g002) 1. **ECG –** Electrocardiogram. The cardiac electrical activity was measured using surface Ag/AgCl adhesive electrodes (Red Dot 2670−5, 3M, USA), positioned in a standard Lead I configuration on the thorax. To minimize motion artifacts and enhance signal stability, a driven-right-leg (DRL) circuit was implemented using a bias electrode, effectively reducing common-mode interference, e.g., 50 Hz noise from the power grid. As the system is battery-powered, this further minimizes external power line interference. 2. **PCG –** Phonocardiogram. Cardiac sounds related to valve closure during the various phases of the cardiac cycle were recorded using an electret microphone (AOM-6738L-R, Projects Unlimited, USA) featuring high sensitivity (−38 dB) and stable frequency response from 50 Hz with an omni-directional polar pattern providing consistent performance in the low-frequency range. The analog audio signal was amplified using a dedicated audio preamplifier (TS472, STMicroelectronics, Switzerland), which also provided the necessary bias voltage for the microphone. This preamplifier provides a high bandwidth of 40 kHz and a very low Equivalent Input Noise (EIN) of 10 nV/√Hz. 3. **SCG –** Seismocardiogram. Thorax vibrations produced by the beating heart were captured using a high-resolution analog accelerometer (ADXL356, Analog Devices, USA) placed on the lower sternum. This sensor provides a wide frequency response (up to 2400 Hz on the Z-axis) and low noise density (80 μg/√Hz). Although the used accelerometer is three-dimensional, this study focused on the Z-axis in accordance with the orientation of the sensor on the chest. 4. **PiPG –** Piezoplethysmogram. A piezoelectric sensor (MU0292−1, GETMUSIC, China) was placed on an adjacent finger to the PPG probe. The PiPG sensor captures local mechanical changes caused by arterial pressure waves. 5. **PPG –** Photoplethysmogram. Blood volume changes at the fingertip were measured using a reflective-mode PPG sensor (MAX30102, Maxim Integrated, USA), integrating red (660 nm) and infrared (880 nm) LEDs along with a photodiode. Only the infrared channel was analyzed for heart rate and heart rate variability analysis, because of its higher performance than red light sensors [[1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357922#pone.0357922.ref001)]. **Signal digitization and acquisition**. Except for the PPG signal, all analog signals were digitized using a 24-bit digital converter (ADS1298, Texas Instruments, USA) at a 1000 Hz sampling rate. This analog front-end features a highly lin
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