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Related Concept Videos

Correlation between ECG and Cardiac Cycle01:25

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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
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Factors Influencing Heart Rate01:30

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The heart rate, or pulse rate, is a vital indicator of cardiovascular health. It reflects the number of times the heart beats per minute. Various physiological and environmental factors influence heart rate, increasing or decreasing cardiac output. Understanding these factors is crucial for assessing heart function and identifying potential health issues.
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Related Experiment Video

Updated: Feb 20, 2026

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
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Adaptive Autocorrelation Based Heart Rate Estimation from Single-Axis Seismocardiogram: A Comprehensive Benchmark

Ajdar Ullah, Ismail Elnaggar, Sepehr Seifizarei

    IEEE Journal of Biomedical and Health Informatics
    |February 18, 2026
    PubMed
    Summary

    A new method called Adaptive Autocorrelation Function Detector (AACFD) accurately estimates heart rate (HR) from seismocardiograms (SCG) using a single accelerometer axis. This lightweight, automatic pipeline achieves sub-bpm accuracy on resting data and clinically acceptable results for various heart conditions.

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    Area of Science:

    • Biomedical Engineering
    • Cardiovascular Physiology
    • Signal Processing

    Background:

    • Seismocardiography (SCG) offers a non-invasive method for monitoring heart rate (HR).
    • Existing SCG-based HR estimation methods often require ECG calibration or machine learning, limiting their applicability.
    • There is a need for lightweight, automatic, and calibration-free algorithms for HR estimation from SCG.

    Purpose of the Study:

    • To introduce and validate the Adaptive Autocorrelation Function Detector (AACFD), a novel pipeline for window-averaged HR estimation from single-axis SCG.
    • To demonstrate AACFD's ability to operate without ECG calibration or machine learning, making it broadly accessible.
    • To evaluate AACFD's performance across diverse datasets, including healthy subjects and patients with cardiovascular conditions.

    Main Methods:

    • AACFD combines YIN-style difference-function analysis of an adaptive SCG envelope with short-window autocorrelation on 3-second segments.
    • A feature-aware weighting strategy fuses the two detection branches, incorporating signal-quality indices and multi-scale features.
    • Hampel filtering and temporal consistency checks are employed to remove outlier windows, ensuring robustness.

    Main Results:

    • AACFD achieved a mean absolute error (MAE) of less than 1 bpm on resting datasets (MCG, CEBS).
    • Clinically acceptable MAEs were observed in valvular heart disease (VHD) and invasive right-heart catheterization (RHC) cohorts (4.38 bpm and 3.58 bpm on 30-s windows, respectively).
    • Over 90% of non-overlapping 10-60 second windows passed quality control across all datasets, indicating high usability.

    Conclusions:

    • The AACFD algorithm provides accurate window-averaged HR estimation from single-axis SCG, even in challenging clinical and mobile settings.
    • Its lightweight and automatic nature allows for real-time implementation on commodity hardware.
    • AACFD represents a significant advancement for non-invasive cardiac monitoring, particularly for applications where ECG is unavailable or impractical.