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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
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Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
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Automated Heart Rate Detection in Seismocardiograms Using Electrocardiogram-Based Algorithms-A Feasibility Study.

Evgenii Pustozerov1, Ulf Kulau2, Urs-Vito Albrecht1

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Summary

Algorithms for electrocardiographic (ECG) signals can be adapted for seismocardiographic (SCG) signals to calculate heart rate (HR). This study validates these methods, showing promise for SCG in real-world HR monitoring.

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Area of Science:

  • Biomedical Engineering
  • Cardiovascular Physiology
  • Signal Processing

Background:

  • Heart rate (HR) analysis traditionally relies on electrocardiographic (ECG) signals.
  • Seismocardiography (SCG) offers a non-invasive alternative for monitoring cardiac activity.
  • Existing HR algorithms developed for ECG present a potential avenue for SCG signal analysis.

Purpose of the Study:

  • To investigate the applicability of ECG-derived R-peak detection algorithms for HR calculation using SCG signals.
  • To evaluate and optimize preprocessing and peak detection methods for SCG-based HR estimation.
  • To compare the precision of SCG-based HR detection against ECG and photoplethysmography (PPG) methods.

Main Methods:

  • Adapted established ECG signal processing and peak detection algorithms for SCG signals.
  • Systematically assessed 15 peak detection and 6 preprocessing methods on the CEBS dataset.
  • Validated the best-performing method on lab-collected SCG data during rest and physical activity.

Main Results:

  • The selected methods demonstrated high precision for HR detection from SCG during sitting rest (0.12 ± 0.35 bpm difference from ECG).
  • Moderate precision was observed during physical activity (6.45 ± 3.01 bpm difference from ECG).
  • Performance was comparable to state-of-the-art photoplethysmographic (PPG) methods in literature.

Conclusions:

  • Computationally simple preprocessing and peak detection techniques from ECG are viable for HR detection in SCG.
  • SCG shows potential for HR monitoring, particularly in resting conditions.
  • Further improvements are needed to enhance SCG HR detection accuracy during physical activity.