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Related Experiment Video

Updated: Aug 16, 2025

Dual-Dye Optical Mapping of Hearts from RyR2R2474S Knock-In Mice of Catecholaminergic Polymorphic Ventricular Tachycardia
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Dual Wavelength Photoplethysmography Framework for Heart Rate Calculation.

Ludvik Alkhoury1, JiWon Choi1, Vishnu D Chandran2

  • 1Department of Electrical and Computer Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA.

Sensors (Basel, Switzerland)
|December 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a Dual Wavelength (DWL) framework for accurate heart rate (HR) calculation from photoplethysmography (PPG) signals during intense physical activity. The DWL method effectively removes motion artifacts without needing extra sensors, proving efficient for wearables.

Keywords:
dual-wavelength PPGgreen and infrared PPGheart rate calculationmotion artifacts reductionphotoplethysmography (PPG)

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Area of Science:

  • Biomedical Engineering
  • Physiological Monitoring
  • Signal Processing

Background:

  • Photoplethysmography (PPG) signal quality for heart rate (HR) extraction degrades with motion.
  • Current methods often rely on auxiliary signals like accelerometers to mitigate motion artifacts.
  • Previous noise-component denoising techniques were limited to minor movements.

Purpose of the Study:

  • To extend PPG-based HR calculation to high-intensity, full-body motion.
  • To introduce and validate the Dual Wavelength (DWL) framework for robust HR estimation.
  • To assess the efficacy of DWL in eliminating the need for auxiliary motion sensors.

Main Methods:

  • Developed a Dual Wavelength (DWL) framework utilizing noise components from infrared PPG to denoise green PPG signals.
  • Collected PPG data from 14 participants during treadmill running (high-intensity macro-motion).
  • Compared DWL performance against accelerometer-dependent methods (TROIKA, JOSS).

Main Results:

  • DWL demonstrated high accuracy in HR calculation during intense motion, outperforming existing methods.
  • The DWL framework successfully operated using only PPG signals, negating the need for accelerometers.
  • Achieved a Mean Absolute Error (MAE) of 1.22±0.57 BPM and a performance index (PI) of 95.88±4.9%.
  • Demonstrated computational efficiency with a processing time of 3.0±0.3 seconds for a 360-second run.

Conclusions:

  • The DWL framework enables accurate HR calculation from PPG signals even under significant motion.
  • DWL offers a sensor-efficient and computationally effective solution for wearable HR monitoring.
  • This technique advances PPG-based physiological monitoring for dynamic activities.