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Instrumentation Amplifier01:25

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An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...
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Adaptive Layer-Dependent Threshold Function for Wavelet Denoising of ECG and Multimode Fiber Cardiorespiratory

Yuanfang Zhang1, Kaimin Yu2, Chufeng Huang1

  • 1School of Ocean Information Engineering, Jimei University, Xiamen 361021, China.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
Summary

This study introduces an adaptive thresholding method for denoising electrocardiogram (ECG) and cardiopulmonary signals. The novel approach effectively removes noise while preserving crucial signal features for accurate analysis.

Keywords:
ECG signalautocorrelation functionlayered threshold functionoptical fiber sensorwavelet transform

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Wearable Technology

Background:

  • Physiological signals like ECG are prone to various artifacts.
  • Accurate signal denoising is crucial for reliable diagnosis and monitoring.
  • Existing denoising methods may struggle with complex noise types and preserving signal morphology.

Purpose of the Study:

  • To propose an adaptive layer-dependent threshold function (ALDTF) for denoising ECG and optical fiber-based cardiopulmonary signals.
  • To enhance the performance of physiological signal denoising compared to existing techniques.
  • To ensure the preservation of clinically relevant signal features.

Main Methods:

  • Wavelet transform-based denoising.
  • Adaptive threshold determination using the non-zero periodic peak (NZOPP) of the normalized autocorrelation function.
  • Layer-dependent soft and hard thresholding strategies for different frequency noise components.

Main Results:

  • ALDTF significantly outperforms standard and hybrid denoising methods (SWT, DTCWT) on ECG signals with various artifacts (BW, EM, MA, MIX).
  • Achieved substantial improvements in Signal-to-Noise Ratio (ΔSNR: 1.68–10.00 dB) and Signal-to-Noise-and-Distortion Ratio (ΔSINAD: 1.68–9.98 dB).
  • Demonstrated significant reductions in Root Mean Square Error (RMSE: 0.02–0.56) and Percentage Residual Distortion (PRD: 2.88–183.29%).
  • Successfully preserved diagnostic features (QRS complexes, ST segments) in real ECG and cardiopulmonary signals.

Conclusions:

  • ALDTF offers an efficient and versatile solution for physiological signal denoising.
  • The method effectively suppresses artifacts while maintaining signal integrity.
  • ALDTF shows strong potential for application in real-time wearable monitoring systems.