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Related Concept Videos

Instrumentation Amplifier01:25

Instrumentation Amplifier

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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Denoising Non-Invasive Electroespinography Signals by Different Cardiac Artifact Removal Algorithms.

Desirée I Gracia1,2, Eduardo Iáñez1,2, Mario Ortiz1,2

  • 1Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, 03202 Elche, Spain.

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|February 26, 2026
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Summary
This summary is machine-generated.

Adaptive Template Subtraction (ATS) effectively removes electrocardiographic (ECG) artifacts from electrospinography (ESG) recordings. This method offers the best balance between artifact reduction and signal integrity for emerging biosensing applications.

Keywords:
adaptive template subtraction (ATS)denoising algorithmelectrocardiographic (ECG) artifactelectrospinography (ESG)

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Signal Processing

Background:

  • Non-invasive spinal cord neuronal activity recording via electrospinography (ESG) using high-density surface electromyography (HD-sEMG) shows promise.
  • Electrocardiographic (ECG) artifacts frequently contaminate ESG signals, necessitating effective denoising techniques.

Purpose of the Study:

  • To evaluate seven established electromyography (EMG) denoising algorithms for their efficacy in removing ECG artifacts from ESG data.
  • To assess the algorithms' ability to preserve the broad spectral bandwidth crucial for ESG analysis.

Main Methods:

  • Seven denoising algorithms were tested: Template Subtraction (TS), Adaptive Template Subtraction (ATS), High-Pass Filtering at 200 Hz (HP200), ATS+HP200, Second-Order Extended Kalman Smoother (EKS2), Stationary Wavelet Transform (SWT), and Empirical Mode Decomposition (EMD).
  • Performance was quantified using Relative Error (RE), Signal-to-Noise Ratio (SNR), Cross-Correlation (CC), Spectral Distortion (SD), Kurtosis Ratio (KR2), and ΔKR2.
  • ESG data were collected from nine participants at brachial and lumbar plexus sites with varying electrode configurations.

Main Results:

  • Adaptive Template Subtraction (ATS) demonstrated superior performance in suppressing diverse ECG artifact shapes compared to all other methods.
  • ATS achieved the optimal balance between artifact removal and signal integrity, despite not fully preserving low-frequency components.
  • Algorithm performance was enhanced in recordings with lower ECG contamination, particularly brachial plexus recordings with closely spaced reference electrodes.

Conclusions:

  • ATS is the most effective algorithm for removing ECG artifacts in ESG recordings, providing a good balance between artifact suppression and signal preservation.
  • The effectiveness of denoising algorithms is influenced by the level of ECG contamination and electrode configuration.
  • Further research into ESG signal processing is warranted, with ATS serving as a strong baseline method.