What question did this study set out to answer?

The aim is to evaluate the effectiveness of various denoising algorithms in removing cardiac artifacts from electrospinography signals.

February 2, 2026Open Access

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

Key Result

Adaptive Template Subtraction (ATS) consistently outperformed other algorithms in suppressing cardiac artifacts while maintaining signal integrity in electrospinography recordings.

Key Points

The aim is to evaluate the effectiveness of various denoising algorithms in removing cardiac artifacts from electrospinography signals.
Assessed seven EMG denoising algorithms including Template Subtraction and Adaptive Template Subtraction.
Recorded ESG data from nine healthy individuals at brachial and lumbar plexus sites.
Performance was measured using metrics like Signal-to-Noise Ratio and Cross-Correlation.
Adaptive Template Subtraction showed the best performance in reducing cardiac artifacts.
Other methods did not achieve the same balance between artifact removal and signal quality.
Algorithm effectiveness improved with lower ECG contamination, particularly in brachial recordings.

Structured PICO

Which denoising algorithm best removes electrocardiographic (ECG) artifacts from non-invasive electrospinography (ESG) signals in healthy participants?

Population

9 healthy participants with ESG data recorded at brachial and lumbar plexus sites with various electrode configurations

Intervention

Seven EMG denoising algorithms: Template Subtraction (TS), Adaptive Template Subtraction (ATS), High-Pass Filtering at 200 Hz (HP200), ATS combined with HP200, Second-Order Extended Kalman Smoother (EKS2), Stationary Wavelet Transform (SWT), and Empirical Mode Decomposition (EMD)

Comparator

Comparison among the seven algorithms

Outcome

Algorithm performance quantified by Relative Error (RE), Signal-to-Noise Ratio (SNR), Cross-Correlation (CC), Spectral Distortion (SD), and Kurtosis Ratio (KR2) and its variation (ΔKR2)surrogate

Adaptive Template Subtraction (ATS) is the most effective algorithm for removing ECG artifacts from electrospinography signals while preserving signal integrity.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

The non-invasive recording of spinal cord neuronal activity, also known as electrospinography (ESG), using high-density surface electromyography (HD-sEMG) is a promising emerging biosensing modality. However, these recordings often contain electrocardiographic (ECG) artifacts that must be removed for accurate analysis. Given the emerging nature of ESG and the lack of dedicated signal processing methods, this study assesses the performance of seven established EMG denoising algorithms for their ability to preserve the broad spectral bandwidth needed for future ESG characterization: Template Subtraction (TS), Adaptive Template Subtraction (ATS), High-Pass Filtering at 200 Hz (HP200), ATS combined with HP200, Second-Order Extended Kalman Smoother (EKS2), Stationary Wavelet Transform (SWT), and Empirical Mode Decomposition (EMD). Performance was quantified using six metrics: Relative Error (RE), Signal-to-Noise Ratio (SNR), Cross-Correlation (CC), Spectral Distortion (SD), and Kurtosis Ratio (KR2) and its variation (ΔKR2). ESG data were recorded from nine healthy participants at brachial and lumbar plexus sites with various electrode configurations. ATS consistently outperformed all other methods in suppressing cardiac artifacts of varying shapes. Although it did not fully preserve low-frequency content, ATS achieved the best balance between artifact removal and signal integrity. Algorithm performance improved when ECG contamination was lower, especially in brachial plexus recordings with closer reference electrodes.

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