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  1. Home
  3. Real-time Ecg Artifact Removal For Adaptive Deep Brain Stimulation: A Comparative Study.
  1. Home
  3. Real-time Ecg Artifact Removal For Adaptive Deep Brain Stimulation: A Comparative Study.

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Real-Time ECG Artifact Removal for Adaptive Deep Brain Stimulation: A Comparative Study.

Lucrezia Silvi1, Valentina D'Onofrio2, Simone Cauzzo1,2

  • 1Biomedical Engineering Research to Advance and Innovate Translational Neuroscience (BRAIN Unit), Department of Neuroscience, University of Padova, 35122 Padua, Italy.

Sensors (Basel, Switzerland)
|May 13, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Template subtraction (TS) is the best method for removing electrocardiographic (ECG) artifacts in real-time adaptive deep brain stimulation (aDBS) for Parkinson's disease (PD). It balances computational efficiency and biomarker accuracy for closed-loop control.

Keywords:
ECG artifactParkinson’s diseaseadaptive deep brain stimulationlocal field potentialsreal-time signal processing

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Adaptive deep brain stimulation (aDBS) for Parkinson's disease (PD) requires precise detection of beta oscillatory activity.
  • Electrocardiographic (ECG) artifacts commonly contaminate local field potentials (LFPs), hindering the performance of control algorithms.
  • The real-time feasibility of existing offline ECG artifact removal methods for aDBS is not well-established.

Purpose of the Study:

  • To evaluate the performance of four ECG removal algorithms for real-time aDBS applications.
  • To assess artifact removal quality and real-time processing feasibility of different algorithms.

Main Methods:

  • Four ECG removal algorithms were tested: template subtraction (TS), singular value decomposition (SVD), extended SVD (eSVD), and Perceive toolbox (PR).
  • Simulated datasets with varying contamination levels (-30 to +20 dB) and clinical recordings from 20 PD patients were used.
  • Performance metrics included beta power preservation, signal-to-noise ratio, and 99th percentile processing latency (P99 < 50 ms for real-time feasibility).

    • Main Results:

    • Template subtraction (TS) and standard SVD met the real-time feasibility threshold (P99 < 50 ms).
    • TS demonstrated superior timing consistency (P99 ≈ 10 ms), while eSVD and PR exceeded the latency threshold (P99 > 90 ms).
    • TS maintained beta power accuracy within ±12% across contamination levels, offering the best balance between artifact suppression and signal preservation.

    Conclusions:

    • Template subtraction (TS) is the recommended algorithm for real-time ECG artifact removal in aDBS for Parkinson's disease.
    • TS provides a critical balance of computational efficiency and biomarker fidelity necessary for closed-loop control systems.
    • This method ensures reliable detection of beta oscillatory activity essential for effective aDBS therapy.