[Onset detection of surface diaphragmatic electromyography based on sample entropy and individualized threshold]

Area of Science:

• Biomedical Engineering
• Physiology
• Signal Processing

Context:

• Surface diaphragmatic electromyography (sEMGdi) is crucial for respiratory rehabilitation in hemiparetic stroke patients.
• Detecting the onset of sEMGdi is challenging due to interference from electrocardiography (ECG).
• Existing methods struggle with accuracy and individual variability in sEMGdi onset detection.

Purpose:

• To propose and evaluate a novel Sample Entropy (SampEn) based method for accurate sEMGdi onset detection.
• To optimize SampEn parameters (w, r0) and establish individualized thresholds for robust detection.
• To compare the performance of the SampEn method against traditional techniques like RMS and TKE, with and without Wavelet Transform (WT).

Summary:

• The study introduces a SampEn method for sEMGdi onset detection, optimizing parameters and using individualized thresholds.
• The SampEn method demonstrated superior and more stable detection precision compared to RMS-WT, TKE-WT, and TKE methods.
• Performance was evaluated using the cumulative sum of the absolute error (τ) on sEMGdi signals from 12 healthy subjects.

Impact:

• The SampEn method offers high detection accuracy without the need for ECG denoising, adapting to individual differences.
• This provides a reliable foundation for sEMGdi-based respiratory rehabilitation and real-time interactive training systems.
• Enables more effective and personalized rehabilitation strategies for stroke survivors.