A cascaded CNN-LSTM framework for quantifying respiratory motion from surface electromyographic signals

Area of Science:

• Biomedical Engineering
• Signal Processing
• Machine Learning

Background:

• Diaphragm surface electromyographic (sEMG) signals are crucial for real-time respiratory monitoring in clinical settings like radiotherapy and intensive care.
• Electrocardiographic (ECG) interference commonly contaminates sEMG signals, hindering accurate respiratory motion estimation.
• Existing methods for ECG artifact removal often introduce delays and rely on linear assumptions, limiting real-time clinical applicability.

Purpose of the Study:

• To develop a robust deep learning framework for real-time respiratory motion quantification from sEMG signals.
• To achieve high-fidelity artifact suppression and accurate estimation of respiratory motion.
• To overcome the limitations of traditional signal processing techniques in clinical respiratory monitoring.

Main Methods:

• A cascaded deep learning framework was proposed, integrating a CNN-LSTM model for respiratory sEMG component isolation and a multi-scale CNN for nonlinear feature abstraction.
• sEMG and respiratory data were collected from 45 subjects (20 for training, 25 for validation).
• Cross-correlation analysis was used to evaluate the correlation between sEMG-derived respiration and reference signals.

Main Results:

• The proposed deep learning method achieved a superior correlation coefficient (Pearson's r = 0.949 ± 0.030) with abdominal pressure-derived respiration compared to gating (0.910 ± 0.046) and template subtraction (0.859 ± 0.081).
• The method demonstrated significantly higher correlation with reference signals even without post-processing, highlighting its real-time artifact suppression capabilities.
• This indicates robust performance in accurately quantifying respiratory motion from contaminated sEMG signals.

Conclusions:

• The developed deep learning framework offers an efficient solution for high-fidelity artifact suppression in sEMG signals.
• It enables accurate and real-time respiratory monitoring, crucial for clinical applications.
• This approach advances the field of physiological signal processing for improved patient care and monitoring.