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Related Experiment Video

Updated: Sep 13, 2025

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

Neural Signals-Based Respiratory Motion Tracking: A Surface Electromyography Study.

Xiangbin Zhang¹, Yihan Huang¹, Yihan Wu²

¹Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.

International Journal of Radiation Oncology, Biology, Physics

|August 2, 2025

Summary

This summary is machine-generated.

Surface electromyography (sEMG) enables real-time respiratory motion tracking for radiation therapy by decoding neural signals. This method offers a promising solution to system latency issues in medical linear accelerators.

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Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Area of Science:

Biomedical Engineering
Neuroscience
Medical Physics

Background:

Respiratory motion tracking is crucial for radiation therapy accuracy.
System latency in current methods limits precision.
Neural signals offer a potential alternative for real-time tracking.

Purpose of the Study:

To develop and validate a clinically applicable respiratory motion tracking method using surface electromyography (sEMG).
To address the challenge of real-time decoding of respiratory-related neural signals.
To overcome system latency issues in medical linear accelerators.

Main Methods:

Simultaneous recording of neural signals (sEMG) and respiratory motion in 15 healthy subjects.
Cross-correlation analysis to determine time dependencies between neural signals and motion.
Development of an online analysis framework using recurrent neural networks for real-time prediction.

Main Results:

High correlation (r > 0.90) between offline-extracted neural signals and respiratory motion with an average precursor time of 319 ms.
Stable precursor time observed across different recording intervals.
Online analysis achieved low mean absolute error (0.075 ± 0.021) and root mean square error (0.098 ± 0.028).

Conclusions:

A clinically applicable sEMG-based respiratory motion tracking method has been developed.
The online analysis method achieves high accuracy with minimal latency.
sEMG-based neural signal tracking is a promising solution for medical linear accelerator system latency in cancer radiation therapy.