A Multimodal Time Point Labeling Approach for Analyzing Mastication and Swallowing Dynamics.

Area of Science:

• Biomedical Engineering
• Physiology
• Signal Processing

Background:

• Mastication and swallowing are complex physiological processes.
• Existing detection methods lack comprehensive information and temporal analysis.
• Accurate analysis of these movements is crucial for understanding oral functions.

Purpose of the Study:

• To propose a novel method for analyzing masticatory and swallowing movements.
• To integrate maxillofacial electromyographic (EMG) signals with laryngeal movement signals.
• To explore the temporal state analysis of these physiological processes.

Main Methods:

• Acquired and processed maxillofacial EMG signals using EMG electrodes.
• Utilized flexible PVDF piezoelectric sensors for laryngeal movement signal acquisition.
• Employed a sliding-window-based dispersion calculation for time-point labeling and feature extraction.

Main Results:

• The integrated signals provided comprehensive detection of masticatory and swallowing movements.
• Temporal features from both signal types were successfully correlated with physiological states.
• PVDF sensor demonstrated high linearity (R²=0.99446), good pressure resolution (0.08 kPa), and fast response times (<10 ms).

Conclusions:

• The proposed method effectively integrates EMG and laryngeal movement signals.
• This approach enables accurate temporal parameter extraction for oral movements.
• The findings suggest feasibility for analyzing masticatory and swallowing dynamics in healthy individuals.