Complexity-Based Analysis of Cardiac-Facial Muscle Coupling During Controlled Respiration

This study investigates how different controlled respiration patterns influence the coordinated dynamics of facial muscle activity and cardiac function by analyzing electromyography (EMG) and electrocardiography (ECG) signals. Twenty-six healthy participants performed four respiration tasks with different controlled respiration conditions, while facial EMG and ECG signals were continuously recorded. To characterize the temporal structure of these physiological signals, three nonlinear complexity metrics — fractal dimension (FD), sample entropy (SampEn), and approximate entropy (ApEn) — were computed from EMG signals and heart rate variability (HRV) derived from ECG. The results revealed that the complexity of both EMG and HRV signals increased as the difficulty of the respiration tasks increased, indicating more irregular and information-rich physiological dynamics under more demanding breathing patterns. In addition, strong positive correlations were observed between the complexity variations of facial EMG and HRV across the respiration conditions, suggesting coordinated adaptation between somatic and autonomic physiological systems during controlled breathing. These findings demonstrate that nonlinear complexity measures provide sensitive indicators of cardio-somatic responses to respiration modulation and highlight the potential of combined EMG–ECG complexity analysis for understanding physiological regulation during breathing tasks and related psychophysiological states.