The Influence of Body Posture on Cardiac Autonomic Regulation and Electrocardiogram (ECG) Signal Quality: A Heart Rate Variability and Shannon Entropy Analysis

Haya Noor; Nayyar Ijaz Dar; Atiqa Saleem; Alina Sabeen Khalid; Laiba Emaan

doi:10.59890/ijsas.v3i12.210

Authors

Haya Noor Department of Biomedical Engineering, Hitec University
Nayyar Ijaz Dar College of Materials Science and Engineering, Hohai University
Atiqa Saleem Department of Biomedical Engineering, Hitec University
Alina Sabeen Khalid Department of Biomedical Engineering, Hitec University
Laiba Emaan Department of Biomedical Engineering, Hitec University

DOI:

https://doi.org/10.59890/ijsas.v3i12.210

Keywords:

ECG Analysis, Heart Rate Variability, Posture Recognition, R-Peak Detection, Shannon Entropy, Signal Quality Index

Abstract

Accurate assessment of cardiac autonomic regulation is essential for understanding how physiological states adapt to postural changes. Body posture significantly influences electrocardiogram (ECG) dynamics, affecting both heart rate variability (HRV) and signal integrity. This study investigates the effect of body posture on ECG-derived heart rate variability (HRV) and signal quality, using three postures: sitting, standing, and supine. Time-domain HRV parameters (RMSSD, SDNN, mean RR) and signal quality index (SQI) based on Shannon entropy were extracted from ECG signals collected from healthy individuals. R-peak detection algorithms were used to calculate RR intervals and derive metrics. The results demonstrate a clear autonomic modulation: standing posture showed reduced HRV and higher entropy, indicating sympathetic dominance and increased signal noise. Supine posture exhibited increased HRV and lower entropy, reflecting enhanced parasympathetic activity and stable signal acquisition. Sitting presented intermediate characteristics. All analysis was performed using a reproducible MATLAB pipeline. This combination of HRV and entropy analysis provides a reliable, low-complexity approach for evaluating autonomic function and ECG signal quality across postures. The findings offer valuable insight into posture-aware health monitoring and form a basis for future expansion involving frequency-domain and nonlinear analyses.

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