Electrocardiogram Heart Rate Variability Signal Analysis and Data Acquisition:A Novel Approach for Sympathovagal Inference

This research delves into the complexities of Heart Rate Variability (HRV), a noninvasive procedure used for the assessment of cardiac autonomic functions. We utilized the advanced BIOPAC Data Acquisition (DAQ) system to meticulously gather Electrocardiogram (ECG) readings from a sole participant, exploring both sitting and supine positions and mindful of potential discrepancies during the initial recording period. Subsequently, the amassed data was critically processed using BIOPAC Student Lab 4.1 and KUBIOS software, resulting in a wide array of HRV as a cardiac autonomic function marker. The comprehensive set of indices included indices of the parasympathetic and sympathetic nervous systems (PNS and SNS), RR Time Series, carefully Detrended RR Series, time-domain metrics, nonlinear outputs, and frequency-domain measurements, all graphically encapsulated within a Poincare plot and Detrended Fluctuations Analysis (DFA). Furthermore, an in-depth examination of HRV parameters during peak activity intervals (from 296.000 to 298.000 seconds) was conducted, resulting in histogram and waterfall visualizations for both postural conditions. This meticulous investigation underscored significant discrepancies in HRV metrics based on body position, stressing the vital role of posture during HRV analysis. This study furnishes a baseline reference for future exploration of HRV in healthy individuals and signifies potential clinical applications for detecting ANS dysfunctions in a variety of diseases.