Work

The autonomic-ventricular coherence index: a novel single-lead ECG method with acquisition-dependent discrimination

Year: 2026

Type: article

Abstract: Background: Autonomic regulation and the heart's electrical activity are both central to normal cardiac function, yet there is currently no direct way to capture the interaction between them from a standard ECG. This paper introduces the Autonomic-Ventricular Coherence Index (AVCI), a single-lead ECG method that measures the beat-to-beat spectral coherence between variability in RR intervals and variability in QRS area. Methods: = 41), with a further six recordings from the MIT-BIH Long-Term ECG Database used for preliminary independent validation. Coherence was integrated over the high-frequency band (0.15-0.40 Hz). Because the source databases differ in sampling rate and recording length, AVCI was examined both under each database's native recording conditions and after acquisition standardization, harmonizing all recordings to a common 128 Hz and 30-minute duration, additionally upsampling to 360 Hz for 30 min under uniform acquisition, and applying a two-tier duration-stratified framework. The analysis code is available at https://github.com/JayaSreejai/AVCI.git. Results: Under native conditions, AVCI demonstrated a high capacity to distinguish between congestive heart failure and normal sinus rhythm (AUC 0.844). Like SDNN, it performed moderately in harmonized acquisition (NSR vs. CHF AUC 0.647; NSR vs. all-disease AUC 0.619). Under native conditions, machine learning models trained on balanced datasets performed well (KNN accuracy 79.5%, ROC-AUC 85.7, SVM accuracy 79.0%, ROC-AUC 83.0), whereas under harmonized conditions, they produced average results. R-peak identification was consistent across QRS windows and accurate (F1 0.892). These results show that AVCI's diagnostic performance depends on acquisition parameters, particularly sampling rate and recording duration. Conclusions: AVCI is a practical and understandable measure of autonomic-ventricular coupling from a single ECG lead. Combined with machine learning, it offers a promising path toward more robust and explainable cardiovascular diagnostics. Standardized acquisition protocols are key to reliable clinical use.

Source: Frontiers in Digital Health

DOI: 10.3389/fdgth.2026.1964571

Authors: Jayasree R, Chemmalar Selvi G

Institution: Vellore Institute of Technology University

Language: English

Topic: Heart Rate Variability and Autonomic Control

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,077