The autonomic-ventricular coherence index: a novel single-lead ECG method with acquisition-dependent discrimination.
- 1School of Computer Science Engineering and Information Systems, Vellore Institute of Technology Deemed to be University, Vellore, India.
Abstract
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. AVCI was developed and evaluated on 74 recordings from three PhysioNet databases (MIT-BIH Normal Sinus Rhythm, n = 18; BIDMC Congestive Heart Failure, n = 15; and MIT-BIH Arrhythmia, n = 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. 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. 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.
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BibTeX
@article{jayasree2026autonomicventricular,
title = {{The autonomic-ventricular coherence index: a novel single-lead ECG method with acquisition-dependent discrimination.}},
author = {Jayasree R and Chemmalar Selvi G},
journal = {Frontiers in digital health},
year = {2026},
month = oct,
doi = {10.3389/fdgth.2026.1964571},
url = {https://europepmc.org/article/MED/42952703},
note = {CC BY},
}
RIS
TY - JOUR
TI - The autonomic-ventricular coherence index: a novel single-lead ECG method with acquisition-dependent discrimination.
AU - Jayasree R
AU - Chemmalar Selvi G
PY - 2026
DA - 2026/10/14
JO - Frontiers in digital health
DO - 10.3389/fdgth.2026.1964571
UR - https://europepmc.org/article/MED/42952703
AB - 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. AVCI was developed and evaluated on 74 recordings from three PhysioNet databases (MIT-BIH Normal Sinus Rhythm, n = 18; BIDMC Congestive Heart Failure, n = 15; and MIT-BIH Arrhythmia, n = 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. 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. 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.
SN - 2673-253X
ER -