ArticleEuropean journal of clinical investigation2026
From Electrical Morphology to Physiological Dynamics: Lessons From ECG Adoption for the Clinical Integration of Heart Rate Variability.
Article in European journal of clinical investigation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundHeart rate variability (HRV) has remained a relatively finite and niche tool in cardiology despite decades of research supporting its physiological and clinical relevance. This limited adoption may resemble the early history of electrocardiography (ECG), which was initially regarded by many physicians as a laboratory instrument rather than a routine clinical tool. The delayed acceptance of ECG reflected technological limitations, cultural resistance and the need for clinicians to master unfamiliar concepts derived from physics and electrophysiology. HRV faces comparable barriers today. Although derived from ECG RR intervals, HRV requires interpretation of time-domain, frequency-domain, geometric and nonlinear indices that may appear mathematically complex and distant from conventional bedside reasoning.
aimsWe argue that HRV should not be viewed as a replacement for ECG, but as an extension of ECG from electrical morphology to physiological dynamics. MATERIALS AND
methodsLessons from ECG history were examined and compared with the current state of HRV adoption in clinical practice. The complementary diagnostic roles of ECG morphology and HRV analysis were considered, together with the potential contribution of wearable sensors, remote monitoring, artificial intelligence and large language models to facilitate HRV interpretation, education and clinical integration.
resultsWhereas conventional ECG morphology identifies arrhythmias, conduction disturbances, ischemic alterations and overt electrical abnormalities, HRV provides insight into autonomic modulation, cardiovascular adaptability and systemic physiological regulation. The emergence of wearable sensors, remote monitoring, artificial intelligence and large language models creates an opportunity to overcome barriers that have limited HRV adoption. Artificial intelligence may serve as an educational and interpretive bridge, translating complex HRV metrics into clinically meaningful concepts while supporting medical training, artefact awareness, case-based learning and workflow integration. DISCUSSION: HRV faces barriers comparable to those encountered during the early adoption of ECG, including technological limitations, educational challenges and resistance to incorporating unfamiliar physiological concepts into routine clinical practice.
conclusionLessons from ECG history suggest that HRV adoption will depend not only on evidence but also on standardization, education, clinical interpretation and cultural acceptance within cardiology.
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