Key result
HRV and Shannon entropy differentiate heart rhythms, showing increasing complexity from sinus rhythm to AF.
Observational
Mathematical complexity metrics of ECG signals can effectively differentiate between normal and abnormal cardiac rhythms, with atrial fibrillation showing the highest complexity.
ECG complexity metrics may refine automated rhythm detection; leaves open incremental clinical value beyond standard ECG interpretation.
In this study, we explore diverse rhythms of heart activity through the lens of complexity, shedding light on the intricate dynamics of heart rate variability and the amplitude of ECG signals. Metrics such as heart rate variability, Shannon entropy, and the quantities realted to Poincar? section distinctly differentiate between bradycardia, normal rhythm, and tachycardia. Our findings indicate an increase in complexity, progressing from ECGs with sinus rhythm, through those with atrial flutter, to ECGs exhibiting atrial fibrillation.
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Kršić et al. (2025) conducted an observational in Arrhythmias. Complexity metrics (HRV, Shannon entropy, Poincaré section) was evaluated on Differentiation of heart rhythms based on complexity. Complexity metrics, including Shannon entropy and heart rate variability, distinctly differentiate heart rhythms, demonstrating increasing complexity from sinus rhythm to atrial fibrillation.
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