Key result
Heart rate variability analysis using time and frequency domain parameters achieved classification accuracies of 84.61% for normal rhythm and 84.21% for ST change, but performed poorly for CUVT (50%) and MVA (62.5%).
Observational (n=66)
Linear and nonlinear HRV parameters can effectively classify normal sinus rhythm and ST changes, while Poincare plot analysis may be needed to improve detection of complex ventricular arrhythmias.
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HRV analysis may aid detection of normal rhythm and ST changes; leaves open reliable use for complex arrhythmias pending validation.
Heart rate variability (HRV) refers to the beat-to-beat alterations in heart rate. HRV analysis is based on the concept that, fast fluctuations reflect the changes of sympathetic and vagal activity which results in variability of intervals between R waves i.e. "RR intervals". In the current work HRV was assessed by traditional linear time and frequency-domain indexes, in parallel with the non linear Poincare plot analysis.
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Thalange et al. (2010) conducted an observational in Arrhythmias (n=66). Heart rate variability (HRV) parameter analysis vs. Normal sinus rhythm was evaluated on Classification accuracy of arrhythmias. Heart rate variability analysis using time and frequency domain parameters achieved classification accuracies of 84.61% for normal rhythm and 84.21% for ST change, but performed poorly for CUVT (50%) and MVA (62.5%).
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