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Rhythmometric analysis of heart rate variability indices during long term monitoring

Abstract:

Long-term monitoring of ECG signals is receiving much attention, still being an open issue how to deal with this massive source of information. In particular, Heart Rate Variability (HRV) indices have been widely used to characterize the state of the autonomous regulation of the heart from 24-hour Holter monitoring, but there is few knowledge on the long-term evolution of HRV indices. A data set of 7-day Holter recordings in 12 Congestive Heart Failure (CHF) patients was assembled. For its analysis, an automatic rhythmometric procedure was designed, allowing to characterize the ultradian and the infradian components, with possible inclusion of near-periodic fluctuations. A bootstrap hypothesis test allows us to systematically adjust the model architecture for each patient. The temporal evolution of relevant time-domain (AVNN, SDNN, NN50), frequency-domain (LF, HF, HFn, LF/HF), and nonlinear (α1, SampEn) HRV indices, was analyzed. Larger relative deviations from the daily average pattern were more clearly observed in nonlinear indices and in NN50. Infradian subharmonic was mostly present in NN50, AVNN, α1, and SampEn. Long-term monitoring of HRV conveys new relevant rhythmometric information that can be analyzed with the proposed automatic procedure.