We performed in sequence the following investigations: first, a statistical analysis was carried out on 9 morphological parameters, automatically measured from Poincaré plots. In this paper, we explored the possibility to feed machine-learning (ML) algorithms using unconventional quantitative parameters extracted from Poincaré plots (generated from 24-h electrocardiogram recordings) to classify patients with CHF belonging to different New York Heart Association (NYHA) classes. Previous research has documented Poincaré plot analysis as a valuable approach to study heart-rate variability performance among different subjects. Heart-rate variability has proved a valid tool in prognosis definition of patients with congestive heart failure (CHF).
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