Extracting Features from Poincare Plots to Distinguish Congestive Heart Failure Patients According to NYHA Classes.

ABSTRACT: Heart-rate variability has proved a valid tool in prognosis definition of patients with congestive heart failure (CHF). Previous research has documented Poincaré plot analysis as a valuable approach to study heart-rate variability performance among different subjects. 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. We performed in sequence the following investigations: first, a statistical analysis was carried out on 9 morphological parameters, automatically measured from Poincaré plots. Subsequently, a feature selection through a wrapp

SUBMITTER: D'Addio G 

PROVIDER: S-EPMC8533203 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature