Why the study?
With population aging increasing chronic heart failure cases, the study aimed to implement a noncontact system to predict heart failure exacerbation through vocal analysis.
Does machine-learning-based vocal analysis accurately classify NYHA stage in patients hospitalized with acute pulmonary edema?
Population
16 hospitalized patients admitted with cardiogenic acute pulmonary edema
Comparison
Evaluation across different machine-learning algorithms (ANN, SVM, KNN) using voice recordings
Design
Proof-of-concept observational study
Follow-up
From day one of hospitalization until discharge
Authors
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Noncontact vocal analysis shows promise for remote HF monitoring; leaves open validation in larger prospective cohorts before clinical use.
Does machine-learning-based vocal analysis accurately classify NYHA stage in patients hospitalized with acute pulmonary edema?
Machine-learning analysis of voice recordings shows high preliminary accuracy in classifying heart failure severity, offering a potential non-contact monitoring tool.
Pană et al. (2021) studied this question.