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This paper is in the following e-collection/theme issue:

New Methods (1826) Wearable Devices and Sensors (1619) Machine Learning (3272) Novel Sensors and Data Acquisition Methods in Cardiology (267) Artificial Intelligence (4894) Outbreak and Pandemic Preparedness and Management (1973)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/76613, first published .
Man checking his smartwatch displaying a heart rate monitor and activity tracker

Classification of Individuals With COVID-19 and Post–COVID-19 Condition and Healthy Controls Using Heart Rate Variability: Machine Learning Study With a Near–Real-Time Monitoring Component

Classification of Individuals With COVID-19 and Post–COVID-19 Condition and Healthy Controls Using Heart Rate Variability: Machine Learning Study With a Near–Real-Time Monitoring Component

Authors of this article:

Carlos Alberto Sanches1 Author Orcid Image ;   Andre Felipe Henriques Librantz1 Author Orcid Image ;   Luciana Maria Malosá Sampaio2 Author Orcid Image ;   Peterson Adriano Belan1 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Journals

  1. Park K, Park E, Park W, Jeon E. From Reactive to Proactive Healthcare: Synergizing Wearable Biomarkers and Machine Learning in Digital Therapeutics. Bioengineering 2026;13(9):977 View