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
BackgroundHeart rate variability (HRV) is a validated biomarker of autonomic and inflammatory regulation and has been associated with both acute COVID-19 and post–COVID-19 condition. Although reverse transcription polymerase chain reaction remains the diagnostic gold standard...
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JMIR Publications
2025-08-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2025/1/e76613 |
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| author | Carlos Alberto Sanches Andre Felipe Henriques Librantz Luciana Maria Malosá Sampaio Peterson Adriano Belan |
| author_facet | Carlos Alberto Sanches Andre Felipe Henriques Librantz Luciana Maria Malosá Sampaio Peterson Adriano Belan |
| author_sort | Carlos Alberto Sanches |
| collection | DOAJ |
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BackgroundHeart rate variability (HRV) is a validated biomarker of autonomic and inflammatory regulation and has been associated with both acute COVID-19 and post–COVID-19 condition. Although reverse transcription polymerase chain reaction remains the diagnostic gold standard for acute infection, there is a lack of accessible, noninvasive physiological tools to support ongoing monitoring and stage differentiation of COVID-19 and its sequelae. The growing availability of wearable devices capable of real-time HRV data collection opens up opportunities for early detection and health status classification using machine learning.
ObjectiveThis study aimed to identify HRV patterns capable of distinguishing individuals with active COVID-19 and post–COVID-19 condition and healthy controls using data collected from wearable devices and processed using machine learning models. A secondary objective was to assess the feasibility of a near–real-time health monitoring system based on these patterns using wearable-derived HRV data.
MethodsHRV indexes (SD of the normal-to-normal intervals [SDNN], root mean square of successive differences [RMSSD], low-frequency relative power [LF%], and high-frequency relative power [HF%]) were collected from 61 participants (n=21, 34% with active COVID-19; n=20, 33% with post–COVID-19 condition; and n=20, 33% healthy controls) using 2 standardized datasets. Classification models were developed using supervised machine learning algorithms (decision tree, support vector machines, k-nearest neighbor, and neural networks) and evaluated through cross-validation. A contextual clinical variable indicating recent SARS-CoV-2 infection was incorporated into 1 model configuration to assess its impact on classification performance. In addition, a prototype system for near–real-time monitoring was implemented and tested in a separate group of 4 participants.
ResultsParticipants with active COVID-19 showed significantly lower HRV indexes (SDNN, RMSSD, LF%, and HF%) compared to both participants with post–COVID-19 condition and healthy controls (P<.001), whereas differences between the post–COVID-19 condition and healthy groups were not statistically significant. Decision tree models trained solely on HRV features achieved 76.4% accuracy with high discriminative performance for active COVID-19 (F1-score=88%; area under the curve=0.85) but limited detection of post–COVID-19 condition (F1-score=56%). When a contextual clinical variable indicating recent SARS-CoV-2 infection was included, overall accuracy increased to 87%, and the F1-score for post–COVID-19 condition rose to 92%, with improved area under the curve metrics across classes. A prototype system tested on 4 independent participants correctly classified their status, demonstrating feasibility for near–real-time application.
ConclusionsHRV patterns collected from wearable devices and analyzed via machine learning successfully distinguished individuals with active COVID-19 from healthy individuals with high accuracy using physiological data alone. When a clinical contextual variable indicating recent infection was added, the model also achieved strong performance in identifying post–COVID-19 condition cases. A prototype system demonstrated feasibility for near–real-time application, reinforcing the potential of HRV for individualized health monitoring. |
| format | Article |
| id | doaj-art-b115b8ccb5b94fd2bf2f8856e1be8afd |
| institution | Kabale University |
| issn | 1438-8871 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | JMIR Publications |
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| series | Journal of Medical Internet Research |
| spelling | doaj-art-b115b8ccb5b94fd2bf2f8856e1be8afd2025-08-20T03:37:02ZengJMIR PublicationsJournal of Medical Internet Research1438-88712025-08-0127e7661310.2196/76613Classification 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 ComponentCarlos Alberto Sancheshttps://orcid.org/0000-0002-3609-7575Andre Felipe Henriques Librantzhttps://orcid.org/0000-0001-8599-9009Luciana Maria Malosá Sampaiohttps://orcid.org/0000-0002-0110-7710Peterson Adriano Belanhttps://orcid.org/0000-0001-9529-1637 BackgroundHeart rate variability (HRV) is a validated biomarker of autonomic and inflammatory regulation and has been associated with both acute COVID-19 and post–COVID-19 condition. Although reverse transcription polymerase chain reaction remains the diagnostic gold standard for acute infection, there is a lack of accessible, noninvasive physiological tools to support ongoing monitoring and stage differentiation of COVID-19 and its sequelae. The growing availability of wearable devices capable of real-time HRV data collection opens up opportunities for early detection and health status classification using machine learning. ObjectiveThis study aimed to identify HRV patterns capable of distinguishing individuals with active COVID-19 and post–COVID-19 condition and healthy controls using data collected from wearable devices and processed using machine learning models. A secondary objective was to assess the feasibility of a near–real-time health monitoring system based on these patterns using wearable-derived HRV data. MethodsHRV indexes (SD of the normal-to-normal intervals [SDNN], root mean square of successive differences [RMSSD], low-frequency relative power [LF%], and high-frequency relative power [HF%]) were collected from 61 participants (n=21, 34% with active COVID-19; n=20, 33% with post–COVID-19 condition; and n=20, 33% healthy controls) using 2 standardized datasets. Classification models were developed using supervised machine learning algorithms (decision tree, support vector machines, k-nearest neighbor, and neural networks) and evaluated through cross-validation. A contextual clinical variable indicating recent SARS-CoV-2 infection was incorporated into 1 model configuration to assess its impact on classification performance. In addition, a prototype system for near–real-time monitoring was implemented and tested in a separate group of 4 participants. ResultsParticipants with active COVID-19 showed significantly lower HRV indexes (SDNN, RMSSD, LF%, and HF%) compared to both participants with post–COVID-19 condition and healthy controls (P<.001), whereas differences between the post–COVID-19 condition and healthy groups were not statistically significant. Decision tree models trained solely on HRV features achieved 76.4% accuracy with high discriminative performance for active COVID-19 (F1-score=88%; area under the curve=0.85) but limited detection of post–COVID-19 condition (F1-score=56%). When a contextual clinical variable indicating recent SARS-CoV-2 infection was included, overall accuracy increased to 87%, and the F1-score for post–COVID-19 condition rose to 92%, with improved area under the curve metrics across classes. A prototype system tested on 4 independent participants correctly classified their status, demonstrating feasibility for near–real-time application. ConclusionsHRV patterns collected from wearable devices and analyzed via machine learning successfully distinguished individuals with active COVID-19 from healthy individuals with high accuracy using physiological data alone. When a clinical contextual variable indicating recent infection was added, the model also achieved strong performance in identifying post–COVID-19 condition cases. A prototype system demonstrated feasibility for near–real-time application, reinforcing the potential of HRV for individualized health monitoring.https://www.jmir.org/2025/1/e76613 |
| spellingShingle | Carlos Alberto Sanches Andre Felipe Henriques Librantz Luciana Maria Malosá Sampaio Peterson Adriano Belan 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 Journal of Medical Internet Research |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | https://www.jmir.org/2025/1/e76613 |
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