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Oscoz-Ochandorena S, Legarra-Gorgoñon G, García-Alonso Y, García-Alonso N, Izquierdo M, Ramírez-Vélez R. Reduced autonomic function in patients with long-COVID-19 syndrome is mediated by cardiorespiratory fitness. Curr Probl Cardiol 2024; 49:102732. [PMID: 38960014 DOI: 10.1016/j.cpcardiol.2024.102732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Accepted: 06/26/2024] [Indexed: 07/05/2024]
Abstract
BACKGROUND Long-COVID-19 syndrome (LCS) exhibits neurological problems such as peripheral neuropathy and autonomic nervous system (ANS) dysfunction. Exercise intolerance and, consequently, low cardiorespiratory fitness (CRF) are some of the most common symptoms of LCS. We describe a series of individuals exhibiting LCS symptoms compared to a control group and posit that this condition may be related to the exercise capacity-mediated disruption of the ANS resulting particularly in exercise intolerance. METHODS This study included 87 individuals with LCS and 71 control participants without COVID-19 diagnoses. Heart rate variability (HRV) in supine position is commonly measured to diagnose autonomic dysregulation and subsequently analyzed using the Kubios software (Kuopio, Finland). CRF (peak VO2), post-COVID-19 patient-reported symptoms, maximal muscle strength (grip strength, bilateral leg press, leg extension, pectoral press, and back press exercises), and body composition were also measured. Analysis of covariance (ANCOVA) and mediation analysis were employed to assess the associations among LCS, peak VO2, and HRV indicators. Two-sided p < 0.05 was considered as significant. RESULTS The HRV parameters-RR interval, RMSSD, SDNN, PNS index, LF, HF, total power, SD1, and SD2-were significantly elevated (p < 0.05) in the control group when compared to the LCS patients. In contrast, the HR, stress index, and SNS index parameters were significantly higher (p < 0.05) in the LCS group. When adjusted for RR intervals, these parameters remained statistically significant (p < 0.05). A partially mediated effect was found between peak VO2 and RMSSD (mediation effect = 24.4%) as well as peak VO2 and SDNN (mediation effect = 25.1%) in the LCS patients. CONCLUSIONS These findings contribute new insights on the interplay between CRF and HRV indicators as well as endorse that dysautonomia may be related to the low peak VO2 observed in long COVID-19 patients.
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Affiliation(s)
- Sergio Oscoz-Ochandorena
- Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, España
| | - Gaizka Legarra-Gorgoñon
- Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, España
| | - Yesenia García-Alonso
- Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, España
| | - Nora García-Alonso
- Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, España
| | - Mikel Izquierdo
- Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, España; CIBER of Frailty and Healthy Aging (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
| | - Robinson Ramírez-Vélez
- Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, España; CIBER of Frailty and Healthy Aging (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain.
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Suh HW, Kwon CY, Lee B. Long-Term Impact of COVID-19 on Heart Rate Variability: A Systematic Review of Observational Studies. Healthcare (Basel) 2023; 11:healthcare11081095. [PMID: 37107929 PMCID: PMC10137929 DOI: 10.3390/healthcare11081095] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/07/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) sequelae (or long COVID) has become a clinically significant concern. Several studies have reported the relationship between heart rate variability (HRV) parameters and COVID-19. This review investigates the long-term association between COVID-19 and HRV parameters. Four electronic databases were searched up to 29 July 2022. We included observational studies comparing HRV parameters (measurement durations: 1 min or more) in participants with and without a history of COVID-19. We used assessment tools developed by the National Heart, Lung, and Blood Institute group to evaluate the methodological quality of included studies. Eleven cross-sectional studies compared HRV parameters in individuals who recovered from acute COVID-19 infection to controls (n = 2197). Most studies reported standard deviation of normal-to-normal intervals (SDNN) and root mean square of the successive differences. The methodological quality of the included studies was not optimal. The included studies generally found decreased SDNN and parasympathetic activity in post-COVID-19 individuals. Compared to controls, decreases in SDNN were observed in individuals who recovered from COVID-19 or had long COVID. Most of the included studies emphasized parasympathetic inhibition in post-COVID-19 conditions. Due to the methodological limitations of measuring HRV parameters, the findings should be further validated by robust prospective longitudinal studies.
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Affiliation(s)
- Hyo-Weon Suh
- Health Policy Research Team, Division of Healthcare Research, National Evidence-Based Healthcare Collaborating Agency, 400 Neungdong-ro, Gwangjin-gu, Seoul 04933, Republic of Korea
| | - Chan-Young Kwon
- Department of Oriental Neuropsychiatry, College of Korean Medicine, Dong-Eui University, Busan 47227, Republic of Korea
| | - Boram Lee
- KM Science Research Division, Korea Institute of Oriental Medicine, 1672 Yuseong-daero, Yuseong-gu, Daejeon 34054, Republic of Korea
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3
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Rahul LR, Sarkar R, Sengupta A, Chandra BS, Jana S. Novel AI-based HRV analysis (NAIHA) in healthcare automation and related applications. J Electrocardiol 2023; 79:112-121. [PMID: 37031632 DOI: 10.1016/j.jelectrocard.2023.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 02/16/2023] [Accepted: 03/19/2023] [Indexed: 03/30/2023]
Abstract
BACKGROUND Heart rate variability (HRV) analysis computed on R-R interval series of ECG records with heavy burden of ectopic beats or non-sinus rhythm can significantly distort HRV parameters and hence clinically ineligible for HRV analysis. Yet, existing algorithmic methods of HRV analysis do not check such eligibility and require manual identification of eligible window (portion of ECG record) to ensure reliability. OBJECTIVE We aimed to propose a robust algorithm with a sliding window feature to automate the identification of an eligible window, if available, which compute HRV parameters within that window obviating manual input. METHODS The proposed algorithm classifies each window as either eligible or ineligible. With a window classified eligible, we stop sliding through the record, otherwise we move to the next window and repeat the eligibility identification process, until either an eligible window is found, or all windows are exhausted. RESULTS When evaluated on random subset of 100 records from MIMIC-III waveform database, the proposed algorithm excluded every ineligible record, and missed only 1.25% of eligible ones. The HRV parameters computed using proposed method closely approximated the standard HRV analysis with Pearson correlation coefficients (ideally one) and fractions of variance unexplained (ideally zero) ranging from 96.3% to 99.8% and 0.34% to 7.43%, respectively. CONCLUSIONS When translated into practice, proposed algorithm will reduce clinicians'' burden without compromising the accuracy of HRV analysis, potentially leading to its wider adoption.
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Affiliation(s)
- L R Rahul
- Department of Electrical Engineering, Indian Institute of Technology, Hyderabad, India
| | - Rahuldeb Sarkar
- Department of Respiratory Medicine and Critical Care, Medway NHS Foundation Trust, London, UK; Faculty of Life Sciences, King's College, London, UK
| | - Arnab Sengupta
- Department of Physiology, Institute of Postgraduate Medical and Research, Kolkata, India
| | - B Sandeep Chandra
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, United States of America; Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Soumya Jana
- Department of Electrical Engineering, Indian Institute of Technology, Hyderabad, India.
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4
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Adang EAMC, Strous MTA, van den Bergh JP, Gach D, van Kampen VEM, van Zeeland REP, Barten DG, van Osch FHM. Association of Heart Rate Variability with Pulmonary Function Impairment and Symptomatology Post-COVID-19 Hospitalization. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23052473. [PMID: 36904676 PMCID: PMC10007596 DOI: 10.3390/s23052473] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/09/2023] [Accepted: 02/17/2023] [Indexed: 06/12/2023]
Abstract
The persistence of symptoms beyond three months after COVID-19 infection, often referred to as post-COVID-19 condition (PCC), is commonly experienced. It is hypothesized that PCC results from autonomic dysfunction with decreased vagal nerve activity, which can be indexed by low heart rate variability (HRV). The aim of this study was to assess the association of HRV upon admission with pulmonary function impairment and the number of reported symptoms beyond three months after initial hospitalization for COVID-19 between February and December 2020. Follow-up took place three to five months after discharge and included pulmonary function tests and the assessment of persistent symptoms. HRV analysis was performed on one 10 s electrocardiogram obtained upon admission. Analyses were performed using multivariable and multinomial logistic regression models. Among 171 patients who received follow-up, and with an electrocardiogram at admission, decreased diffusion capacity of the lung for carbon monoxide (DLCO) (41%) was most frequently found. After a median of 119 days (IQR 101-141), 81% of the participants reported at least one symptom. HRV was not associated with pulmonary function impairment or persistent symptoms three to five months after hospitalization for COVID-19.
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Affiliation(s)
- Estelle A. M. C. Adang
- Department of Emergency Medicine, VieCuri Medical Centre, 5912 BL Venlo, The Netherlands
| | - Maud T. A. Strous
- Department of Internal Medicine, VieCuri Medical Centre, 5912 BL Venlo, The Netherlands
| | - Joop P. van den Bergh
- Department of Internal Medicine, VieCuri Medical Centre, 5912 BL Venlo, The Netherlands
- NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Debbie Gach
- NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, 6229 ER Maastricht, The Netherlands
- Department of Clinical Epidemiology, VieCuri Medical Centre, 5912 BL Venlo, The Netherlands
| | | | | | - Dennis G. Barten
- Department of Emergency Medicine, VieCuri Medical Centre, 5912 BL Venlo, The Netherlands
| | - Frits H. M. van Osch
- NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, 6229 ER Maastricht, The Netherlands
- Department of Clinical Epidemiology, VieCuri Medical Centre, 5912 BL Venlo, The Netherlands
- Department of Epidemiology, Maastricht University, 6229 ER Maastricht, The Netherlands
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5
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Kwon CY. The Impact of SARS-CoV-2 Infection on Heart Rate Variability: A Systematic Review of Observational Studies with Control Groups. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:909. [PMID: 36673664 PMCID: PMC9859268 DOI: 10.3390/ijerph20020909] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/26/2022] [Accepted: 12/31/2022] [Indexed: 05/13/2023]
Abstract
Autonomic nervous system (ANS) dysfunction can arise after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and heart rate variability (HRV) tests can assess its integrity. This review investigated the relationship between the impact of SARS-CoV-2 infection on HRV parameters. Comprehensive searches were conducted in four electronic databases. Observational studies with a control group reporting the direct impact of SARS-CoV-2 infection on the HRV parameters in July 2022 were included. A total of 17 observational studies were included in this review. The square root of the mean squared differences of successive NN intervals (RMSSD) was the most frequently investigated. Some studies found that decreases in RMSSD and high frequency (HF) power were associated with SARS-CoV-2 infection or the poor prognosis of COVID-19. Also, decreases in RMSSD and increases in the normalized unit of HF power were related to death in critically ill COVID-19 patients. The findings showed that SARS-CoV-2 infection, and the severity and prognosis of COVID-19, are likely to be reflected in some HRV-related parameters. However, the considerable heterogeneity of the included studies was highlighted. The methodological quality of the included observational studies was not optimal. The findings suggest rigorous and accurate measurements of HRV parameters are highly needed on this topic.
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Affiliation(s)
- Chan-Young Kwon
- Department of Oriental Neuropsychiatry, College of Korean Medicine, Dongeui University, 52-57, Yangjeong-ro, Busanjin-gu, Busan 47227, Republic of Korea
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6
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de Almeida LV, Santos-de-Araújo AD, Cutrim RC, Tavarez RRDJ, Borghi-Silva A, Pereira FHF, Pontes-Silva A, Rêgo AS, Rocha DS, Marinho RS, Dibai-Filho AV, Bassi-Dibai D. Intra- and Interrater Reliability of Short-Term Measurement of Heart Rate Variability on Rest in Individuals Post-COVID-19. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13587. [PMID: 36294172 PMCID: PMC9602575 DOI: 10.3390/ijerph192013587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/17/2022] [Accepted: 09/22/2022] [Indexed: 06/16/2023]
Abstract
Individuals affected by COVID-19 have an alteration in autonomic balance, associated with impaired cardiac parasympathetic modulation and, consequently, a decrease in heart rate variability (HRV). This study examines the inter- and intrarater reliability of HRV) parameters derived from short-term recordings in individuals post-COVID. Sixty-nine participants of both genders post-COVID were included. The RR interval, the time elapsed between two successive R-waves of the QRS signal on the electrocardiogram (RRi), were recorded during a 10 min period in a supine position using a portable heart rate monitor (Polar® V800 model). The data were transferred into Kubios® HRV standard analysis software and analyzed within the stable sessions containing 256 sequential RRi. The intraclass correlation coefficient (ICC) ranged from 0.920 to 1.000 according to the intrarater analysis by Researcher 01 and 0.959 to 0.999 according to the intrarater by Researcher 02. The interrater ICC ranged from 0.912 to 0.998. The coefficient of variation was up to 9.23 for Researcher 01 intrarater analysis, 6.96 for Researcher 02 intrarater analysis and 8.83 for interrater analysis. The measurement of HRV in post-COVID-19 individuals is reliable and presents a small amount of error inherent to the method, supporting its use in the clinical environment and in scientific research.
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Affiliation(s)
- Lucivalda Viegas de Almeida
- Postgraduate Program in Programs Management and Health Services, Universidade Ceuma, São Luís 65075-120, MA, Brazil
- Grupo de Pesquisa em Avaliação e Reabilitação Cardiovascular, Respiratória e Metabólica, Universidade Ceuma, São Luís 65075-120, MA, Brazil
| | - Aldair Darlan Santos-de-Araújo
- Department of Physical Therapy, Universidade Federal de São Carlos, São Carlos 13565-905, SP, Brazil
- Cardiopulmonary Physiotherapy Laboratory—LACAP, Universidade Federal de São Carlos, São Carlos 13565-905, SP, Brazil
| | - Rodrigo Costa Cutrim
- Grupo de Pesquisa em Avaliação e Reabilitação Cardiovascular, Respiratória e Metabólica, Universidade Ceuma, São Luís 65075-120, MA, Brazil
- Postgraduate Program in Dentistry, Universidade Ceuma, São Luís 65075-120, MA, Brazil
| | | | - Audrey Borghi-Silva
- Department of Physical Therapy, Universidade Federal de São Carlos, São Carlos 13565-905, SP, Brazil
- Cardiopulmonary Physiotherapy Laboratory—LACAP, Universidade Federal de São Carlos, São Carlos 13565-905, SP, Brazil
| | - Fábio Henrique Ferreira Pereira
- Grupo de Pesquisa em Avaliação e Reabilitação Cardiovascular, Respiratória e Metabólica, Universidade Ceuma, São Luís 65075-120, MA, Brazil
- Postgraduate Program in Environment, Universidade Ceuma, São Luís 65075-120, MA, Brazil
| | - André Pontes-Silva
- Department of Physical Therapy, Universidade Federal de São Carlos, São Carlos 13565-905, SP, Brazil
| | - Adriana Sousa Rêgo
- Postgraduate Program in Programs Management and Health Services, Universidade Ceuma, São Luís 65075-120, MA, Brazil
- Grupo de Pesquisa em Avaliação e Reabilitação Cardiovascular, Respiratória e Metabólica, Universidade Ceuma, São Luís 65075-120, MA, Brazil
- Postgraduate Program in Environment, Universidade Ceuma, São Luís 65075-120, MA, Brazil
- Department of Physical Therapy, Universidade Ceuma, São Luís 65075-120, MA, Brazil
- Postgraduate Program in Adult Health, Universidade Federal do Maranhão, São Luís 65080-805, MA, Brazil
| | - Daniel Santos Rocha
- Grupo de Pesquisa em Avaliação e Reabilitação Cardiovascular, Respiratória e Metabólica, Universidade Ceuma, São Luís 65075-120, MA, Brazil
- Department of Physical Therapy, Universidade Ceuma, São Luís 65075-120, MA, Brazil
| | - Renan Shida Marinho
- Department of Physical Therapy, Universidade Federal de São Carlos, São Carlos 13565-905, SP, Brazil
- Cardiopulmonary Physiotherapy Laboratory—LACAP, Universidade Federal de São Carlos, São Carlos 13565-905, SP, Brazil
| | - Almir Vieira Dibai-Filho
- Postgraduate Program in Adult Health, Universidade Federal do Maranhão, São Luís 65080-805, MA, Brazil
- Grupo de Pesquisa em Reabilitação, Exercício e Movimento (REMOVI) Universidade Federal do Maranhão, São Luís 65080-805, MA, Brazil
| | - Daniela Bassi-Dibai
- Postgraduate Program in Programs Management and Health Services, Universidade Ceuma, São Luís 65075-120, MA, Brazil
- Grupo de Pesquisa em Avaliação e Reabilitação Cardiovascular, Respiratória e Metabólica, Universidade Ceuma, São Luís 65075-120, MA, Brazil
- Postgraduate Program in Dentistry, Universidade Ceuma, São Luís 65075-120, MA, Brazil
- Postgraduate Program in Environment, Universidade Ceuma, São Luís 65075-120, MA, Brazil
- Department of Physical Therapy, Universidade Ceuma, São Luís 65075-120, MA, Brazil
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Salem AM, Yar T, Al Eid M, Almahfoudh H, Alsaffar M, Al Ibrahim A, Almadan A, Alaidarous S, Almulhim R, Rafique N, Latif R, Siddiqui IA, Alsunni A. Post-Acute Effect of SARS-CoV-2 Infection on the Cardiac Autonomic Function. Int J Gen Med 2022; 15:7593-7603. [PMID: 36204699 PMCID: PMC9531620 DOI: 10.2147/ijgm.s382331] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 09/20/2022] [Indexed: 11/23/2022] Open
Abstract
Background Methods Results Conclusion
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Affiliation(s)
- Ayad Mohammed Salem
- Department of Physiology, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
- Correspondence: Ayad Mohammed Salem, Department of Physiology, College of Medicine, Imam Abdulrahman Bin Faisal University, PO Box 2114-31451, Dammam, Saudi Arabia, Email
| | - Talay Yar
- Department of Physiology, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Mohammed Al Eid
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Husain Almahfoudh
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Mohammed Alsaffar
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Abdullah Al Ibrahim
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Ali Almadan
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Sana Alaidarous
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Razan Almulhim
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Nazish Rafique
- Department of Physiology, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Rabia Latif
- Department of Physiology, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Intisar Ahmad Siddiqui
- Department of Dental Education, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Ahmed Alsunni
- Department of Physiology, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
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8
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Agrawal A, Chauhan A, Shetty MK, P GM, Gupta MD, Gupta A. ECG-iCOVIDNet: Interpretable AI model to identify changes in the ECG signals of post-COVID subjects. Comput Biol Med 2022; 146:105540. [PMID: 35533456 PMCID: PMC9055384 DOI: 10.1016/j.compbiomed.2022.105540] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 03/26/2022] [Accepted: 04/15/2022] [Indexed: 12/16/2022]
Abstract
OBJECTIVE Studies showed that many COVID-19 survivors develop sub-clinical to clinical heart damage, even if subjects did not have underlying heart disease before COVID. Since Electrocardiogram (ECG) is a reliable technique for cardiovascular disease diagnosis, this study analyzes the 12-lead ECG recordings of healthy and post-COVID (COVID-recovered) subjects to ascertain ECG changes after suffering from COVID-19. METHOD We propose a shallow 1-D convolutional neural network (CNN) deep learning architecture, namely ECG-iCOVIDNet, to distinguish ECG data of post-COVID subjects and healthy subjects. Further, we employed ShAP technique to interpret ECG segments that are highlighted by the CNN model for the classification of ECG recordings into healthy and post-COVID subjects. RESULTS ECG data of 427 healthy and 105 post-COVID subjects were analyzed. Results show that the proposed ECG-iCOVIDNet model could classify the ECG recordings of healthy and post-COVID subjects better than the state-of-the-art deep learning models. The proposed model yields an F1-score of 100%. CONCLUSION So far, we have not come across any other study with an in-depth ECG signal analysis of the COVID-recovered subjects. In this study, it is shown that the shallow ECG-iCOVIDNet CNN model performed good for distinguishing ECG signals of COVID-recovered subjects from those of healthy subjects. In line with the literature, this study confirms changes in the ECG signals of COVID-recovered patients that could be captured by the proposed CNN model. Successful deployment of such systems can help the doctors identify the changes in the ECG of the post-COVID subjects on time that can save many lives.
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Affiliation(s)
| | | | | | - Girish M. P
- Department of Cardiology, GIPMER, Delhi, India
| | | | - Anubha Gupta
- SBILab, Department of ECE, IIIT-Delhi, Delhi, India,Corresponding author
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9
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Shah B, Kunal S, Bansal A, Jain J, Poundrik S, Shetty MK, Batra V, Chaturvedi V, Yusuf J, Mukhopadhyay S, Tyagi S, Meenahalli Palleda G, Gupta A, Gupta MD. Heart rate variability as a marker of cardiovascular dysautonomia in post-COVID-19 syndrome using artificial intelligence. Indian Pacing Electrophysiol J 2022; 22:70-76. [PMID: 35101582 PMCID: PMC8800539 DOI: 10.1016/j.ipej.2022.01.004] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 12/29/2021] [Accepted: 01/20/2022] [Indexed: 01/03/2023] Open
Abstract
Introduction Cardiovascular dysautonomia comprising postural orthostatic tachycardia syndrome (POTS) and orthostatic hypotension (OH) is one of the presentations in COVID-19 recovered subjects. We aim to determine the prevalence of cardiovascular dysautonomia in post COVID-19 patients and to evaluate an Artificial Intelligence (AI) model to identify time domain heart rate variability (HRV) measures most suitable for short term ECG in these subjects. Methods This observational study enrolled 92 recently COVID-19 recovered subjects who underwent measurement of heart rate and blood pressure response to standing up from supine position and a 12-lead ECG recording for 60 s period during supine paced breathing. Using feature extraction, ECG features including those of HRV (RMSSD and SDNN) were obtained. An AI model was constructed with ShAP AI interpretability to determine time domain HRV features representing post COVID-19 recovered state. In addition, 120 healthy volunteers were enrolled as controls. Results Cardiovascular dysautonomia was present in 15.21% (OH:13.04%; POTS:2.17%). Patients with OH had significantly lower HRV and higher inflammatory markers. HRV (RMSSD) was significantly lower in post COVID-19 patients compared to healthy controls (13.9 ± 11.8 ms vs 19.9 ± 19.5 ms; P = 0.01) with inverse correlation between HRV and inflammatory markers. Multiple perceptron was best performing AI model with HRV(RMSSD) being the top time domain HRV feature distinguishing between COVID-19 recovered patients and healthy controls. Conclusion Present study showed that cardiovascular dysautonomia is common in COVID-19 recovered subjects with a significantly lower HRV compared to healthy controls. The AI model was able to distinguish between COVID-19 recovered patients and healthy controls.
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Affiliation(s)
- Bhushan Shah
- Department of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, India
| | - Shekhar Kunal
- Department of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, India
| | - Ankit Bansal
- Department of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, India
| | - Jayant Jain
- SBILab, Department of ECE, IIIT, Delhi, India
| | | | - Manu Kumar Shetty
- Department of Pharmacology, Maulana Azad Medical College, Delhi, India
| | - Vishal Batra
- Department of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, India
| | - Vivek Chaturvedi
- Senior Consultant Cardiologist and Director Cardiac Electrophysiology, Narayana Superspeciality Hospital, Gurugram, India
| | - Jamal Yusuf
- Department of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, India
| | - Saibal Mukhopadhyay
- Department of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, India
| | - Sanjay Tyagi
- Department of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, India
| | - Girish Meenahalli Palleda
- Department of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, India
| | | | - Mohit Dayal Gupta
- Department of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, India.
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