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Liu C, Lee CH, Lin SF, Tsai WC. Temporal Clustering of Skin Sympathetic Nerve Activity Bursts in Acute Myocardial Infarction Patients. Front Neurosci 2021; 15:720827. [PMID: 34916895 PMCID: PMC8669957 DOI: 10.3389/fnins.2021.720827] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 10/18/2021] [Indexed: 11/13/2022] Open
Abstract
Backgrounds: Acute myocardial infarction (AMI) affects the autonomic nervous system (ANS) function. The aim of our study is to detect the particular patterns of ANS regulation in AMI. We hypothesize that altered ANS regulation in AMI patients causes synchronized neural discharge (clustering phenomenon) detected by non-invasive skin sympathetic nerve activity (SKNA). Methods: Forty subjects, including 20 AMI patients and 20 non-AMI controls, participated in the study. The wide-band bioelectrical signals (neuECG) were continuously recorded on the body surface for 5 min. SKNA was signal processed to depict the envelope of SKNA (eSKNA). By labeling the clusters, the AMI subjects were separated into non-AMI, non-cluster appearing (AMINCA), and cluster appearing (AMICA) groups. Results: The average eSKNA was significantly correlated with HRV low-frequency (LF) power (rho = −0.336) and high-frequency power (rho = −0.372). The cross-comparison results demonstrated that eSKNA is a valid surrogate marker to assess ANS in AMI patients. The frequency of cluster occurrence was 0.01–0.03 Hz and the amplitude was about 3 μV. The LF/HF ratio of AMICA (median: 1.877; Q1–Q3: 1.483–2.413) revealed significantly lower than AMINCA (median: 3.959; Q1–Q3: 1.840–6.562). The results suggest that the SKNA clustering is a unique temporal pattern of ANS synchronized discharge, which could indicate the lower sympathetic status (by HRV) in AMI patients. Conclusion: This is the first study to identify SKNA clustering phenomenon in AMI patients. Such a synchronized nerve discharge pattern could be detected with non-invasive SKNA signals. SKNA temporal clustering could be a novel biomarker to classify ANS regulation ability in AMI patients. Clinical and Translational Significance: SKNA is higher in AMI patients than in control and negatively correlates with parasympathetic parameters. SKNA clustering is associated with a lower LF/HF ratio that has been shown to correlate with sudden cardiac death in AMI. The lack of SKNA temporal clustering could indicate poor ANS regulation in AMI patients.
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Affiliation(s)
- Chun Liu
- Department of Electrical and Computer Engineering, College of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan
| | - Chien-Hung Lee
- Department of Public Health, College of Health Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan.,Research Center for Environmental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Shien-Fong Lin
- Department of Electrical and Computer Engineering, College of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan.,Institute of Biomedical Engineering, College of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan
| | - Wei-Chung Tsai
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.,Department of Internal Medicine, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
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Hernández-Vicente A, Hernando D, Vicente-Rodríguez G, Bailón R, Garatachea N, Pueyo E. ECG Ventricular Repolarization Dynamics during Exercise: Temporal Profile, Relation to Heart Rate Variability and Effects of Age and Physical Health. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:9497. [PMID: 34574421 PMCID: PMC8469015 DOI: 10.3390/ijerph18189497] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 08/30/2021] [Accepted: 09/06/2021] [Indexed: 12/18/2022]
Abstract
Periodic repolarization dynamics (PRD) is a novel electrocardiographic marker of cardiac repolarization instability with powerful risk stratification capacity for total mortality and sudden cardiac death. Here, we use a time-frequency analysis approach to continuously quantify PRD at rest and during exercise, assess its dependence on heart rate variability (HRV) and characterize the effects of age (young adults/middle-aged adults/older adults), body mass index (non-overweight/overweight) and cardiorespiratory fitness level (fit/unfit). Sixty-six male volunteers performed an exercise test. RR and dT variabilities (RRV, dTV), as well as the fraction of dT variability unrelated to RR variability, were computed based on time-frequency representations. The instantaneous LF power of dT (PdTV), representing the same concept as PRD, and of its RRV-unrelated component (PdTVuRRV) were quantified. dT angle was found to mostly oscillate in the LF band. Overall, 50-70% of PdTV was linearly unrelated to RRV. The onset of exercise caused a sudden increase in PdTV and PdTVuRRV, which returned to pre-exercise levels during recovery. Clustering analysis identified a group of overweight and unfit individuals with significantly higher PdTV and PdTVuRRV values at rest than the rest of the population. Our findings shed new light on the temporal profile of PRD during exercise, its relationship to HRV and the differences in PRD between subjects according to phenotypic characteristics.
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Affiliation(s)
- Adrián Hernández-Vicente
- Growth, Exercise, NUtrition and Development (GENUD) Research Group, University of Zaragoza, 50009 Zaragoza, Spain; (G.V.-R.); (N.G.)
- Department of Physiatry and Nursing, Faculty of Health and Sport Science (FCSD), University of Zaragoza, 22002 Huesca, Spain
- Red española de Investigación en Ejercicio Físico y Salud en Poblaciones Especiales (EXERNET), 50009 Zaragoza, Spain
| | - David Hernando
- Biomedical Signal Interpretation and Computational Simulation (BSICoS), Aragón Institute for Engineering Research (I3A), IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain; (D.H.); (R.B.); (E.P.)
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
| | - Germán Vicente-Rodríguez
- Growth, Exercise, NUtrition and Development (GENUD) Research Group, University of Zaragoza, 50009 Zaragoza, Spain; (G.V.-R.); (N.G.)
- Department of Physiatry and Nursing, Faculty of Health and Sport Science (FCSD), University of Zaragoza, 22002 Huesca, Spain
- Red española de Investigación en Ejercicio Físico y Salud en Poblaciones Especiales (EXERNET), 50009 Zaragoza, Spain
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBER-Obn), 28029 Madrid, Spain
- Instituto Agroalimentario de Aragón -IA2- CITA-Universidad de Zaragoza, 50013 Zaragoza, Spain
| | - Raquel Bailón
- Biomedical Signal Interpretation and Computational Simulation (BSICoS), Aragón Institute for Engineering Research (I3A), IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain; (D.H.); (R.B.); (E.P.)
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
| | - Nuria Garatachea
- Growth, Exercise, NUtrition and Development (GENUD) Research Group, University of Zaragoza, 50009 Zaragoza, Spain; (G.V.-R.); (N.G.)
- Department of Physiatry and Nursing, Faculty of Health and Sport Science (FCSD), University of Zaragoza, 22002 Huesca, Spain
- Red española de Investigación en Ejercicio Físico y Salud en Poblaciones Especiales (EXERNET), 50009 Zaragoza, Spain
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBER-Obn), 28029 Madrid, Spain
- Instituto Agroalimentario de Aragón -IA2- CITA-Universidad de Zaragoza, 50013 Zaragoza, Spain
| | - Esther Pueyo
- Biomedical Signal Interpretation and Computational Simulation (BSICoS), Aragón Institute for Engineering Research (I3A), IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain; (D.H.); (R.B.); (E.P.)
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
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