Normalization of Electrocardiogram-Derived Cardiac Risk Indices: A Scoping Review of the Open-Access Literature
Abstract
:Featured Application
Abstract
1. Introduction
- Which ECG-derived features used as cardiac risk indices have been normalized in the literature?
- How was normalization performed?
- Why was normalization performed?
2. Materials and Methods
2.1. Literature Search Strategy
- ECG, EKG, electrocardiogra*;
- normaliz*;
- alternans, risk, threat*, arrhythmi*, instability, abnorma*, anomal*, irregular*, vulnerab*, susceptib*.
2.2. Selection of Documents
- Documents where the term ‘normalization’ is defined as the transformation process of a parameter consisting in dividing it by an intra-subject or inter-subject consistent factor.
- Documents where normalization is applied specifically to ECG-derived parameters.
- Documents where normalization is used to enhance the effectiveness or reliability of the parameter.
- Documents where the application modality of normalization is uniquely described.
- Documents where the application of normalization is adequately justified.
- Documents in which the term ‘normalization’ is defined as the restoration of the physiological condition;
- Documents in which normalization is included in the standard definition of a parameter, not applied after the parameter extraction, or applied to parameters extracted from signals other than the ECG;
- Documents in which normalization was intended as a default preprocessing step for a machine learning application;
- Documents in which there was no adequate explanation of why and/or how normalization was applied.
2.3. Data Charting
3. Results
4. Discussion
Study Limitations
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
SECTION | ITEM | PRISMA-ScR CHECKLIST ITEM | PAGE(S) |
---|---|---|---|
TITLE | |||
Title | 1 | Identify the report as a scoping review. | 1 |
ABSTRACT | |||
Structured summary | 2 | Provide a structured summary that includes (as applicable): background, objectives, eligibility criteria, sources of evidence, charting methods, results, and conclusions that relate to the review questions and objectives. | 1 |
INTRODUCTION | |||
Rationale | 3 | Describe the rationale for the review in the context of what is already known. Explain why the review questions/objectives lend themselves to a scoping review approach. | 2 |
Objectives | 4 | Provide an explicit statement of the questions and objectives being addressed with reference to their key elements (e.g., population or participants, concepts, and context) or other relevant key elements used to conceptualize the review questions and/or objectives. | 2 |
MATERIALS and METHODS | |||
Protocol and registration | 5 | Indicate whether a review protocol exists; state if and where it can be accessed (e.g., a Web address); and if available, provide registration information, including the registration number. | NA |
Eligibility criteria | 6 | Specify characteristics of the sources of evidence used as eligibility criteria (e.g., years considered, language, and publication status), and provide a rationale. | 3 |
Information sources | 7 | Describe all information sources in the search (e.g., databases with dates of coverage and contact with authors to identify additional sources), as well as the date the most recent search was executed. | 3 |
Search | 8 | Present the full electronic search strategy for at least 1 database, including any limits used, such that it could be repeated. | 15–16 |
Selection of sources of evidence | 9 | State the process for selecting sources of evidence (i.e., screening and eligibility) included in the scoping review. | 3–4 |
Data charting process | 10 | Describe the methods of charting data from the included sources of evidence (e.g., calibrated forms or forms that have been tested by the team before their use, and whether data charting was done independently or in duplicate) and any processes for obtaining and confirming data from investigators. | 4 |
Data items | 11 | List and define all variables for which data were sought and any assumptions and simplifications made. | 16–17 |
Critical appraisal of individual sources of evidence | 12 | If done, provide a rationale for conducting a critical appraisal of included sources of evidence; describe the methods used and how this information was used in any data synthesis (if appropriate). | NA |
Synthesis of results | 13 | Describe the methods of handling and summarizing the data that were charted. | 4 |
RESULTS | |||
Selection of sources of evidence | 14 | Give numbers of sources of evidence screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally using a flow diagram. | 4–5 |
Characteristics of sources of evidence | 15 | For each source of evidence, present characteristics for which data were charted and provide the citations. | 6–11 |
Critical appraisal within sources of evidence | 16 | If done, present data on critical appraisal of included sources of evidence (see item 12). | NA |
Results of individual sources of evidence | 17 | For each included source of evidence, present the relevant data that were charted that relate to the review questions and objectives. | 7–11 |
Synthesis of results | 18 | Summarize and/or present the charting results as they relate to the review questions and objectives. | 9–11 |
DISCUSSION | |||
Summary of evidence | 19 | Summarize the main results (including an overview of concepts, themes, and types of evidence available), link to the review questions and objectives, and consider the relevance to key groups. | 11–13 |
Limitations | 20 | Discuss the limitations of the scoping review process. | 13 |
CONCLUSIONS | |||
Conclusions | 21 | Provide a general interpretation of the results with respect to the review questions and objectives, as well as potential implications and/or next steps. | 13–14 |
Funding | 22 | Describe sources of funding for the included sources of evidence, as well as sources of funding for the scoping review. Describe the role of the funders of the scoping review. | 14 |
Appendix B
Appendix B.1. Search Query Used on Scopus
Appendix B.2. Search Query Used on PubMed
Appendix B.3. Search Query Used on WoS
Appendix C
Item | Definition |
---|---|
Publication year | Year in which the document was officially published in an international scientific journal or conference |
Population size | Total number of individuals within the population being studied |
Sex | Total number of males and total number of females (biological sexes) within the population being studied |
Age | Age range to which the population being studied belongs, considering the following:
|
Clinical Information: Health status and Details | Definition of the health status of individuals within the population being studied, considering the following:
|
Target of the study | Description of the rationale and intent of the study being described in the document (as it is reported in the document or uniquely derived from it) |
Feature | Definition of the ECG-derived parameter being analyzed |
Domain | Domain in which the feature was computed (time or frequency) |
Normalization modality (how) | Description of how normalization was applied to the feature, expressed through a mathematical formula (as it is reported in the document or uniquely derived from it) |
Normalization reason (why) | Description of why normalization was applied to the feature (as it is reported in the document or uniquely derived from it) |
References
- German, C.A.; Baum, S.J.; Ferdinand, K.C.; Gulati, M.; Polonsky, T.S.; Toth, P.P.; Shapiro, M.D. Defining preventive cardiology: A clinical practice statement from the American Society for Preventive Cardiology. Am. J. Prev. Cardiol. 2022, 12, 100432. [Google Scholar] [CrossRef] [PubMed]
- Wang, C.; Ye, D.; Xie, Z.; Huang, X.; Wang, Z.; Shangguan, H.; Zhu, W.; Wang, S. Assessment of Cardiovascular Risk Factors and Their Interactions in the Risk of Coronary Heart Disease in Patients with Type 2 Diabetes with Different Weight Levels, 2013–2018. Diabetes Metab. Syndr. Obes. 2021, 14, 4253–4262. [Google Scholar] [CrossRef] [PubMed]
- Gimeno-Blanes, F.J.; Blanco-Velasco, M.; Barquero-Pérez, Ó.; García-Alberola, A.; Rojo-Álvarez, J.L. Sudden Cardiac Risk Stratification with Electrocardiographic Indices—A Review on Computational Processing, Technology Transfer, and Scientific Evidence. Front. Physiol. 2016, 7, 82. [Google Scholar] [CrossRef] [PubMed]
- Singh, P.K.; Akhtar, S.; Singh, S. The Clinical Relevance of ECG Parameters in the Prediction of Cardiac Mortality: A Comprehensive Review. Open Bioinform. J. 2024, 17, e18750362295563. [Google Scholar] [CrossRef]
- Straus, S.M.; Kors, J.A.; De Bruin, M.L.; van der Hooft, C.S.; Hofman, A.; Heeringa, J.; Deckers, J.W.; Kingma, J.H.; Sturkenboom, M.C.; Stricker, B.H.; et al. Prolonged QTc interval and risk of sudden cardiac death in a population of older adults. J. Am. Coll. Cardiol. 2006, 47, 362–367. [Google Scholar] [CrossRef]
- Tse, G.; Yan, B.P. Traditional and novel electrocardiographic conduction and repolarization markers of sudden cardiac death. EP Eur. 2017, 19, 712–721. [Google Scholar] [CrossRef]
- Calò, L.; Lanza, O.; Crescenzi, C.; Parisi, C.; Panattoni, G.; Martino, A.; Rebecchi, M.; Tarzia, P.; Ciampi, P.; Romeo, F.; et al. The value of the 12-lead electrocardiogram in the prediction of sudden cardiac death. Eur. Heart J. Suppl. 2023, 25, C218–C226. [Google Scholar] [CrossRef]
- Carbone, V.; Guarnaccia, F.; Carbone, G.; Zito, G.B.; Oliviero, U.; Soreca, S.; Carbone, F. Gender differences in the 12-lead electrocardiogram: Clinical implications and prospects. Ital. J. Gend.-Specif. Med. 2020, 6, 126–141. [Google Scholar] [CrossRef]
- Mansi, I.A.; Nash, I.S. Ethnic differences in electrocardiographic amplitude measurements. Ann. Saudi Med. 2004, 24, 459–464. [Google Scholar] [CrossRef]
- Butt, J.H.; Claggett, B.L.; Miao, Z.M.; Jering, K.S.; Sim, D.; van der Meer, P.; Ntsekhe, M.; Amir, O.; Cho, M.C.; Carrillo-Calvillo, J.; et al. Geographic differences in patients with acute myocardial infarction in the PARADISE-MI trial. Eur. J. Heart Fail. 2023, 25, 1228–1242. [Google Scholar] [CrossRef]
- Alkhaqani, A. Electrocardiography Morphology of Electrolytes Disturbance. J. Clin. Nurs. 2023, 5, 1–6. [Google Scholar]
- Parodi, J.B.; Ramchandani, R.; Zhou, Z.; Chango, D.X.; Acunzo, R.; Liblik, K.; Farina, J.M.; Zaidel, E.J.; Ruiz-Mori, E.; Carreón, J.M.A.; et al. A systematic review of electrocardiographic changes in healthy high-altitude populations. Trends Cardiovasc. Med. 2023, 33, 309–315. [Google Scholar] [CrossRef] [PubMed]
- Ramchandani, R.; Zhou, Z.; Parodi, J.B.; Farina, J.M.; Liblik, K.; Sotomayor, J.; Burak, C.; Herman, R.; Baranchuk, A. A Systematic Review of Electrocardiographic Changes in Populations Temporarily Ascending to High Altitudes. Curr. Probl. Cardiol. 2023, 48, 101630. [Google Scholar] [CrossRef] [PubMed]
- Hassing, G.J.; van der Wall, H.E.C.; van Westen, G.J.P.; Kemme, M.J.B.; Adiyaman, A.; Elvan, A.; Burggraaf, J.; Gal, P. Body mass index related electrocardiographic findings in healthy young individuals with a normal body mass index. Neth. Heart J. 2019, 27, 506–512. [Google Scholar] [CrossRef] [PubMed]
- Iconaru, E.I.; Ciucurel, C. The Relationship between Body Composition and ECG Ventricular Activity in Young Adults. Int. J. Environ. Res. Public Health 2022, 19, 11105. [Google Scholar] [CrossRef] [PubMed]
- Musa, N.; Gital, A.Y.; Aljojo, N.; Chiroma, H.; Adewole, K.S.; Mojeed, H.A.; Faruk, N.; Abdulkarim, A.; Emmanuel, I.; Folawiyo, Y.Y.; et al. A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram. J. Ambient. Intell. Humaniz. Comput. 2023, 14, 9677–9750. [Google Scholar] [CrossRef]
- Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef]
- Tricco, A.C.; Lillie, E.; Zarin, W.; O’Brien, K.K.; Colquhoun, H.; Levac, D.; Moher, D.; Peters, M.D.J.; Horsley, T.; Weeks, L.; et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and explanation. Ann. Intern. Med. 2018, 169, 467–473. [Google Scholar] [CrossRef]
- Bexton, R.S.; Vallin, H.O.; Camm, A.J. Diurnal variation of the QT interval—Influence of the autonomic nervous system. Br. Heart J. 1986, 55, 253–258. [Google Scholar] [CrossRef]
- Furlan, R.; Ardizzone, S.; Palazzolo, L.; Rimoldi, A.; Perego, F.; Barbic, F.; Bevilacqua, M.; Vago, L.; Bianchi Porro, G.; Malliani, A. Sympathetic overactivity in active ulcerative colitis: Effects of clonidine. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2005, 290, R224–R232. [Google Scholar] [CrossRef]
- Itagi, A.B.H.; Arora, D.; Patil, N.A.; Bailwad, S.A.; Yunus, G.Y.; Goel, A. Short-term acute effects of gutkha chewing on heart rate variability among young adults: A cross-sectional study. Int. J. Appl. Basic. Med. Res. 2016, 6, 45–49. [Google Scholar] [CrossRef] [PubMed]
- Jarrin, D.C.; Ivers, H.; Lamy, M.; Chen, I.Y.; Harvey, A.G.; Morin, C.M. Cardiovascular autonomic dysfunction in insomnia patients with objective short sleep duration. J. Sleep. Res. 2018, 27, e12663. [Google Scholar] [CrossRef] [PubMed]
- Melo, R.C.; Santos, M.D.B.; Silva, E.; Quitério, R.J.; Moreno, M.A.; Reis, M.S.; Verzola, I.A.; Oliveira, L.; Martins, L.E.; Gallo-Junior, L.; et al. Effects of age and physical activity on the autonomic control of heart rate in healthy men. Braz. J. Med. Biol. Res. 2005, 38, 1331–1338. [Google Scholar] [CrossRef] [PubMed]
- Pecis, M.; Azevedo, M.J.; Moraes, R.S.; Ferlin, E.L.; Gross, J.L. Autonomic dysfunction and urinary albumin excretion rate are associated with an abnormal blood pressure pattern in normotensive normoalbuminuric type 1 diabetic patients. Diabetes Care. 2000, 23, 989–993. [Google Scholar] [CrossRef] [PubMed]
- Pei, J.; Tang, W.; Li, L.X.; Su, C.Y.; Wang, T. The study of spectral analysis of heart rate variability in different blood pressure types in euvolemic peritoneal dialysis patients. Ren. Fail. 2012, 34, 722–726. [Google Scholar] [CrossRef]
- Poulikakos, D.; Banerjee, D.; Malik, M. Repolarisation descriptors and heart rate variability in hemodialysed patients. Physiol. Res. 2014, 64, 487–493. [Google Scholar] [CrossRef]
- Rauchenzauner, M.; Ernst, F.; Hintringer, F.; Ulmer, H.; Ebenbichler, C.F.; Kasseroler, M.T.; Joannidis, M. Arrhythmias and increased neuro-endocrine stress response during physicians’ night shifts: A randomized cross-over trial. Eur. Heart J. 2009, 30, 2606–2613. [Google Scholar] [CrossRef]
- Scavone, G.; Baril, A.A.; Montplaisir, J.; Carrier, J.; Desautels, A.; Zadra, A. Autonomic Modulation During Baseline and Recovery Sleep in Adult Sleepwalkers. Front. Neurol. 2021, 12, 680596. [Google Scholar] [CrossRef]
- Kim, J.; Kim, J.J.; Seok, J.H.; Kim, E.; Park, J.Y.; Kim, H.E.; Oh, J. Association and interaction between clinician-rated measures of depression and anxiety with heart rate variability in elderly patients with psychiatric disorders. Heliyon 2023, 9, e20740. [Google Scholar] [CrossRef]
- Chen, X.; Tereshchenko, L.G.; Berger, R.D.; Trayanova, N.A. Arrhythmia risk stratification based on QT interval instability: An intracardiac electrocardiogram study. Heart Rhythm. 2013, 10, 875–880. [Google Scholar] [CrossRef] [PubMed]
- Fereniec, M.; Stix, G.; Kania, M.; Mroczka, T.; Maniewski, R. An analysis of the U-wave and its relation to the T-wave in body surface potential maps for healthy subjects and MI patients. Ann. Noninvasive Electrocardiol. 2013, 19, 145–156. [Google Scholar] [CrossRef] [PubMed]
- Frimerman, A.; Meisel, S.; Shotan, A.; Blondheim, D.S. Enhancement of Standard ECGs by a New Method for Multi-Cycle Superimposition and Summation. Isr. Med. Assoc. J. 2018, 20, 14–19. [Google Scholar] [PubMed]
- Giuliani, C.; Agostinelli, A.; Fioretti, S.; Di Nardo, F.; Burattini, L. Abnormal repolarization in the acute myocardial infarction patients: A frequency-based characterization. Open Biomed. Eng. J. 2014, 8, 42–51. [Google Scholar] [CrossRef] [PubMed]
- Giuliani, C.; Swenne, C.A.; Man, S.; Agostinelli, A.; Fioretti, S.; Di Nardo, F.; Burattini, L. Predictive Power of f99 Repolarization Index for the Occurrence of Ventricular Arrhythmias. Ann. Noninvasive Electrocardiol. 2015, 21, 152–160. [Google Scholar] [CrossRef]
- Buchheit, M.; Simon, C.; Piquard, F.; Ehrhart, J.; Brandenberger, G. Effects of increased training load on vagal-related indexes of heart rate variability: A novel sleep approach. Am. J. Physiol. Heart Circ. Physiol. 2004, 287, H2813–H2818. [Google Scholar] [CrossRef]
- Fei, L.; Slade, A.K.; Prasad, K.; Malik, M.; McKenna, W.J.; Camm, A.J. Is there increased sympathetic activity in patients with hypertrophic cardiomyopathy? J. Am. Coll. Cardiol. 1995, 26, 472–480. [Google Scholar] [CrossRef]
- Mulkey, S.B.; Govindan, R.; Metzler, M.; Swisher, C.B.; Hitchings, L.; Wang, Y.; Baker, R.; Larry Maxwell, G.; Krishnan, A.; du Plessis, A.J. Heart rate variability is depressed in the early transitional period for newborns with complex congenital heart disease. Clin. Auton. Res. 2019, 30, 165–172. [Google Scholar] [CrossRef]
- Noben, L.; Verdurmen, K.M.J.; Warmerdam, G.J.J.; Vullings, R.; Oei, S.G.; van Laar, J.O.E.H. The fetal electrocardiogram to detect the effects of betamethasone on fetal heart rate variability. Early Hum. Dev. 2019, 130, 57–64. [Google Scholar] [CrossRef]
- Van Boven, A.J.; Brouwer, J.; Crijns, H.J.G.M.; Haaksma, J.; Lie, K.I. Differential autonomic mechanisms underlying early morning and daytime transient myocardial ischaemia in patients with stable coronary artery disease. Heart. 1995, 73, 134–138. [Google Scholar] [CrossRef]
- Verdurmen, K.M.J.; Warmerdam, G.J.J.; Lempersz, C.; Hulsenboom, A.D.J.; Renckens, J.; Dieleman, J.P.; Vullings, R.; van Laar, J.O.E.H.; Oei, S.G. The influence of betamethasone on fetal heart rate variability, obtained by non-invasive fetal electrocardiogram recordings. Early Hum. Dev. 2018, 119, 8–14. [Google Scholar] [CrossRef]
- Yang, J.H.; Choi, S.H.; Lee, M.H.; Oh, S.M.; Choi, J.W.; Park, J.E.; Park, K.S.; Lee, Y.J. Association of heart rate variability with REM sleep without atonia in idiopathic REM sleep behavior disorder. J. Clin. Sleep. Med. 2021, 17, 461–469. [Google Scholar] [CrossRef] [PubMed]
- Liviero, F.; Scapellato, M.L.; Volpin, A.; Battistella, M.; Fabris, L.; Brischigliaro, L.; Folino, F.; Moretto, A.; Mason, P.; Pavanello, S. Long term follow-up of heart rate variability in healthcare workers with mild COVID-19. Front. Neurol. 2024, 15, 1403551. [Google Scholar] [CrossRef] [PubMed]
- Haigney, M.C.; Zareba, W.; Gentlesk, P.J.; Goldstein, R.E.; Illovsky, M.; McNitt, S.; Andrews, M.L.; Moss, A.J.; Multicenter Automatic Defibrillator Implantation Trial II investigators. QT interval variability and spontaneous ventricular tachycardia or fibrillation in the Multicenter Automatic Defibrillator Implantation Trial (MADIT) II patients. J. Am. Coll. Cardiol. 2004, 44, 1481–1487. [Google Scholar] [CrossRef] [PubMed]
- Pohl, R.K.; Yeragani, V. QT interval variability in panic disorder patients after isoproterenol infusions. Int. J. Neuropsychopharmacol. 2001, 4, 17–20. [Google Scholar] [CrossRef] [PubMed]
- Takeuchi, Y.; Omeki, Y.; Horio, K.; Nishio, M.; Nagata, R.; Oikawa, S.; Mizutani, Y.; Nagatani, A.; Funamoto, Y.; Uchida, H.; et al. Relationship between QT and JT peak interval variability in prepubertal children. Ann. Noninvasive Electrocardiol. 2017, 22, e12444. [Google Scholar] [CrossRef]
- Kotidis, C.; Wertheim, D.; Weindling, M.; Rabe, H.; Turner, M.A. Assessing patent ductus arteriosus in preterm infants from standard neonatal intensive care monitoring. Eur. J. Pediatr. 2022, 181, 1117–1124. [Google Scholar] [CrossRef]
- Krahn, A.D.; Pickett, R.A.; Sakaguchi, S.; Shaik, N.; Cao, J.; Norman, H.S.; Guerrero, P. R-wave sensing in an implantable cardiac monitor without ECG-based preimplant mapping: Results from a multicenter clinical trial. Pacing Clin. Electrophysiol. 2013, 37, 505–511. [Google Scholar] [CrossRef]
- Lin, C.C.; Yang, C.M. Heartbeat classification using normalized RR intervals and morphological features. Math. Probl. Eng. 2014, 2014, 712474. [Google Scholar] [CrossRef]
- Martín-Yebra, A.; Caiani, E.G.; Monasterio, V.; Pellegrini, A.; Laguna, P.; Martínez, J.P. Evaluation of T-wave alternans activity under stress conditions after 5 d and 21 d of sedentary head-down bed rest. Physiol. Meas. 2015, 36, 2041–2055. [Google Scholar] [CrossRef]
- Piccirillo, G.; Magrì, D.; Matera, S.; Magnanti, M.; Torrini, A.; Pasquazzi, E.; Schifano, E.; Velitti, S.; Marigliano, V.; Quaglione, R.; et al. QT variability strongly predicts sudden cardiac death in asymptomatic subjects with mild or moderate left ventricular systolic dysfunction: A prospective study. Eur. Heart J. 2007, 28, 1344–1350. [Google Scholar] [CrossRef]
- Toman, O.; Hnatkova, K.; Šišáková, M.; Smetana, P.; Huster, K.M.; Barthel, P.; Novotný, T.; Andršová, I.; Schmidt, G.; Malik, M. Short-Term Beat-to-Beat QT Variability Appears Influenced More Strongly by Recording Quality Than by Beat-to-Beat RR Variability. Front. Physiol. 2022, 13, 863873. [Google Scholar] [CrossRef] [PubMed]
- Tsipouras, M.G.; Fotiadis, D.I. An efficient system for the detection of arrhythmic segments in ECG recordings based on non-linear features of the RR interval signal. In Proceedings of the Computers in Cardiology, Thessaloniki, Greece, 21–24 September 2003; pp. 533–536. [Google Scholar] [CrossRef]
- Woodward, J.L.; Connolly, M.; Hennessy, P.W.; Holleran, C.L.; Mahtani, G.B.; Brazg, G.; Fahey, M.; Maganti, K.; Hornby, T.G. Cardiopulmonary Responses during Clinical and Laboratory Gait Assessments in People with Chronic Stroke. Phys. Ther. 2019, 99, 86–87. [Google Scholar] [CrossRef] [PubMed]
- Zhang, F.; Hamon, D.; Fang, Z.; Xu, Y.; Yang, B.; Ju, W.; Bradfield, J.; Shivkumar, K.; Chen, M.; Tung, R. Value of a Posterior Electrocardiographic Lead for Localization of Ventricular Outflow Tract Arrhythmias: The V4/V8 Ratio. JACC Clin. Electrophysiol. 2017, 3, 678–686. [Google Scholar] [CrossRef] [PubMed]
- Dono, F.; Evangelista, G.; Consoli, S.; Venditti, R.; Russo, M.; De Angelis, M.V.; Faustino, M.; Di Iorio, A.; Vollono, C.; Anzellotti, F.; et al. Heart rate variability modifications in adult patients with early versus late-onset temporal lobe epilepsy: A comparative observational study. Neurophysiol. Clin. 2023, 53, 102852. [Google Scholar] [CrossRef]
- Kania, M.; Maniewski, R.; Kobylecka, M.; Zaczek, R.; Królicki, L.; Opolski, G.; Janusek, D. Prognostic value of the total cosine R to T measured in high resolution body surface potential mapping during exercise test. Biomed. Signal Process. Control 2015, 20, 135–141. [Google Scholar] [CrossRef]
- Pukropski, J.; Baumann, J.; Jordan, A.; Bausch, M.; von Wrede, R.; Surges, R. Short-term effects of transcutaneous auricular vagus nerve stimulation on T-wave alternans in people with focal epilepsy—An exploratory pilot study. Epilepsy Behav. Rep. 2024, 26, 100657. [Google Scholar] [CrossRef]
- Maheshwari, A.; Norby, F.L.; Soliman, E.Z.; Alraies, M.C.; Adabag, S.; O’Neal, W.T.; Alonso, A.; Chen, L.Y. Relation of Prolonged P-Wave Duration to Risk of Sudden Cardiac Death in the General Population (from the Atherosclerosis Risk in Communities Study). Am. J. Cardiol. 2017, 119, 1302–1306. [Google Scholar] [CrossRef]
- Maheshwari, A.; Norby, F.L.; Roetker, N.S.; Soliman, E.Z.; Koene, R.J.; Rooney, M.R.; O’Neal, W.T.; Shah, A.M.; Claggett, B.L.; Solomon, S.D.; et al. Refining Prediction of Atrial Fibrillation-Related Stroke Using the P2-CHA2DS2-VASc Score. Circulation 2019, 139, 180–191. [Google Scholar] [CrossRef]
- Maheshwari, A.; Norby, F.L.; Soliman, E.Z.; Alonso, A.; Sotoodehnia, N.; Chen, L.Y. Association of P-Wave Abnormalities With Sudden Cardiac and Cardiovascular Death: The ARIC Study. Circulation. Arrhythmia Electrophysiol. 2021, 14, e009314. [Google Scholar] [CrossRef]
- Lehtonen, A.O.; Langén, V.L.; Puukka, P.J.; Kähönen, M.; Nieminen, M.S.; Jula, A.M.; Niiranen, T.J. Incidence rates, correlates, and prognosis of electrocardiographic P-wave abnormalities—A nationwide population-based study. J. Electrocardiol. 2017, 50, 925–932. [Google Scholar] [CrossRef]
- Andršová, I.; Hnatkova, K.; Šišáková, M.; Toman, O.; Smetana, P.; Huster, K.M.; Barthel, P.; Novotný, T.; Schmidt, G.; Malik, M. Heart Rate Influence on the QT Variability Risk Factors. Diagnostics 2020, 10, 1096. [Google Scholar] [CrossRef] [PubMed]
Ref | Pop. Size | Age | Sex | Clinical Information | |
---|---|---|---|---|---|
Health Status | Details | ||||
[19] | 21 | Adult | 17 M; 4 F | D | Cardiomyopathy |
[20] | 43 | Adult | 27 M; 16 F | 23 D; 20 H | Ulcerative colitis; NA |
[21] | 60 | Young adult | 60 M; 0 F | H | NA |
[22] | 180 | Adult | 67 M; 113 F | D | Chronic insomnia |
[23] | 41 | Adult | 41 M; 0 F | H | NA |
[24] | 39 | Adult | 19 M; 20 F | D | Diabetes |
[25] | 62 | Adult | 24 M; 38 F | D | Dialysis |
[26] | 71 | Adult | 48 M; 23 F | D | Dialysis |
[27] | 30 | Adult | 21 M; 9 F | H | NA |
[28] | 28 | Adult | 10 M; 18 F | 14 D; 14 H | Sleepwalking; NA |
[29] | 114 | Adult | 31 M; 83 F | D | Psychiatric disorder |
[30] | 114 | Adult | 90 M; 24 F | D | Cardiomyopathy |
[31] | 56 | Adult | UA | 36 D; 20 H | Myocardial infarction; NA |
[32] | 504 | Adult | 375 M; 129 F | D | Coronary artery disease |
[33] | 155 | Adult | 125 M; 30 F | 108 D; 47 H | Myocardial infarction |
[34] | 170 | Adult | 146 M; 24 F | D | Heart failure |
[35] | 31 | Young adult | UA | H | NA |
[36] | 52 | Adult | 22 M; 30 F | D | Cardiomyopathy |
[37] | 87 | Infant | 51 M; 36 F | 58 D; 29 H | Congenital heart disease; NA |
[38] | 22 | Fetal | UA | D | Preterm delivery |
[39] | 51 | Adult | 51 M; 0 F | D | Coronary artery disease |
[40] | 12 | Fetal | UA | D | Preterm delivery |
[41] | 73 | Adult | 42 M; 31 F | 47 D; 26 H | Sleep disorder; NA |
[42] | 96 | Adult | 23 M; 73 F | D | COVID-19 |
[43] | 817 | Adult | 670 M; 147 F | D | Myocardial infarction |
[44] | 17 | Young adult | 7 M; 10 F | 6 D; 11 H | Panic disorder; NA |
[45] | 680 | Infant to young adult | 382 M; 298 F | H | NA |
[46] | 14 | Newborn | UA | D | Preterm delivery |
[47] | 41 | Adult | 15 M; 26 F | D | Cardiomyopathy |
[48] | 47 | Adult | 25 M; 22 F | D | Heart rhythm disorder |
[49] | 44 | Adult | 44 M; 0 F | H | NA |
[50] | 396 | Adult | 283 M; 113 F | D | Ventricular systolic disfunction |
[51] | 523 | Adult | 264 M; 259 F | H | NA |
[52] | 47 | Adult | 25 M; 22 F | D | Heart rhythm disorder |
[53] | 53 | Adult | 31 M; 22 F | D | Chronic stroke |
[54] | 134 | Adult | 49 M; 85 F | D | Heart rhythm disorder |
[55] | 50 | Adult | 20 M; 30 F | D | Temporal lobe epilepsy |
[56] | 123 | Adult | 123 M; 0 F | 90 D; 33 H | Cardiomyopathy; NA |
[57] | 5 | Adult | 2 M; 3 F | D | Focal epilepsy |
Ref | Target of the Study |
---|---|
[19] | Evaluate the influence of diurnal changes on cardiac repolarization |
[20] | Evaluate if exaggerated sympathetic activity characterizes active ulcerative colitis |
[21] | Evaluate the effect of gutkha chewing on cardiac autonomic modulation |
[22] | Evaluate cardiovascular autonomic function in insomnia patients |
[23] | Evaluate the effect of age and physical activity on cardiac autonomic control |
[24] | Evaluate the role of autonomic nervous system in a blunted fall in nocturnal blood pressure |
[25] | Evaluate the role of autonomic nervous system in the pathogenesis of blood pressure abnormalities |
[26] | Evaluate the association between T-wave morphology and cardiac autonomic modulation |
[27] | Evaluate the effect of a 24 h medical on-call duty on physiological functions |
[28] | Evaluate the cardiac autonomic modulation during slow-wave sleep in sleepwalkers |
[29] | Evaluate objective psychiatric indicators in geriatric patients with emotional distress |
[30] | Propose an index for cardiac risk stratification |
[31] | Evaluate the U-wave morphology and its relation to the T wave |
[32] | Propose a method to enhance and improve acquisition, analysis, and display of ECG |
[33] | Propose a new index (f99) to characterize repolarization |
[34] | Evaluate the predictive power of f99 index for the occurrence of ventricular arrhythmias |
[35] | Evaluate the beneficial effect of moderate and intensive physical exercise |
[36] | Evaluate the role of autonomic nervous system in hypertrophic cardiomyopathy |
[37] | Evaluate early changes in autonomic nervous system tone in newborns with complex congenital heart disease |
[38] | Evaluate the effect of betamethasone on autonomic modulation of fetal heart rate |
[39] | Evaluate the role of autonomic regulatory mechanisms in transient myocardial ischemia |
[40] | Evaluate the effect of betamethasone on autonomic modulation of fetal heart rate |
[41] | Evaluate autonomic dysfunctions in patients with idiopathic sleep behavior disorders |
[42] | Evaluate autonomic residual effects of COVID-19 infection |
[43] | Evaluate the relation between repolarization lability and ventricular arrhythmias |
[44] | Evaluate the effect of sympathetic stimulation on the autonomic control of the heart |
[45] | Evaluate the relationship between indices of cardiac repolarization |
[46] | Evaluate if patent ductus arteriosus status can be assessed from standard neonatal intensive care monitoring |
[47] | Evaluate R-wave sensing for implantable cardiac monitor |
[48] | Propose an automatic heartbeat classification system |
[49] | Evaluate if head-down (−6°) bed rest increases TWA |
[50] | Evaluate the role of QT parameters as predictors of sudden cardiac death |
[51] | Evaluate the influence of RR variability and signal quality on indices of ventricular repolarization |
[52] | Propose an arrhythmic segment detection method |
[53] | Evaluate cardiorespiratory responses during walk and graded exercise tests |
[54] | Propose the anteroposterior V4/V8 ratio for localization of ventricular outflow tract arrhythmias |
[55] | Evaluate changes of cardio-autonomic function in temporal lobe epilepsy |
[56] | Evaluate the prognostic value of the total cosine R-to-T in arrhythmia risk assessment |
[57] | Evaluate the short-term effect of transcutaneous auricular vagus nerve stimulation on TWA |
Ref | Dom | Feature | Normalization | |
---|---|---|---|---|
HOW | WHY | |||
[19] | T | QT interval | To overcome bias engendered by the longer measured QT intervals of paced complexes | |
[20,21,22,23,24,25,26,27,28,29] | F |
| To minimize the effect of changes in total power on LF and HF, emphasizing the cardiac modulation of the autonomic nervous system | |
[30] | T | QT interval | To quantify the interdependence of the instability in QT dynamics and the occurrence of PA | |
[31] | T |
| To assess the effectiveness of parameters in discriminating myocardial infarction patients | |
[32] | T | Voltage spread (VS) | To account for the electrical variance among patients | |
[33,34] | F |
| To account for the signal energy amplitude | |
[35,36,37,38,39,40,41,42] | F |
| To minimize the effect of changes in total power on LF and HF, emphasizing the cardiac modulation of the autonomic nervous system | |
[43,44,45] | T | QT variability index | To compensate for the effect of HRV | |
[46] | T |
| To compensate for the effect of HR | |
[47] | T | R-wave amplitude (Ramp) | To consider correlation between Ramp and electrode distance | |
[48] | T |
| To see if normalized features can improve the accuracy of classification | |
[49] | T | TWA | To take into account possible changes of T-wave morphology | |
[50] | T | QT variability index | To make QT interval variations independent from HRV | |
[51] | T | QT interval | To compensate for RR-interval duration | |
[52] | T | Entropy | To account for the effect of the system’s energy | |
[53] | T | HR | To evaluate if cardiac responses exceed HR recommended thresholds | |
[54] | T | ECG leads | To see if the normalized index improves diagnostic accuracy | |
[55] | F |
| To compensate for the effect of aging-related processes | |
[56] | T | Total cosine R-to-T (TCRT) | To compensate for the fact that the parameter is HR-dependent | |
[57] | T |
| To assess the effect of stimulation on TWA |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Iammarino, E.; Marcantoni, I.; Sbrollini, A.; Morettini, M.; Burattini, L. Normalization of Electrocardiogram-Derived Cardiac Risk Indices: A Scoping Review of the Open-Access Literature. Appl. Sci. 2024, 14, 9457. https://doi.org/10.3390/app14209457
Iammarino E, Marcantoni I, Sbrollini A, Morettini M, Burattini L. Normalization of Electrocardiogram-Derived Cardiac Risk Indices: A Scoping Review of the Open-Access Literature. Applied Sciences. 2024; 14(20):9457. https://doi.org/10.3390/app14209457
Chicago/Turabian StyleIammarino, Erica, Ilaria Marcantoni, Agnese Sbrollini, Micaela Morettini, and Laura Burattini. 2024. "Normalization of Electrocardiogram-Derived Cardiac Risk Indices: A Scoping Review of the Open-Access Literature" Applied Sciences 14, no. 20: 9457. https://doi.org/10.3390/app14209457
APA StyleIammarino, E., Marcantoni, I., Sbrollini, A., Morettini, M., & Burattini, L. (2024). Normalization of Electrocardiogram-Derived Cardiac Risk Indices: A Scoping Review of the Open-Access Literature. Applied Sciences, 14(20), 9457. https://doi.org/10.3390/app14209457