Healthy Young POLes – HYPOL database with synchronised beat-to-beat heart rate and blood pressure signals
HYPOL – Cardiovascular Time Series Database
DOI:
https://doi.org/10.20883/medical.e941Keywords:
data sharing, cardiovascular time series, RR intervals, blood pressure, interbeat intervals, healthy young peopleAbstract
Data sharing in medical research entails making research data available to other researchers for review, reuse, and collaboration. This paper seeks to describe the HYPOL (Healthy Young POLes) database, which has been prepared for sharing. This database houses the clinical characteristics and beat-to-beat cardiovascular time series of 278 individuals of Polish descent, all aged between 19 and 30 years. The data were collected from healthy volunteers who participated in multiple projects at the Department of Cardiology-Intensive Therapy research laboratory, Poznan University of Medical Sciences, Poznan, Poland. The cardiovascular time series data was obtained from non-invasive continuous finger blood pressure and ECG recordings, with sessions lasting up to 45 minutes. The HYPOL database includes an xls file detailing the main clinical characteristics and text files that capture ECG-derived RR intervals, finger systolic, diastolic, and mean blood pressure values, as well as the duration of interbeat intervals.
The data is from 149 women (53.6% of the total) and 129 men. The median age of all participants studied was 24 years, their BMI was <24 kg/m2, pulse rate and blood pressure were average. The median duration of the recordings was almost 30 minutes. In addition, we summarise selected parameters of heart rate variability (HRV) and heart rate asymmetry (HRA).
The HYPOL database is available at hypol.ump.edu.pl. The download of data is free after simple registration. Researchers and engineers can use the database to test various mathematical algorithms for HRV, HRA, blood pressure variability and asymmetry, and baroreflex function, except for selling it.
Downloads
References
Ohmann C, Banzi R, Canham S, Battaglia S, Matei M, Ariyo C, Becnel L, Bierer B, Bowers S, Clivio L, Dias M, Druml C, Faure H, Fenner M, Galvez J, Ghersi D, Gluud C, Groves T, Houston P, Karam G, Kalra D, Knowles RL, Krleža-Jerić K, Kubiak C, Kuchinke W, Kush R, Lukkarinen A, Marques PS, Newbigging A, O'Callaghan J, Ravaud P, Schlünder I, Shanahan D, Sitter H, Spalding D, Tudur-Smith C, van Reusel P, van Veen EB, Visser GR, Wilson J, Demotes-Mainard J. Sharing and reuse of individual participant data from clinical trials: principles and recommendations. BMJ Open. 2017;7:e018647. doi: 10.1136/bmjopen-2017-018647 DOI: https://doi.org/10.1136/bmjopen-2017-018647
Tenopir C, Allard S, Douglass K, Aydinoglu AU, Wu L, Read E, Manoff M, Frame M. Data sharing by scientists: practices and perceptions. PLoS One. 2011;6:e21101. doi: 10.1371/journal.pone.0021101 DOI: https://doi.org/10.1371/journal.pone.0021101
Popkin G. Data sharing and how it can benefit your scientific career. Nature. 2019;569:445-447. doi: 10.1038/d41586-019-01506-x DOI: https://doi.org/10.1038/d41586-019-01506-x
Tenopir C, Rice NM, Allard S, Baird L, Borycz J, Christian L, Grant B, Olendorf R, Sandusky RJ. Data sharing, management, use, and reuse: Practices and perceptions of scientists worldwide. PLoS One. 2020;15:e0229003. doi: 10.1371/journal.pone.0229003 DOI: https://doi.org/10.1371/journal.pone.0229003
Johnson AE, Pollard TJ, Shen L, Lehman LW, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA, Mark RG. MIMIC-III, a freely accessible critical care database. Sci Data. 2016;3:160035. doi: 10.1038/sdata.2016.35 DOI: https://doi.org/10.1038/sdata.2016.35
Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG, Badawi O. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Sci Data. 2018;5:180178. doi: 10.1038/sdata.2018.178 DOI: https://doi.org/10.1038/sdata.2018.178
Young T, Shahar E, Nieto FJ, Redline S, Newman AB, Gottlieb DJ, Walsleben JA, Finn L, Enright P, Samet JM; Sleep Heart Health Study Research Group. Predictors of sleep-disordered breathing in community-dwelling adults: the Sleep Heart Health Study. Arch Intern Med. 2002;162:893-900. doi: 10.1001/archinte.162.8.893 DOI: https://doi.org/10.1001/archinte.162.8.893
Wang L, Zhou X. Detection of Congestive Heart Failure Based on LSTM-Based Deep Network via Short-Term RR Intervals. Sensors (Basel). 2019;19:1502. doi: 10.3390/s19071502 DOI: https://doi.org/10.3390/s19071502
Papini GB, Fonseca P, Margarito J, van Gilst MM, Overeem S, Bergmans JWM, Vullings R. On the generalizability of ECG-based obstructive sleep apnea monitoring: merits and limitations of the Apnea-ECG database. Annu Int Conf IEEE Eng Med Biol Soc. 2018;2018:6022-6025. doi: 10.1109/EMBC.2018.8513660 DOI: https://doi.org/10.1109/EMBC.2018.8513660
Costa M, Moody GB, Henry I, Goldberger AL. PhysioNet: an NIH research resource for complex signals. J Electrocardiol. 2003;36 Suppl:139-44. doi: 10.1016/j.jelectrocard.2003.09.038 DOI: https://doi.org/10.1016/j.jelectrocard.2003.09.038
Moody GB, Mark RG, Goldberger AL. PhysioNet: physiologic signals, time series and related open source software for basic, clinical, and applied research. Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:8327-30. doi: 10.1109/IEMBS.2011.6092053 DOI: https://doi.org/10.1109/IEMBS.2011.6092053
Ghassemi MM, Moody BE, Lehman LH, Song C, Li Q, Sun H, Mark RG, Westover MB, Clifford GD. You Snooze, You Win: the PhysioNet/Computing in Cardiology Challenge 2018;45:10.22489/cinc.2018.049. doi: 10.22489/cinc.2018.049 DOI: https://doi.org/10.22489/CinC.2018.049
Hong S, Zhang W, Sun C, Zhou Y, Li H. Practical Lessons on 12-Lead ECG Classification: Meta-Analysis of Methods From PhysioNet/Computing in Cardiology Challenge 2020. Front Physiol. 2022;12:811661. doi: 10.3389/fphys.2021.811661 DOI: https://doi.org/10.3389/fphys.2021.811661
Buś S, Jędrzejewski K, Guzik P. A New Approach to Detecting Atrial Fibrillation Using Count Statistics of Relative Changes between Consecutive RR Intervals. J Clin Med. 2023;12:687. doi: 10.3390/jcm12020687 DOI: https://doi.org/10.3390/jcm12020687
Buś S, Jędrzejewski K, Guzik P. Statistical and Diagnostic Properties of pRRx Parameters in Atrial Fibrillation Detection. J Clin Med. 2022;11:5702. doi: 10.3390/jcm11195702 DOI: https://doi.org/10.3390/jcm11195702
Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Eur Heart J. 1996;17:354-81. PMID: 8737210.
Sassi R, Cerutti S, Lombardi F, Malik M, Huikuri HV, Peng CK, Schmidt G, Yamamoto Y. Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society. Europace. 2015;17:1341-53. doi: 10.1093/europace/euv015 DOI: https://doi.org/10.1093/europace/euv015
Parati G, Bilo G, Kollias A, Pengo M, Ochoa JE, Castiglioni P, Stergiou GS, Mancia G, Asayama K, Asmar R, Avolio A, Caiani EG, De La Sierra A, Dolan E, Grillo A, Guzik P, Hoshide S, Head GA, Imai Y, Juhanoja E, Kahan T, Kario K, Kotsis V, Kreutz R, Kyriakoulis KG, Li Y, Manios E, Mihailidou AS, Modesti PA, Omboni S, Palatini P, Persu A, Protogerou AD, Saladini F, Salvi P, Sarafidis P, Torlasco C, Veglio F, Vlachopoulos C, Zhang Y. Blood pressure variability: methodological aspects, clinical relevance and practical indications for management - a European Society of Hypertension position paper. J Hypertens. 2023;41:527-544. doi: 10.1097/HJH.0000000000003363 DOI: https://doi.org/10.1097/HJH.0000000000003363
Guzik P, Piskorski J. Asymmetric properties of heart rate microstructure. J. Med. Sci. 2020;89:e436. doi: 10.20883/medical.e436 DOI: https://doi.org/10.20883/medical.e436
Costa MD, Davis RB, Goldberger AL. Heart Rate Fragmentation: A New Approach to the Analysis of Cardiac Interbeat Interval Dynamics. Front Physiol. 2017;8:255. doi: 10.3389/fphys.2017.00255 DOI: https://doi.org/10.3389/fphys.2017.00255
Guzik P, Wykretowicz A, Wesseling IK, Wysocki H. Adrenal pheochromocytoma associated with dramatic cyclic hemodynamic fluctuations. Int J Cardiol. 2005;103:351-3. doi: 10.1016/j.ijcard.2004.08.071 DOI: https://doi.org/10.1016/j.ijcard.2004.08.071
Guzik P, Piskorski J, Ellert J, Krauze T. Asymmetry of haemodynamic variability in healthy people, 2014 8th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO), Trento, Italy, 2014, pp. 129-130, doi: 10.1109/ESGCO.2014.6847553 DOI: https://doi.org/10.1109/ESGCO.2014.6847553
Guzik P, Piskorski J, Krauze T, Schneider R, Wesseling KH, Wykretowicz A, Wysocki H. Correlations between the Poincaré plot and conventional heart rate variability parameters assessed during paced breathing. J Physiol Sci. 2007;57:63-71. doi: 10.2170/physiolsci.RP005506 DOI: https://doi.org/10.2170/physiolsci.RP005506
Sesso HD, Stampfer MJ, Rosner B, Hennekens CH, Gaziano JM, Manson JE, Glynn RJ. Systolic and diastolic blood pressure, pulse pressure, and mean arterial pressure as predictors of cardiovascular disease risk in Men. Hypertension. 2000;36:801-7. doi: 10.1161/01.hyp.36.5.801 DOI: https://doi.org/10.1161/01.HYP.36.5.801
Kim DH, Shin S, Kim JY, Kim SH, Jo M, Choi YS. Pulse pressure variation and pleth variability index as predictors of fluid responsiveness in patients undergoing spinal surgery in the prone position. Ther Clin Risk Manag. 2018;14:1175-1183. doi: 10.2147/TCRM.S170395 DOI: https://doi.org/10.2147/TCRM.S170395
Geeganage C, Tracy M, England T, Sare G, Moulin T, Woimant F, Christensen H, De Deyn PP, Leys D, O'Neill D, Ringelstein EB, Bath PM; for TAIST Investigators. Relationship between baseline blood pressure parameters (including mean pressure, pulse pressure, and variability) and early outcome after stroke: data from the Tinzaparin in Acute Ischaemic Stroke Trial (TAIST). Stroke. 2011;42:491-3. doi: 10.1161/STROKEAHA.110.596163 DOI: https://doi.org/10.1161/STROKEAHA.110.596163
Maïer B, Turc G, Taylor G, Blanc R, Obadia M, Smajda S, Desilles JP, Redjem H, Ciccio G, Boisseau W, Sabben C, Ben Machaa M, Hamdani M, Leguen M, Gayat E, Blacher J, Lapergue B, Piotin M, Mazighi M; Endovascular Treatment in Ischemic Stroke (ETIS) Investigators. Prognostic Significance of Pulse Pressure Variability During Mechanical Thrombectomy in Acute Ischemic Stroke Patients. J Am Heart Assoc. 2018;7:e009378. doi: 10.1161/JAHA.118.009378 DOI: https://doi.org/10.1161/JAHA.118.009378
Krauze T, Liberkowska M, Adamska K, Turska E, Wykrętowicz A, Guzik P. Acute hemodynamic effects of salted potato chips in healthy people. Pol Arch Intern Med. 2019;129:721-724. doi: 10.20452/pamw.14935 DOI: https://doi.org/10.20452/pamw.14935
Obeid H, Tairi A, Fortier C, Giudici A, Spronck B, Agharazii M. Carotid-femoral pulse wave velocity variability: beat-to-beat assessment. J Hypertens. 2023;41(Suppl 3):e268. doi: 10.1097/01.hjh.0000941768.60309.c1 DOI: https://doi.org/10.1097/01.hjh.0000941768.60309.c1
Svačinová J, Hrušková J, Jakubík J, Budinskaya K, Hidegová S, Fabšík M, Sieglová H, Kaščáková Z, Novák J, Nováková Z. Variability of peripheral pulse wave velocity in patients with diabetes mellitus type 2 during orthostatic challenge. Physiol Res. 2020;69(Suppl 3):S433-S441. doi: 10.33549/physiolres.934594 DOI: https://doi.org/10.33549/physiolres.934594
Triedman JK, Saul JP. Blood pressure modulation by central venous pressure and respiration. Buffering effects of the heart rate reflexes. Circulation. 1994;89:169-79. doi: 10.1161/01.cir.89.1.169 DOI: https://doi.org/10.1161/01.CIR.89.1.169
Zareba W, Bayes de Luna A. QT dynamics and variability. Ann Noninvasive Electrocardiol. 2005;10:256-62. doi: 10.1111/j.1542-474X.2005.10205.x DOI: https://doi.org/10.1111/j.1542-474X.2005.10205.x
Guzik P, Zuchowski B, Blaszyk K, Seniuk W, Wasniewski M, Gwizdala A, Wykretowicz A, Piskorski J. Asymmetry of the variability of heart rate and conduction time between atria and ventricles. Circ J. 2013;77:2904-11. doi: 10.1253/circj.cj-13-0461 DOI: https://doi.org/10.1253/circj.CJ-13-0461
Żuchowski B, Błaszyk K, Piskorski J, Wykrętowicz A, Guzik P. Dependence of the Atrioventricular Conduction Time on the Conduction through the Atrioventricular Node and His-Purkinje System. J Clin Med. 2023;12:1330. doi: 10.3390/jcm12041330 DOI: https://doi.org/10.3390/jcm12041330
Guzik P, Piskorski J, Barthel P, Bauer A, Müller A, Junk N, Ulm K, Malik M, Schmidt G. Heart rate deceleration runs for postinfarction risk prediction. J Electrocardiol. 2012;45:70-6. doi: 10.1016/j.jelectrocard.2011.08.006 DOI: https://doi.org/10.1016/j.jelectrocard.2011.08.006
Bauer A, Guzik P, Barthel P, Schneider R, Ulm K, Watanabe MA, Schmidt G. Reduced prognostic power of ventricular late potentials in post-infarction patients of the reperfusion era. Eur Heart J. 2005;26:755-61. doi: 10.1093/eurheartj/ehi101 DOI: https://doi.org/10.1093/eurheartj/ehi101
Bishop SA, Dech RT, Guzik P, Neary JP. Heart rate variability and implication for sport concussion. Clin Physiol Funct Imaging. 2018;38:733-742. doi: 10.1111/cpf.12487 DOI: https://doi.org/10.1111/cpf.12487
Kaczmarek LD, Behnke M, Enko J, Kosakowski M, Hughes BM, Piskorski J, Guzik P. Effects of emotions on heart rate asymmetry. Psychophysiology. 2019;56:e13318. doi: 10.1111/psyp.13318 DOI: https://doi.org/10.1111/psyp.13318
Kaczmarek LD, Behnke M, Kosakowski M, Enko J, Dziekan M, Piskorski J, Hughes BM, Guzik P. High-approach and low-approach positive affect influence physiological responses to threat and anger. Int J Psychophysiol. 2019 ;138:27-37. doi: 10.1016/j.ijpsycho.2019.01.008 DOI: https://doi.org/10.1016/j.ijpsycho.2019.01.008
Guzik P, Piekos C, Pierog O, Fenech N, Krauze T, Piskorski J, Wykretowicz A. Classic electrocardiogram-based and mobile technology derived approaches to heart rate variability are not equivalent. Int J Cardiol. 2018;258:154-156. doi: 10.1016/j.ijcard.2018.01.056 DOI: https://doi.org/10.1016/j.ijcard.2018.01.056
Guzik P, Piskorski J, Krauze T, Wykretowicz A, Wysocki H. Heart rate asymmetry by Poincaré plots of RR intervals. Biomed Tech (Berl). 2006;51:272-5. doi: 10.1515/BMT.2006.054 DOI: https://doi.org/10.1515/BMT.2006.054
Piskorski J, Guzik P. Geometry of the Poincaré plot of RR intervals and its asymmetry in healthy adults. Physiol Meas. 2007;28:287-300. doi: 10.1088/0967-3334/28/3/005 DOI: https://doi.org/10.1088/0967-3334/28/3/005
Piskorski J, Guzik P. Asymmetric properties of long-term and total heart rate variability. Med Biol Eng Comput. 2011;49:1289-97. doi: 10.1007/s11517-011-0834-z DOI: https://doi.org/10.1007/s11517-011-0834-z
Piskorski J, Guzik P. The structure of heart rate asymmetry: deceleration and acceleration runs. Physiol Meas. 2011;32:1011-23. doi: 10.1088/0967-3334/32/8/002 DOI: https://doi.org/10.1088/0967-3334/32/8/002
Piskorski J, Kośmider M, Mieszkowski D, Żurek S, Biczuk B, Jurga S, Krauze T, Wykrętowicz A, Guzik P. Associations between heart rate asymmetry expression and asymmetric detrended fluctuation analysis results. Med Biol Eng Comput. 2022;60:2969-2979. doi: 10.1007/s11517-022-02645-6 DOI: https://doi.org/10.1007/s11517-022-02645-6
Piskorski J, Ellert J, Krauze T, Grabowski W, Wykretowicz A, Guzik P. Testing heart rate asymmetry in long, nonstationary 24 hour RR-interval time series. Physiol Meas. 2019;40:105001. doi: 10.1088/1361-6579/ab42d5 DOI: https://doi.org/10.1088/1361-6579/ab42d5
Sibrecht G, Piskorski J, Krauze T, Guzik P. Heart Rate Asymmetry, Its Compensation, and Heart Rate Variability in Healthy Adults during 48-h Holter ECG Recordings. J Clin Med. 2023;12:1219. doi: 10.3390/jcm12031219 DOI: https://doi.org/10.3390/jcm12031219
Zalas D, Bobkowski W, Piskorski J, Guzik P. Heart Rate Asymmetry in Healthy Children. J Clin Med. 2023;12:1194. doi: 10.3390/jcm12031194 DOI: https://doi.org/10.3390/jcm12031194
Piskorski J, Guzik P. Compensatory properties of heart rate asymmetry. J Electrocardiol. 2012;45:220-4. doi: 10.1016/j.jelectrocard.2012.02.001 DOI: https://doi.org/10.1016/j.jelectrocard.2012.02.001
Guzik P, Piskorski J, Krauze T, Narkiewicz K, Wykretowicz A, Wysocki H. Asymmetric features of short-term blood pressure variability. Hypertens Res. 2010;33:1199-205. doi: 10.1038/hr.2010.138 DOI: https://doi.org/10.1038/hr.2010.138
Parati G, Di Rienzo M, Mancia G. How to measure baroreflex sensitivity: from the cardiovascular laboratory to daily life. J Hypertens. 2000;18:7-19. PMID: 10678538. DOI: https://doi.org/10.1097/00004872-200018010-00003
Westerhof BE, Gisolf J, Stok WJ, Wesseling KH, Karemaker JM. Time-domain cross-correlation baroreflex sensitivity: performance on the EUROBAVAR data set. J Hypertens. 2004;22:1371-80. doi: 10.1097/01.hjh.0000125439.28861.ed DOI: https://doi.org/10.1097/01.hjh.0000125439.28861.ed
deBoer RW, Karemaker JM, Strackee J. Hemodynamic fluctuations and baroreflex sensitivity in humans: a beat-to-beat model. Am J Physiol. 1987;253:H680-9. doi: 10.1152/ajpheart.1987.253.3.H680 DOI: https://doi.org/10.1152/ajpheart.1987.253.3.H680
Katarzynska-Szymanska A, Ochotny R, Oko-Sarnowska Z, Wachowiak-Baszynska H, Krauze T, Piskorski J, Gwizdala A, Mitkowski P, Guzik P. Shortening baroreflex delay in hypertrophic cardiomyopathy patients -- an unknown effect of β-blockers. Br J Clin Pharmacol. 2013;75:1516-24. doi: 10.1111/bcp.12027 DOI: https://doi.org/10.1111/bcp.12027
Adamska K, Krauze T, Guzik P, Piskorski J, Klimas K, Wykrętowicz A. Acute cardiovascular responses elicited by consumption of beer in healthy people. Pol Arch Intern Med. 2018;128:400-402. doi: 10.20452/pamw.4266 DOI: https://doi.org/10.20452/pamw.4266
Kubiak KB, Więckowska B, Krauze T, Piskorski J, Guzik P. Detection of the baroreflex function changes during the 30-minute supine rest by the Poincaré plot-based method, 2022 12th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO), Vysoké Tatry, Štrbské Pleso, Slovakia, 2022, pp. 1-2, doi: 10.1109/ESGCO55423.2022.9931378.
Kubiak KB, Więckowska B, Krauze T, Piskorski J, Guzik P. Detection of the baroreflex function changes during the 30-minute supine rest by the Poincaré plot-based method, 2022 12th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO), Vysoké Tatry, Štrbské Pleso, Slovakia, 2022, pp. 1-2, doi: 10.1109/ESGCO55423.2022.9931378. DOI: https://doi.org/10.1109/ESGCO55423.2022.9931378
Sawicka-Gutaj N, Gruszczyński D, Guzik P, Mostowska A, Walkowiak J. Publication ethics of human studies in the light of the Declaration of Helsinki—A mini-review. J. Med. Sci. 2022, 91, e700. doi: 0000-0001-9052-5027 DOI: https://doi.org/10.20883/medical.e700
Schneider R, Bauer A, Barthel P, Schmidt G. libRASCH: a programming framework for signal handling. Comput in Cardiol. 2004; Chicago, IL, USA, 2004, pp. 53-56, doi: 10.1109/CIC.2004.1442869 DOI: https://doi.org/10.1109/CIC.2004.1442869
Wykretowicz A, Metzler L, Milewska A, Balinski M, Rutkowska A, Adamska K, Krauze T, Guzik P, Dziarmaga M, Wysocki H. Noninvasively assessed pulsatility of ascending aortic pressure waveform is associated with the presence of coronary artery narrowing. Heart Vessels. 2008;23:16-9. doi: 10.1007/s00380-007-1003-z DOI: https://doi.org/10.1007/s00380-007-1003-z
Guzik P, Piskorski J, Krauze T, Wykretowicz A, Wysocki H. Partitioning total heart rate variability. Int J Cardiol. 2010;144:138-9. doi: 10.1016/j.ijcard.2008.12.151 DOI: https://doi.org/10.1016/j.ijcard.2008.12.151
Lomb NR. Least-squares frequency analysis of unequally spaced data. Astrophys Space Sci. 1976;39:447–462. doi: 10.1007/BF00648343 DOI: https://doi.org/10.1007/BF00648343
Moody GB. Spectral analysis of heart rate without resampling. Proceedings of Computers in Cardiology Conference, London, UK, 1993, pp. 715-718, doi: 10.1109/CIC.1993.378302. DOI: https://doi.org/10.1109/CIC.1993.378302
Laguna P, Moody GB, Mark RG. Power spectral density of unevenly sampled data by least-square analysis: performance and application to heart rate signals. IEEE. Trans. Biomed. Eng. 1998;45:698–715. doi: 10.1109/10.678605 DOI: https://doi.org/10.1109/10.678605
Piskorski J, Guzik P, Krauze T, Zurek S. Cardiopulmonary resonance at 0.1 Hz demonstrated by averaged Lomb-Scargle periodogram. Cent. Eur. J. Physics 2010;8:386–392. doi: 10.2478/s11534-009-0101-1 DOI: https://doi.org/10.2478/s11534-009-0101-1
Bobkowski W, Stefaniak ME, Krauze T, Gendera K, Wykretowicz A, Piskorski J, Guzik P. Measures of Heart Rate Variability in 24-h ECGs Depend on Age but Not Gender of Healthy Children. Front Physiol. 2017;8:311. doi: 10.3389/fphys.2017.00311 DOI: https://doi.org/10.3389/fphys.2017.00311
Porta A, Casali KR, Casali AG, Gnecchi-Ruscone T, Tobaldini E, Montano N, Lange S, Geue D, Cysarz D, Van Leeuwen P. Temporal asymmetries of short-term heart period variability are linked to autonomic regulation. Am J Physiol Regul Integr Comp Physiol. 2008;295:R550-7. doi: 10.1152/ajpregu.00129.2008 DOI: https://doi.org/10.1152/ajpregu.00129.2008
Guzik P, Więckowska B. Data distribution analysis—A preliminary approach to quantitative data in biomedical research. J. Med. Sci. 2023;92:e869. doi: 10.20883/medical.e869 DOI: https://doi.org/10.20883/medical.e869
Bus S, Jedrzejewski K, Krauze T, Guzik P. Experimental comparison of photoplethysmography-based atrial fibrillation detection using simple machine learning methods. Proceedings of SPIE - The International Society for Optical Engineering. 2020. DOI: 10.1117/12.2580594
Kotecha D, Chua WWL, Fabritz L, Hendriks J, Casadei B, Schotten U, Vardas P, Heidbuchel H, Dean V, Kirchhof P; European Society of Cardiology (ESC) Atrial Fibrillation Guidelines Taskforce, the CATCH ME consortium and the European Heart Rhythm Association (EHRA). European Society of Cardiology smartphone and tablet applications for patients with atrial fibrillation and their health care providers. Europace. 2018;20:225-233. doi: 10.1093/europace/eux299 DOI: https://doi.org/10.1093/europace/eux299
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 The copyright to the submitted manuscript is held by the Author, who grants the Journal of Medical Science (JMS) a nonexclusive licence to use, reproduce, and distribute the work, including for commercial purposes.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Accepted 2023-11-12
Published 2023-11-13
