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arxiv: 2606.26816 · v1 · pith:6IH5T5QFnew · submitted 2026-06-25 · 💻 cs.AI

Computational Analysis of Heart Rate Variability in Healthy Adults

Mar\'ia J. Lado , Arturo J. M\'endez , Leandro Rodriguez-Li\~nares , Baltasar Garc\'ia P\'erez-Schofield , Pedro Cuesta-Morales , Brais Iglesias-Otero , Xose A. Vila This is my paper

Pith reviewed 2026-06-26 05:19 UTC · model grok-4.3

classification 💻 cs.AI
keywords heart rate variabilityHRV indicestime domainfrequency domainnonlinear analysishealthy adultsreproducibilityclinical utility
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The pith

Specific HRV indices best represent global, low-frequency and high-frequency components in healthy adults.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper evaluates heart rate variability indices in 40 healthy adults aged 30-50 to identify which ones accurately capture cardiac physiological state. It examines five properties: whether the indices follow normal distributions, remain stable over time, correlate with each other, reproduce when checked against the Fantasia database, and show consistency across studies. Time-domain and nonlinear indices generally follow normal distributions and exhibit stability, while frequency-domain indices display high variability and some HF-related ones are highly correlated, indicating redundancy. Reproducibility errors stay under 10 percent for most indices. The authors conclude that ApEn and IRRR for global variability, HRVi and SD2 for low frequency, and MADRR or rMSSD for high frequency form the best set for clinical and research use.

Core claim

The study establishes that time-domain and nonlinear HRV indices, particularly those for global and low-frequency components, follow normal distributions with some gender differences, remain stable except for high-frequency related measures, show high correlations among HF indices that suggest selecting only one, reproduce with less than 10 percent error against the Fantasia database for most cases, and exhibit low inter-study variability unlike frequency-domain indices. It identifies ApEn and IRRR for global variability, HRVi and SD2 for LF, and MADRR or rMSSD for HF as the indices best suited to represent HRV components and enhance clinical and research relevance.

What carries the argument

Computational evaluation of time-domain, frequency-domain, and nonlinear HRV indices from ECG recordings in 40 healthy adults, testing the five properties of normality, stability, correlation, reproducibility, and consistency.

If this is right

  • Time-domain and nonlinear indices can be used reliably due to their normal distributions and stability.
  • Only one high-frequency index is needed in analyses because of high correlations indicating redundancy.
  • Frequency-domain indices should be interpreted cautiously for cross-study comparisons given their high variability.
  • The recommended set of indices increases the clinical utility of HRV for disease diagnosis.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The gender differences in distributions could support development of separate reference ranges for men and women.
  • These indices might be tested in wearable monitoring systems to check if they improve real-time cardiac assessment.
  • Applying the same evaluation to patient groups with cardiac conditions could identify whether the same indices remain optimal.

Load-bearing premise

The sample of 40 healthy adults aged 30-50 provides sufficient statistical power and representativeness to generalize properties of HRV indices.

What would settle it

A larger study of healthy adults aged 30-50 that finds different normality results or error rates above 10 percent for the recommended indices when compared to the Fantasia database would falsify the selection.

Figures

Figures reproduced from arXiv: 2606.26816 by Arturo J. M\'endez, Baltasar Garc\'ia P\'erez-Schofield, Brais Iglesias-Otero, Leandro Rodriguez-Li\~nares, Mar\'ia J. Lado, Pedro Cuesta-Morales, Xose A. Vila.

Figure 1
Figure 1. Figure 1: Differences in HRV indices from our work and from the Fantasia database. [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Mean ± standard deviation of time-domain and non-linear HRV parameters reported in five papers meeting [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
read the original abstract

Heart Rate Variability (HRV) analysis is a key indicator of cardiac physiological state and aids in disease diagnosis. However, research on HRV parameters in healthy individuals remains limited, and no gold standard exists. This study evaluates HRV indices in 40 healthy adults (20 men, 20 women, aged 30-50) to improve HRV's clinical utility. Using computational methods for signal processing and data analysis, time, frequency, and nonlinear indices were analyzed to address five questions: (1) normality, (2) stability, (3) correlation, (4) reproducibility, and (5) consistency. Key findings: (1) Time-domain and nonlinear indices, particularly global and LF (low frequency), follow normal distributions, with gender differences noted. (2) Most indices are stable except HF (high frequency)-related ones. (3) High correlations in HF-related indices suggest redundancy, indicating only one is necessary in studies. (4) Comparisons with the Fantasia database revealed less than 10% error for most indices, except SD2 and SDNN in women (greater than 15%). (5) Time-domain and nonlinear indices show low inter-study variability, while frequency-domain indices exhibit high variability, limiting cross-study comparisons. The selected indices-ApEn and IRRR (global variability), HRVi and SD2 (LF), and MADRR or rMSSD (HF)-are best suited for accurately representing HRV components and enhancing its clinical and research relevance.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The manuscript presents a computational analysis of heart rate variability (HRV) indices in 40 healthy adults (20 men, 20 women, aged 30-50). It evaluates time-, frequency-, and nonlinear-domain indices against five criteria—normality of distributions, stability, inter-index correlations, reproducibility versus the Fantasia database (<10% error for most indices, >15% for SD2/SDNN in women), and cross-study consistency—and concludes that ApEn and IRRR (global), HRVi and SD2 (LF), and MADRR or rMSSD (HF) are the indices best suited for clinical and research use.

Significance. If the empirical findings hold after methodological clarification, the work could offer practical guidance for standardizing HRV index selection by highlighting indices with favorable statistical properties (normality, low inter-study variability). The computational signal-processing pipeline is a positive aspect, but the absence of detailed statistical procedures and the small cohort limit the strength of the generalizability claim.

major comments (3)
  1. [Abstract] Abstract: the abstract states specific quantitative findings such as normality distributions, <10% error for most indices, and >15% for SD2/SDNN in women, but provides no details on statistical tests, data exclusion rules, or error bar calculations; full methods absent.
  2. [Abstract] Abstract (study design): the gender-stratified analyses (n=20 per group) are used to support claims about normality (Shapiro-Wilk), correlations, and reproducibility error rates; this sample size is marginal for reliable inference and generalization of HRV index properties.
  3. [Abstract] Abstract (reproducibility): the Fantasia database serves as the external benchmark for the <10% error threshold, yet differences in age range and recording conditions are not addressed, weakening the interpretation of reproducibility and the exceptions noted for women.
minor comments (1)
  1. [Abstract] Abstract: acronyms for the recommended indices (ApEn, IRRR, HRVi, SD2, MADRR, rMSSD) are introduced without prior definition, reducing immediate clarity for readers outside the HRV subfield.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment below and indicate the revisions made to strengthen the presentation of methods, limitations, and reproducibility analysis.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the abstract states specific quantitative findings such as normality distributions, <10% error for most indices, and >15% for SD2/SDNN in women, but provides no details on statistical tests, data exclusion rules, or error bar calculations; full methods absent.

    Authors: The full manuscript includes a Methods section specifying the Shapiro-Wilk test for normality, correlation procedures, percentage-error reproducibility metric against Fantasia, and preprocessing steps. The abstract summarizes results at a high level. We will revise the abstract to include a brief reference to the primary statistical tests employed and ensure all quantitative claims are traceable to the detailed methods. revision: yes

  2. Referee: [Abstract] Abstract (study design): the gender-stratified analyses (n=20 per group) are used to support claims about normality (Shapiro-Wilk), correlations, and reproducibility error rates; this sample size is marginal for reliable inference and generalization of HRV index properties.

    Authors: We agree that n=20 per gender is modest and limits statistical power for generalization. The cohort was assembled for an exploratory computational analysis of index properties rather than definitive normative values. We will add an explicit limitations paragraph discussing sample size, power considerations for the gender-stratified results, and the need for larger validation studies. revision: yes

  3. Referee: [Abstract] Abstract (reproducibility): the Fantasia database serves as the external benchmark for the <10% error threshold, yet differences in age range and recording conditions are not addressed, weakening the interpretation of reproducibility and the exceptions noted for women.

    Authors: The referee correctly notes that demographic (age) and protocol differences between our cohort and Fantasia were not discussed. We will add a paragraph in the Discussion comparing the datasets and qualifying the reproducibility results, while retaining the practical <10% threshold as an internal benchmark and acknowledging its interpretive limits. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical HRV index evaluation

full rationale

This paper is a purely empirical data-processing study. It computes standard HRV indices (time-domain, frequency-domain, nonlinear) directly from ECG signals recorded in 40 healthy adults, applies statistical tests for normality/stability/correlation/reproducibility/consistency, and compares results to the external Fantasia database. No mathematical derivations, fitted parameters presented as predictions, ansatzes, or self-citation chains appear in the load-bearing steps. All claims rest on direct computation and external benchmarks, so the analysis is self-contained with no reduction of outputs to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The analysis rests on established HRV index definitions and standard statistical procedures from prior literature; no free parameters are fitted, no new entities postulated, and no ad-hoc axioms beyond routine assumptions of the signal-processing domain.

axioms (1)
  • domain assumption Standard statistical tests for normality, stability, and correlation are appropriate and correctly applied to the computed HRV indices.
    Invoked implicitly when reporting normality, stability, and redundancy findings without specifying exact test procedures or corrections.

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Reference graph

Works this paper leans on

49 extracted references · 39 canonical work pages

  1. [1]

    Inteligencia artificial en la salud mental: oportunidades, dificultades y cuestionamientos

    Canabal A, Delgado EA, Alonso González M. Inteligencia artificial en la salud mental: oportunidades, dificultades y cuestionamientos. Inteligencia Artificial 1(2): 1–20, 2024. doi: 10.4114/intartif.vol27iss74pp133-151

    work page doi:10.4114/intartif.vol27iss74pp133-151 2024

  2. [2]

    Deep learning for healthcare applications based on physiological signals: A review

    Faust O, Hagiwara Y , Hong TJ, Lih OS, Acharya UR. Deep learning for healthcare applications based on physiological signals: A review. Comput Methods Programs Biomed 161:1–13, 2018. doi: 10.1016/j.cmpb.2018.04.005

    work page doi:10.1016/j.cmpb.2018.04.005 2018

  3. [3]

    Principles of clinical electrocardiography, Lange Medical Publications, 1986

    Goldman MJ. Principles of clinical electrocardiography, Lange Medical Publications, 1986

    1986

  4. [4]

    Heart rate variability

    Malik M, Bigger JT, Camm AJ, Kleiger RE, Malliani A, Moss AJ, Schwartz PJ. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use, Eur Heart J, 17:354-381, 1996. doi: 10.1093/oxfordjournals.eurheartj.a014868

    work page doi:10.1093/oxfordjournals.eurheartj.a014868 1996

  5. [5]

    Components of heart rate variability--what they really mean and what we really measure, Am J Cardiol, 72:821-822, 1993

    Malik M, Camm AJ. Components of heart rate variability--what they really mean and what we really measure, Am J Cardiol, 72:821-822, 1993. doi: 10.1016/0002-9149(93)91070-x

    work page doi:10.1016/0002-9149(93)91070-x 1993

  6. [6]

    Heart rate variability analysis with the R package RHRV , Springer, 2017

    García-Martínez CA, Otero-Quintana A, Vila XA, Lado MJ, Rodríguez-Liñares L, Rodríguez- Presedo JM, Méndez AJ. Heart rate variability analysis with the R package RHRV , Springer, 2017

    2017

  7. [7]

    An Overview of Heart Rate Variability Metrics and Norms, Front Public Health, 5:258, 2017

    Shaffer F, Ginsberg JP. An Overview of Heart Rate Variability Metrics and Norms, Front Public Health, 5:258, 2017. doi: 10.3389/fpubh.2017.00258

    work page doi:10.3389/fpubh.2017.00258 2017

  8. [8]

    Heart rate variability and obstructive sleep apnea: Current perspectives and novel technologies, J Sleep Res , 30:e13274,

    Ucak S, Dissanayake HU, Sutherland K, de Chazal P, Cistulli PA. Heart rate variability and obstructive sleep apnea: Current perspectives and novel technologies, J Sleep Res , 30:e13274,

  9. [9]

    doi: 10.1111/jsr.13274

    work page doi:10.1111/jsr.13274

  10. [10]

    Determinants of the heart rate variability in type 1 diabetes mellitus, Front Endocrinol , 14:1247054, 2023

    Hajdu M, Garmpis K, Vértes V , V orobcsuk-Varga N, Molnár GA, Hejjel L, Wittmann I, Faludi R. Determinants of the heart rate variability in type 1 diabetes mellitus, Front Endocrinol , 14:1247054, 2023. doi: 10.3389/fendo.2023.1247054

    work page doi:10.3389/fendo.2023.1247054 2023

  11. [11]

    The Role of Heart Rate Variability (HRV) in Different Hypertensive Syndromes, Diagnostics, 13:785, 2023

    Yugar LBT, Yugar-Toledo JC, Dinamarco N, Sedenho-Prado LG, Moreno BVD, Rubio TA, Fattori A, Rodrigues B, Vilela-Martin JF, Moreno H. The Role of Heart Rate Variability (HRV) in Different Hypertensive Syndromes, Diagnostics, 13:785, 2023. doi: 10.3390/diagnostics13040785

    work page doi:10.3390/diagnostics13040785 2023

  12. [12]

    Acute cardiac dysfunction after short-term diesel exhaust particles exposure, Toxicol Lett, 192:349-355, 2010

    Huang CH, Lin LY , Tsai MS, Hsu CY , Chen HW, Wang TD, Chang WT, Cheng TJ, Chen WJ. Acute cardiac dysfunction after short-term diesel exhaust particles exposure, Toxicol Lett, 192:349-355, 2010. doi: 10.1016/j.toxlet.2009.11.008

    work page doi:10.1016/j.toxlet.2009.11.008 2010

  13. [13]

    Analysis of myocardial infarction using discrete wavelet transform, J Med Syst, 34:985-992, 2010

    Jayachandran ES, Joseph KP, Acharya UR. Analysis of myocardial infarction using discrete wavelet transform, J Med Syst, 34:985-992, 2010. doi: 10.1007/s10916-009-9314-5

    work page doi:10.1007/s10916-009-9314-5 2010

  14. [14]

    Practices and Applications of Heart Rate Variability Monitoring in Endurance Athletes, Int J Sports Med, 44:9-19, 2023

    Lundstrom CJ, Foreman NA, Biltz G. Practices and Applications of Heart Rate Variability Monitoring in Endurance Athletes, Int J Sports Med, 44:9-19, 2023. doi: 10.1055/a-1864-9726

    work page doi:10.1055/a-1864-9726 2023

  15. [15]

    Effects of weight loss through lifestyle changes on heart rate variability in overweight and obese patients: A systematic review, Clin Nutr , 41:2577-2586, 2022

    Mattos S, Rabello da Cunha M, Barreto Silva MI, Serfaty F, Tarvainen MP, Klein MRST, Neves MF. Effects of weight loss through lifestyle changes on heart rate variability in overweight and obese patients: A systematic review, Clin Nutr , 41:2577-2586, 2022. doi: 10.1016/j.clnu.2022.09.009

    work page doi:10.1016/j.clnu.2022.09.009 2022

  16. [16]

    Motivated selective attention during political ad processing: The dynamic interplay between emotional ad content and candidate evaluation, Commun Res , 41:119-156, 2014

    Wang Z, Morey AC, Srivastava J. Motivated selective attention during political ad processing: The dynamic interplay between emotional ad content and candidate evaluation, Commun Res , 41:119-156, 2014. doi: 10.1177/0093650212441793 11

    work page doi:10.1177/0093650212441793 2014

  17. [17]

    A preliminary study on the effect of age and heart rate on the short-term heart rate variability, IOSR J Dent Med Sci, 18:24-28, 2019

    Rajkumari R, Keithellakpam S, Devi LS, Srivastava A. A preliminary study on the effect of age and heart rate on the short-term heart rate variability, IOSR J Dent Med Sci, 18:24-28, 2019. doi: 10.9790/0853-1809042428

    work page doi:10.9790/0853-1809042428 2019

  18. [18]

    Gender Differences in Cardiac Chronotropic Control: Implications for Heart Rate Variability Research, Appl Psychophysiol Biofeedback, 47:65-75, 2022

    Williams DP, Joseph N, Gerardo GM, Hill LK, Koenig J, Thayer JF. Gender Differences in Cardiac Chronotropic Control: Implications for Heart Rate Variability Research, Appl Psychophysiol Biofeedback, 47:65-75, 2022. doi: 10.1007/s10484-021-09528-w

    work page doi:10.1007/s10484-021-09528-w 2022

  19. [19]

    Heart rate variability related to effort at work, Appl Ergon, 42:830-838, 2011

    Uusitalo A, Mets T, Martinmäki K, Mauno S, Kinnunen U, Rusko H. Heart rate variability related to effort at work, Appl Ergon, 42:830-838, 2011. doi: 10.1016/j.apergo.2011.01.005

    work page doi:10.1016/j.apergo.2011.01.005 2011

  20. [20]

    Porto AA, Benjamim CJR, Gonzaga LA, Luciano de Almeida M, Bueno Júnior CR, Garner DM, Valenti VE. Caffeine intake and its influences on heart rate variability recovery in healthy active adults after exercise: A systematic review and meta-analysis, Nutr Metab Cardiovasc Dis, 32:1071-1082, 2022. doi: 10.1016/j.numecd.2022.01.015

    work page doi:10.1016/j.numecd.2022.01.015 2022

  21. [21]

    Agreement between three commercially available instruments for measuring short-term heart rate variability, Physiol Meas , 25:1115- 1124, 2004

    Sandercock GR, Shelton C, Bromley P, Brodie DA. Agreement between three commercially available instruments for measuring short-term heart rate variability, Physiol Meas , 25:1115- 1124, 2004. doi: 10.1088/0967-3334/25/5/003

    work page doi:10.1088/0967-3334/25/5/003 2004

  22. [22]

    Statistical heart rate variability analysis for healthy person: Influence of gender and body posture, J Electrocardiol , 79:81-88, 2023

    Kumar P, Das AK, Halder S. Statistical heart rate variability analysis for healthy person: Influence of gender and body posture, J Electrocardiol , 79:81-88, 2023. doi: 10.1016/j.jelectrocard.2023.03.011

    work page doi:10.1016/j.jelectrocard.2023.03.011 2023

  23. [23]

    Determinants and interindividual variation of R-R interval dynamics in healthy middle-aged subjects, Am J Physiol Heart Circ Physiol, 280:H1400-1406, 2001

    Pikkujämsä SM, Mäkikallio TH, Airaksinen KE, Huikuri HV . Determinants and interindividual variation of R-R interval dynamics in healthy middle-aged subjects, Am J Physiol Heart Circ Physiol, 280:H1400-1406, 2001. doi: 10.1152/ajpheart.2001.280.3.H1400

    work page doi:10.1152/ajpheart.2001.280.3.h1400 2001

  24. [24]

    A quantitative systematic review of normal values for short-term heart rate variability in healthy adults, Pacing Clin Electrophysiol , 33:1407-1417,

    Nunan D, Sandercock GR, Brodie DA. A quantitative systematic review of normal values for short-term heart rate variability in healthy adults, Pacing Clin Electrophysiol , 33:1407-1417,

  25. [25]

    doi: 10.1111/j.1540-8159.2010.02841.x

    work page doi:10.1111/j.1540-8159.2010.02841.x 2010

  26. [26]

    Reference values for time- and frequency-domain heart rate variability measures, Heart Rhythm, 13:1309-1316, 2016

    Sammito S, Böckelmann I. Reference values for time- and frequency-domain heart rate variability measures, Heart Rhythm, 13:1309-1316, 2016. doi: 10.1016/j.hrthm.2016.02.006

    work page doi:10.1016/j.hrthm.2016.02.006 2016

  27. [27]

    Reference values of heart rate variability, Heart Rhythm, 14:302-303, 2017

    Bauer A, Camm AJ, Cerutti S, Guzik P, Huikuri H, Lombardi F, Malik M, Peng CK, Porta A, Sassi R, Schmidt G, Schwartz PJ, Stein PK, Yamamoto Y . Reference values of heart rate variability, Heart Rhythm, 14:302-303, 2017. doi: 10.1016/j.hrthm.2016.12.015

    work page doi:10.1016/j.hrthm.2016.12.015 2017

  28. [28]

    New reference values of heart rate variability during ordinary daily activity, Heart Rhythm, 14:304-307, 2017

    Sammito S, Böckelmann I. New reference values of heart rate variability during ordinary daily activity, Heart Rhythm, 14:304-307, 2017. doi: 10.1016/j.hrthm.2016.12.016

    work page doi:10.1016/j.hrthm.2016.12.016 2017

  29. [29]

    Determinants of heart rate variability in the general population: The Lifelines Cohort Study, Heart Rhythm, 15:1552-1558, 2018

    Tegegne BS, Man T, van Roon AM, Riese H, Snieder H. Determinants of heart rate variability in the general population: The Lifelines Cohort Study, Heart Rhythm, 15:1552-1558, 2018. doi: 10.1016/j.hrthm.2018.05.006

    work page doi:10.1016/j.hrthm.2018.05.006 2018

  30. [30]

    Determinants and reference values of short-term heart rate variability in children, Eur J Appl Physiol, 113:1477-1488, 2013

    Michels N, Clays E, De Buyzere M, Huybrechts I, Marild S, Vanaelst B, De Henauw S, Sioen I. Determinants and reference values of short-term heart rate variability in children, Eur J Appl Physiol, 113:1477-1488, 2013. doi: 10.1007/s00421-012-2572-9

    work page doi:10.1007/s00421-012-2572-9 2013

  31. [31]

    Resting heart rate variability in men varying in habitual physical activity, Med Sci Sports Exerc, 32:1894-1901, 2000

    Melanson EL. Resting heart rate variability in men varying in habitual physical activity, Med Sci Sports Exerc, 32:1894-1901, 2000. doi: 10.1097/00005768-200011000-00012

    work page doi:10.1097/00005768-200011000-00012 1901

  32. [32]

    Everything Hertz: methodological issues in short-term frequency-domain HRV , Front Physiol, 5:177, 2014

    Heathers JA. Everything Hertz: methodological issues in short-term frequency-domain HRV , Front Physiol, 5:177, 2014. doi: 10.3389/fphys.2014.00177

    work page doi:10.3389/fphys.2014.00177 2014

  33. [33]

    Laborde S, Mosley E, Thayer JF. Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research - Recommendations for Experiment Planning, Data Analysis, and Data Reporting, Front Psychol, 8:213, 2017. doi: 10.3389/fpsyg.2017.00213 12

    work page doi:10.3389/fpsyg.2017.00213 2017

  34. [34]

    An introduction to heart rate variability: methodological considerations and clinical applications, Front Physiol , 6:55, 2015

    Billman GE, Huikuri HV , Sacha J, Trimmel K. An introduction to heart rate variability: methodological considerations and clinical applications, Front Physiol , 6:55, 2015. doi: 10.3389/fphys.2015.00055

    work page doi:10.3389/fphys.2015.00055 2015

  35. [35]

    Evidence Based Recommendations for Designing Heart Rate Variability Studies, J Med Syst, 43:311, 2019

    Vila XA, Lado MJ, Cuesta-Morales P. Evidence Based Recommendations for Designing Heart Rate Variability Studies, J Med Syst, 43:311, 2019. doi: 10.1007/s10916-019-1437-8

    work page doi:10.1007/s10916-019-1437-8 2019

  36. [36]

    Multiclasificación de arritmias cardíacas usando una red neuronal y la tarjeta MyRio -1900

    Flores -Calero M, Leppe B, Pilla M, Gualsaquí M, Zabala -Blanco D, Albuja A. Multiclasificación de arritmias cardíacas usando una red neuronal y la tarjeta MyRio -1900. Inteligencia Artificial 24(67):129–146, 2021. doi: 10.4114/intartif.vol24iss67pp129-146

    work page doi:10.4114/intartif.vol24iss67pp129-146 1900

  37. [37]

    Available from: https://www.polar.com/en/sensors/h10- heart-rate-sensor

    Polar H10 Heart Rate Sensor, 2023. Available from: https://www.polar.com/en/sensors/h10- heart-rate-sensor

    2023

  38. [38]

    gV ARVI: A graphical software tool for the acquisition of the heart rate in response to external stimuli, Comput Methods Programs Biomed, 132:197-205, 2016

    Vila Blanco N, Rodríguez-Liñares L, Cuesta P, Lado MJ, Méndez AJ, Vila XA. gV ARVI: A graphical software tool for the acquisition of the heart rate in response to external stimuli, Comput Methods Programs Biomed, 132:197-205, 2016. doi: 10.1016/j.cmpb.2016.05.005

    work page doi:10.1016/j.cmpb.2016.05.005 2016

  39. [39]

    Available from: https://www.r-project.org/

    The R Project for Statistical Computing, 2023. Available from: https://www.r-project.org/

    2023

  40. [40]

    Time-frequency analysis of heart-rate variability, IEEE Eng Med Biol Mag, 16:119-126, 1997

    Vila J, Palacios F, Presedo J, Fernández-Delgado M, Felix P, Barro S. Time-frequency analysis of heart-rate variability, IEEE Eng Med Biol Mag, 16:119-126, 1997. doi: 10.1109/51.620503

    work page doi:10.1109/51.620503 1997

  41. [41]

    Welch P. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms, IEEE Trans Audio Electroacoust , 15:70- 73, 1967. doi: 10.1109/TAU.1967.1161901

    work page doi:10.1109/tau.1967.1161901 1967

  42. [42]

    Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests, J Stat Model Analytics, 2:21-33, 2011

    Razali NM, Wah YB. Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests, J Stat Model Analytics, 2:21-33, 2011

    2011

  43. [43]

    Statistics corner: A guide to appropriate use of correlation coefficient in medical research, Malawi Med J, 24:69-71, 2012

    Mukaka MM. Statistics corner: A guide to appropriate use of correlation coefficient in medical research, Malawi Med J, 24:69-71, 2012

    2012

  44. [44]

    Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics, Am J Physiol , 271:R1078-R1084, 1996

    Iyengar N, Peng CK, Morin R, Goldberger AL, Lipsitz LA. Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics, Am J Physiol , 271:R1078-R1084, 1996. doi: 10.1152/ajpregu.1996.271.4.R1078

    work page doi:10.1152/ajpregu.1996.271.4.r1078 1996

  45. [45]

    Goswami, M., Szafer, K., Choudhry, A., Cai, Y ., Li, S., and Dubrawski, A

    Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals, Circulation, 101:E215-E220, 2000. doi: 10.1161/01.cir.101.23.e215

    work page doi:10.1161/01.cir.101.23.e215 2000

  46. [46]

    Fantasia [Film], Walt Disney Animation Studios, 1940

    Disney W, producer. Fantasia [Film], Walt Disney Animation Studios, 1940

    1940

  47. [47]

    Influence of age, gender, body mass index, and functional capacity on heart rate variability in a cohort of subjects without heart disease, Am J Cardiol, 93:381-385, 2004

    Antelmi I, de Paula RS, Shinzato AR, Peres CA, Mansur AJ, Grupi CJ. Influence of age, gender, body mass index, and functional capacity on heart rate variability in a cohort of subjects without heart disease, Am J Cardiol, 93:381-385, 2004. doi: 10.1016/j.amjcard.2003.09.065

    work page doi:10.1016/j.amjcard.2003.09.065 2004

  48. [48]

    Influence of age and gender on autonomic regulation of heart, J Clin Monit Comput, 27:259-264,

    Abhishekh HA, Nisarga P, Kisan R, Meghana A, Chandran S, Trichur Raju, Sathyaprabha TN. Influence of age and gender on autonomic regulation of heart, J Clin Monit Comput, 27:259-264,

  49. [49]

    doi: 10.1007/s10877-012-9424-3 13

    work page doi:10.1007/s10877-012-9424-3