Biomarker-Informed Machine Learning Model of Cognitive Fatigue from a Heart Rate Response Perspective

Kar Fye Alvin Lee, Woon-Seng Gan, Georgios Christopoulos, Kar Fye Alvin Lee, Woon-Seng Gan, Georgios Christopoulos

Abstract

Cognitive fatigue is a psychological state characterised by feelings of tiredness and impaired cognitive functioning arising from high cognitive demands. This paper examines the recent research progress on the assessment of cognitive fatigue and provides informed recommendations for future research. Traditionally, cognitive fatigue is introspectively assessed through self-report or objectively inferred from a decline in behavioural performance. However, more recently, researchers have attempted to explore the biological underpinnings of cognitive fatigue to understand and measure this phenomenon. In particular, there is evidence indicating that the imbalance between sympathetic and parasympathetic nervous activity appears to be a physiological correlate of cognitive fatigue. This imbalance has been indexed through various heart rate variability indices that have also been proposed as putative biomarkers of cognitive fatigue. Moreover, in contrast to traditional inferential methods, there is also a growing research interest in using data-driven approaches to assessing cognitive fatigue. The ubiquity of wearables with the capability to collect large amounts of physiological data appears to be a major facilitator in the growth of data-driven research in this area. Preliminary findings indicate that such large datasets can be used to accurately predict cognitive fatigue through various machine learning approaches. Overall, the potential of combining domain-specific knowledge gained from biomarker research with machine learning approaches should be further explored to build more robust predictive models of cognitive fatigue.

Keywords: biomarker; cognitive fatigue; heart rate variability; machine learning.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
An example of a heartbeat waveform across a 3-second time window. The R-wave peaks are the most positive deflection observed in the waveform. The R-R intervals are the time difference between each R-wave peaks.

References

    1. Boksem M.A.S., Tops M. Mental fatigue: Costs and benefits. Brain Res. Rev. 2008;59:125–139. doi: 10.1016/j.brainresrev.2008.07.001.
    1. Kluger B.M., Krupp L.B., Enoka R.M. Fatigue and fatigability in neurologic illnesses: Proposal for a unified taxonomy. Neurology. 2013;80:409–416. doi: 10.1212/WNL.0b013e31827f07be.
    1. Desmond P.A., Hancock P.A. Active and passive fatigue states. In: Hancock P.A., Desmond P.A., editors. Stress, Workload, and Fatigue. 1st ed. CRC Press; Boca Raton, FL, USA: 2008. pp. 455–465.
    1. Grandjean E. Fatigue in industry. Occup. Environ. Med. 1979;36:175–186. doi: 10.1136/oem.36.3.175.
    1. Job R.F.S., Dalziel J. Defining fatigue as a condition of the organism and distinguishing it from habituation, adaptation, and boredom. In: Hancock P.A., Desmond P.A., editors. Stress, Workload, and Fatigue. 1st ed. CRC Press; Boca Raton, FL, USA: 2008. pp. 466–476.
    1. Subramanyam M., Muralidhara P., Muralidhara P. Mental workload and cognitive fatigue: A study. IUP J. Manag. Res. 2013;12:29–39.
    1. van der Linden D., Frese M., Meijman T.F. Mental fatigue and the control of cognitive processes: Effects on perseveration and planning. Acta Psychol. 2003;113:45–65. doi: 10.1016/S0001-6918(02)00150-6.
    1. Ilies R., Huth M., Ryan A.M., Dimotakis N. Explaining the links between workload, distress, and work–family conflict among school employees: Physical, cognitive, and emotional fatigue. J. Educ. Psychol. 2015;107:1136–1149. doi: 10.1037/edu0000029.
    1. Lim J., Dinges D.F. Sleep deprivation and vigilant attention. Ann. N. Y. Acad. Sci. 2008;1129:305–322. doi: 10.1196/annals.1417.002.
    1. Gergelyfi M., Jacob B., Olivier E., Zénon A. Dissociation between mental fatigue and motivational state during prolonged mental activity. Front. Behav. Neurosci. 2015;9:176. doi: 10.3389/fnbeh.2015.00176.
    1. Holtzer R., Shuman M., Mahoney J.R., Lipton R., Verghese J. Cognitive fatigue defined in the context of attention networks. Neuropsychol. Dev. Cogn. B Aging Neuropsychol. Cogn. 2011;18:108–128. doi: 10.1080/13825585.2010.517826.
    1. Simon J., Takács E., Orosz G., Berki B., Winkler I. Short-term cognitive fatigue effect on auditory temporal order judgments. Exp. Brain Res. 2020;238:305–319. doi: 10.1007/s00221-019-05712-x.
    1. Tanaka M., Ishii A., Watanabe Y. Effects of mental fatigue on brain activity and cognitive performance: A magnetoencephalography study. Anat. Physiol. 2015;5:1–5. doi: 10.4172/2161-0940.S4-002.
    1. Al-Mekhlafi A.A., Isha A.S.N., Naji G.M.A. The relationship between fatigue and driving performance: A review and directions for future research. J. Crit. Rev. 2020;7:134–141. doi: 10.31838/jcr.07.14.24.
    1. Goode J.H. Are pilots at risk of accidents due to fatigue? J. Saf. Res. 2003;34:309–313. doi: 10.1016/S0022-4375(03)00033-1.
    1. Johansson B., Starmark A., Berglund P., Rödholm M., Rönnbäck L. A self-assessment questionnaire for mental fatigue and related symptoms after neurological disorders and injuries. Brain Inj. 2010;24:2–12. doi: 10.3109/02699050903452961.
    1. Chalder T., Berelowitz G., Pawlikowska T., Watts L., Wessely S., Wright D., Wallace E.P. Development of a fatigue scale. J. Psychosom. Res. 1993;37:147–153. doi: 10.1016/0022-3999(93)90081-P.
    1. Greenberg S., Aislinn P., Kirsten D. Development and validation of the fatigue state questionnaire: Preliminary findings. Open Psychol. J. 2016;9:50–65. doi: 10.2174/1874350101609010050.
    1. Haeffel G.J., Howard G.S. Self-report: Psychology’s four-letter word. Am. J. Psychol. 2010;123:181–188. doi: 10.5406/amerjpsyc.123.2.0181.
    1. Penner I.K., Raselli C., Stöcklin M., Opwis K., Kappos L., Calabrese P. The fatigue scale for motor and cognitive functions (FSMC): Validation of a new instrument to assess multiple sclerosis-related fatigue. Mult. Scler. 2009;15:1509–1517. doi: 10.1177/1352458509348519.
    1. Schmidt E.A., Schrauf M., Simon M., Fritzsche M., Buchner A., Kincses W.E. Drivers’ misjudgement of vigilance state during prolonged monotonous daytime driving. Accid. Anal. Prev. 2009;41:1087–1093. doi: 10.1016/j.aap.2009.06.007.
    1. Dorrian J., Roach G.D., Fletcher A., Dawson D. Simulated train driving: Fatigue, self-awareness and cognitive disengagement. Appl. Ergon. 2007;38:155–166. doi: 10.1016/j.apergo.2006.03.006.
    1. Kikuchi Y., Ishii N. The relationship between self-awareness of fatigue symptoms and working conditions in female nurses. Sangyo Eiseigaku Zasshi. 2015;57:230–240. doi: 10.1539/sangyoeisei.E14005.
    1. Brown I.D. Driver fatigue. Hum. Factors. 1994;36:298–314. doi: 10.1177/001872089403600210.
    1. Brown I.D. Prospects for technological countermeasures against driver fatigue. Accid. Anal. Prev. 1997;29:525–531. doi: 10.1016/S0001-4575(97)00032-8.
    1. Anwer S., Li H., Antwi-Afari M.F., Umer W., Wong A.Y.L. Evaluation of physiological metrics as real-time measurement of physical fatigue in construction workers: State-of-the-art review. J. Constr. Eng. Manag. 2021;147:03121001. doi: 10.1061/(ASCE)CO.1943-7862.0002038.
    1. Younis E.M.G., Kanjo E., Chamberlain A. Designing and evaluating mobile self-reporting techniques: Crowdsourcing for citizen science. Pers. Ubiquitous Comput. 2019;23:329–338. doi: 10.1007/s00779-019-01207-2.
    1. Lorist M.M., Bezdan E., ten Caat M., Span M.M., Roerdink J.B.T.M., Maurits N.M. The influence of mental fatigue and motivation on neural network dynamics; an EEG coherence study. Brain Res. 2009;1270:95–106. doi: 10.1016/j.brainres.2009.03.015.
    1. Schwid S.R., Tyler C.M., Scheid E.A., Weinstein A., Goodman A.D., McDermott M.P. Cognitive fatigue during a test requiring sustained attention: A pilot study. Mult. Scler. 2003;9:503–508. doi: 10.1191/1352458503ms946oa.
    1. Tanaka M., Ishii A., Watanabe Y. Neural effects of mental fatigue caused by continuous attention load: A magnetoencephalography study. Brain Res. 2014;1561:60–66. doi: 10.1016/j.brainres.2014.03.009.
    1. Stroop J.R. Studies of interference in serial verbal reactions. J. Exp. Psychol. 1935;18:643–662. doi: 10.1037/h0054651.
    1. Simon J.R., Wolf J.D. Choice reaction time as a function of angular stimulus-response correspondence and age. Ergonomics. 1963;6:99–105. doi: 10.1080/00140136308930679.
    1. Wang C., Ding M., Kluger B.M. Change in intraindividual variability over time as a key metric for defining performance-based cognitive fatigability. Brain Cogn. 2014;85:251–258. doi: 10.1016/j.bandc.2014.01.004.
    1. Wylie G.R., Genova H.M., DeLuca J., Dobryakova E. The relationship between outcome prediction and cognitive fatigue: A convergence of paradigms. Cogn. Affect. Behav. Neurosci. 2017;17:838–849. doi: 10.3758/s13415-017-0515-y.
    1. Liu J.-P., Zhang C., Zheng C.-X. Estimation of the cortical functional connectivity by directed transfer function during mental fatigue. Appl. Ergon. 2010;42:114–121. doi: 10.1016/j.apergo.2010.05.008.
    1. O’Keeffe K., Hodder S., Lloyd A. A comparison of methods used for inducing mental fatigue in performance research: Individualised, dual-task and short duration cognitive tests are most effective. Ergonomics. 2020;63:1–12. doi: 10.1080/00140139.2019.1687940.
    1. Zhou F., Alsaid A., Blommer M., Curry R., Swaminathan R., Kochhar D., Talamonti W., Tijerina L., Lei B. Driver fatigue transition prediction in highly automated driving using physiological features. Expert Syst. Appl. 2020;147:113204. doi: 10.1016/j.eswa.2020.113204.
    1. Möckel T., Beste C., Wascher E. The effects of time on task in response selection—An ERP study of mental fatigue. Sci. Rep. 2015;5:10113. doi: 10.1038/srep10113.
    1. Samuel I.B.H., Wang C., Burke S.E., Kluger B., Ding M. Compensatory neural responses to cognitive fatigue in young and older adults. Front. Neural Circuits. 2019;13:12. doi: 10.3389/fncir.2019.00012.
    1. Wang C., Trongnetrpunya A., Samuel I.B.H., Ding M., Kluger B.M. Compensatory neural activity in response to cognitive fatigue. J. Neurosci. 2016;36:3919–3924. doi: 10.1523/JNEUROSCI.3652-15.2016.
    1. Tran Y., Craig A., Craig R., Chai R., Nguyen H. The influence of mental fatigue on brain activity: Evidence from a systematic review with meta-analyses. Psychophysiology. 2020;57:e13554. doi: 10.1111/psyp.13554.
    1. Biomarkers Definitions Working Group Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clin. Pharmacol. Ther. 2001;69:89–95. doi: 10.1067/mcp.2001.113989.
    1. Egelund N. Spectral analysis of heart rate variability as an indicator of driver fatigue. Ergonomics. 1982;25:663–672. doi: 10.1080/00140138208925026.
    1. Fairclough S.H., Venables L., Tattersall A. The influence of task demand and learning on the psychophysiological response. Int. J. Psychophysiol. 2005;56:171–184. doi: 10.1016/j.ijpsycho.2004.11.003.
    1. Li Z., Jiao K., Chen M., Yang Y., Wang C., Qi S. Spectral analysis of heart rate variability as a quantitative indicator of driver mental fatigue. SAE Tech. Pap. 2002:2002-01-0090:1–2002-01-0090:5. doi: 10.4271/2002-01-0090.
    1. Mascord D.J., Heath R.A. Behavioral and physiological indices of fatigue in a visual tracking task. J. Saf. Res. 1992;23:19–25. doi: 10.1016/0022-4375(92)90036-9.
    1. Mizuno K., Tanaka M., Yamaguti K., Kajimoto O., Kuratsune H., Watanabe Y. Mental fatigue caused by prolonged cognitive load associated with sympathetic hyperactivity. Behav. Brain Funct. 2011;7:17. doi: 10.1186/1744-9081-7-17.
    1. Tanaka M., Mizuno K., Tajima S., Sasabe T., Watanabe Y. Central nervous system fatigue alters autonomic nerve activity. Life Sci. 2009;84:235–239. doi: 10.1016/j.lfs.2008.12.004.
    1. Tanaka M., Mizuno K., Yamaguti K., Kuratsune H., Fujii A., Baba H., Matsuda K., Nishimae A., Takesaka T., Watanabe Y. Autonomic nervous alterations associated with daily level of fatigue. Behav. Brain Funct. 2011;7:46. doi: 10.1186/1744-9081-7-46.
    1. Zhang C., Zheng C.-X., Yu X.-L. Automatic recognition of cognitive fatigue from physiological indices by using wavelet packet transform and kernel learning algorithms. Expert Syst. Appl. 2009;36:4664–4671. doi: 10.1016/j.eswa.2008.06.022.
    1. Zhang C., Yu X. Estimating mental fatigue based on electroencephalogram and heart rate variability. Pol. J. Med. Phys. Eng. 2010;16:67–84. doi: 10.2478/v10013-010-0007-7.
    1. McCorry L.K. Physiology of the autonomic nervous system. Am. J. Pharm. Educ. 2007;71:78. doi: 10.5688/aj710478.
    1. Porges S.W. The polyvagal perspective. Biol. Psychol. 2007;74:116–143. doi: 10.1016/j.biopsycho.2006.06.009.
    1. Thayer J.F., Lane R.D. A model of neurovisceral integration in emotion regulation and dysregulation. J. Affect. Disord. 2000;61:201–216. doi: 10.1016/S0165-0327(00)00338-4.
    1. Saul J.P. Beat-to-beat variations of heart rate reflect modulation of cardiac autonomic outflow. Physiology. 1990;5:32–37. doi: 10.1152/physiologyonline.1990.5.1.32.
    1. Peltola M.A. Role of editing of R–R intervals in the analysis of heart rate variability. Front. Physiol. 2012;3:148. doi: 10.3389/fphys.2012.00148.
    1. Benarroch E.E. The central autonomic network: Functional organization, dysfunction, and perspective. Mayo Clin. Proc. 1993;68:988–1001. doi: 10.1016/S0025-6196(12)62272-1.
    1. Benarroch E.E. Central autonomic control. In: Robertson D., Biaggioni I., Burnstock G., Low P.A., Paton J.F.R., editors. Primer on the Autonomic Nervous System. 3rd ed. Elsevier; Amsterdam, The Netherlands: 2012. pp. 9–12.
    1. Hansen A.L., Johnsen B.H., Thayer J.F. Vagal influence on working memory and attention. Int. J. Psychophysiol. 2003;48:263–274. doi: 10.1016/S0167-8760(03)00073-4.
    1. Massaro S., Pecchia L. Heart rate variability (HRV) analysis: A methodology for organizational neuroscience. Organ. Res. Methods. 2019;22:354–393. doi: 10.1177/1094428116681072.
    1. Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation. 1996;93:1043–1065. doi: 10.1161/01.CIR.93.5.1043.
    1. Shaffer F., Ginsberg J.P. An overview of heart rate variability metrics and norms. Front. Public Health. 2017;5:258. doi: 10.3389/fpubh.2017.00258.
    1. Melillo P., Bracale M., Pecchia L. Nonlinear heart rate variability features for real-life stress detection. Case study: Students under stress due to university examination. Biomed. Eng. Online. 2011;10:96. doi: 10.1186/1475-925X-10-96.
    1. Fiskum C., Andersen T.G., Bornas X., Aslaksen P.M., Flaten M.A., Jacobsen K. Non-linear heart rate variability as a discriminator of internalizing psychopathology and negative affect in children with internalizing problems and healthy controls. Front. Physiol. 2018;9:561. doi: 10.3389/fphys.2018.00561.
    1. Kleiger R.E., Stein P.K., Bigger J.T. Heart rate variability: Measurement and clinical utility. Ann. Noninvasive Electrocardiol. 2005;10:88–101. doi: 10.1111/j.1542-474X.2005.10101.x.
    1. Bonaduce D., Marciano F., Petretta M., Migaux M.L., Morgano G., Bianchi V., Salemme L., Valva G., Condorelli M. Effects of converting enzyme inhibition on heart period variability in patients with acute myocardial infarction. Circulation. 1994;90:108–113. doi: 10.1161/01.CIR.90.1.108.
    1. Fleisher L.A., Frank S.M., Sessler D.I., Cheng C., Matsukawa T., Vannier C.A. Thermoregulation and heart rate variability. Clin. Sci. 1996;90:97–103. doi: 10.1042/cs0900097.
    1. Taylor J.A., Carr D.L., Myers C.W., Eckberg D.L. Mechanisms underlying very-low-frequency RR-interval oscillations in humans. Circulation. 1998;98:547–555. doi: 10.1161/01.CIR.98.6.547.
    1. Bernardi L., Leuzzi S., Radaelli A., Passino C., Johnston J.A., Sleight P. Low-frequency spontaneous fluctuations of R-R interval and blood pressure in conscious humans: A baroreceptor or central phenomenon? Clin. Sci. 1994;87:649–654. doi: 10.1042/cs0870649.
    1. Pagani M., Lombardi F., Guzzetti S., Rimoldi O., Furlan R., Pizzinelli P., Sandrone G., Malfatto G., Dell’Orto S., Piccaluga E. Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dog. Circ. Res. 1986;59:178–193. doi: 10.1161/01.RES.59.2.178.
    1. Pomeranz B., Macaulay R.J.B., Caudill M.A., Kutz I., Adam D., Gordon D., Kilborn K.M., Barger A.C., Shannon D.C., Cohen R.J., et al. Assessment of autonomic function in humans by heart rate spectral analysis. Am. J. Physiol. Heart Circ. Physiol. 1985;248:H151–H153. doi: 10.1152/ajpheart.1985.248.1.H151.
    1. Rahman F., Pechnik S., Gross D., Sewell L., Goldstein D.S. Low frequency power of heart rate variability reflects baroreflex function, not cardiac sympathetic innervation. Clin. Auton. Res. 2011;21:133–141. doi: 10.1007/s10286-010-0098-y.
    1. Malliani A., Lombardi F., Pagani M. Power spectrum analysis of heart rate variability: A tool to explore neural regulatory mechanisms. Br. Heart J. 1994;71:1–2. doi: 10.1136/hrt.71.1.1.
    1. Mourot L., Bouhaddi M., Perrey S., Cappelle S., Henriet M.-T., Wolf J.-P., Rouillon J.-D., Regnard J. Decrease in heart rate variability with overtraining: Assessment by the Poincaré plot analysis. Clin. Physiol. Funct. Imaging. 2004;24:10–18. doi: 10.1046/j.1475-0961.2003.00523.x.
    1. Mourot L., Bouhaddi M., Perrey S., Rouillon J.-D., Regnard J. Quantitative poincaré plot analysis of heart rate variability: Effect of endurance training. Eur. J. Appl. Physiol. 2004;91:79–87. doi: 10.1007/s00421-003-0917-0.
    1. Tulppo M.P., Mäkikallio T.H., Takala T.E.S., Seppänen T., Huikuri H.V. Quantitative beat-to-beat analysis of heart rate dynamics during exercise. Am. J. Physiol. Heart Circ. Physiol. 1996;271:H244–H252. doi: 10.1152/ajpheart.1996.271.1.H244.
    1. De Vito G., Galloway S.D.R., Nimmo M.A., Maas P., McMurray J.J.V. Effects of central sympathetic inhibition on heart rate variability during steady-state exercise in healthy humans. Clin. Physiol. Funct. Imaging. 2002;22:32–38. doi: 10.1046/j.1475-097X.2002.00395.x.
    1. Bolea J., Laguna P., Remartínez J.M., Rovira E., Navarro A., Bailón R. Methodological framework for estimating the correlation dimension in HRV signals. Comput. Math. Methods Med. 2014;2014:129248. doi: 10.1155/2014/129248.
    1. Toichi M., Sugiura T., Murai T., Sengoku A. A new method of assessing cardiac autonomic function and its comparison with spectral analysis and coefficient of variation of R–R interval. J. Auton. Nerv. Syst. 1997;62:79–84. doi: 10.1016/S0165-1838(96)00112-9.
    1. Goldberger J.J. Sympathovagal balance: How should we measure it? Am. J. Physiol. Heart Circ. Physiol. 1999;276:H1273–H1280. doi: 10.1152/ajpheart.1999.276.4.H1273.
    1. Ritsner M.S., Gottesman I.I. Where do we stand in the quest for neuropsychiatric biomarkers and endophenotypes and what next? In: Ritsner M.S., editor. The Handbook of Neuropsychiatric Biomarkers, Endophenotypes and Genes Volume I: Neuropsychological Endophenotypes and Biomarkers. Springer; Dordrecht, The Netherlands: 2009. pp. 3–21.
    1. Woo C.-W., Chang L.J., Lindquist M.A., Wager T.D. Building better biomarkers: Brain models in translational neuroimaging. Nat. Neurosci. 2017;20:365–377. doi: 10.1038/nn.4478.
    1. Babrak L.M., Menetski J., Rebhan M., Nisato G., Zinggeler M., Brasier N., Baerenfaller K., Brenzikofer T., Baltzer L., Vogler C., et al. Traditional and digital biomarkers: Two worlds apart? Digit. Biomark. 2019;3:92–102. doi: 10.1159/000502000.
    1. Califf R.M. Biomarker definitions and their applications. Exp. Biol. Med. 2018;243:213–221. doi: 10.1177/1535370217750088.
    1. Seshadri D.R., Li R.T., Voos J.E., Rowbottom J.R., Alfes C.M., Zorman C.A., Drummond C.K. Wearable sensors for monitoring the physiological and biochemical profile of the athlete. NPJ Digit. Med. 2019;2:72. doi: 10.1038/s41746-019-0150-9.
    1. Rykov Y., Thach T., Dunleavy G., Roberts A.C., Christopoulos G., Soh C., Car J. Activity tracker–based metrics as digital markers of cardiometabolic health in working adults: Cross-sectional study. JMIR mHealth uHealth. 2020;8:e16409:1–e16409:17. doi: 10.2196/16409.
    1. Yarkoni T., Westfall J. Choosing prediction over explanation in psychology: Lessons from machine learning. Perspect. Psychol. Sci. 2017;12:1100–1122. doi: 10.1177/1745691617693393.
    1. Dwyer D.B., Falkai P., Koutsouleris N. Machine learning approaches for clinical psychology and psychiatry. Annu. Rev. Clin. Psychol. 2018;14:91–118. doi: 10.1146/annurev-clinpsy-032816-045037.
    1. Murphy K.P. Machine Learning: A Probabilistic Perspective. The MIT Press; Cambridge, MA, USA: 2012. Machine learning: What and why? pp. 1–2.
    1. Bzdok D., Meyer-Lindenberg A. Machine learning for precision psychiatry: Opportunities and challenges. Biol. Psychiatry Cogn. Neurosci. Neuroimaging. 2018;3:223–230. doi: 10.1016/j.bpsc.2017.11.007.
    1. Choy G., Khalilzadeh O., Michalski M., Do S., Samir A.E., Pianykh O.S., Geis J.R., Pandharipande P.V., Brink J.A., Dreyer K.J. Current applications and future impact of machine learning in radiology. Radiology. 2018;288:318–328. doi: 10.1148/radiol.2018171820.
    1. Jordan M.I., Mitchell T.M. Machine learning: Trends, perspectives, and prospects. Science. 2015;349:255–260. doi: 10.1126/science.aaa8415.
    1. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning. 2nd ed. Springer; New York, NY, USA: 2009.
    1. Ripley B.D. Pattern Recognition and Neural Networks. Cambridge University Press; Cambridge, UK: 2005.
    1. Ballabio D., Grisoni F., Todeschini R. Multivariate comparison of classification performance measures. Chemometr. Intell. Lab. Syst. 2018;174:33–44. doi: 10.1016/j.chemolab.2017.12.004.
    1. Botchkarev A. Evaluating performance of regression machine learning models using multiple error metrics in azure machine learning studio. SSRN Electron. J. 2018:1–16. doi: 10.2139/ssrn.3177507.
    1. Botchkarev A. A new typology design of performance metrics to measure errors in machine learning regression algorithms. Interdiscip. J. Inf. Knowl. Manag. 2019;14:45–76. doi: 10.28945/4184.
    1. Chai T., Draxler R.R. Root mean square error (RMSE) or mean absolute error (MAE)?—Arguments against avoiding RMSE in the literature. Geosci. Model Dev. 2014;7:1247–1250. doi: 10.5194/gmd-7-1247-2014.
    1. Ferri C., Hernández-Orallo J., Modroiu R. An experimental comparison of performance measures for classification. Pattern Recognit. Lett. 2009;30:27–38. doi: 10.1016/j.patrec.2008.08.010.
    1. Sokolova M., Lapalme G. A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 2009;45:427–437. doi: 10.1016/j.ipm.2009.03.002.
    1. Bent B., Wang K., Grzesiak E., Jiang C., Qi Y., Jiang Y., Cho P., Zingler K., Ogbeide F.I., Zhao A., et al. The digital biomarker discovery pipeline: An open-source software platform for the development of digital biomarkers using mHealth and wearables data. J. Clin. Transl. Sci. 2021;5:e19:1–e19:8. doi: 10.1017/cts.2020.511.
    1. Kamišalić A., Fister I., Turkanović M., Karakatič S. Sensors and functionalities of non-invasive wrist-wearable devices: A review. Sensors. 2018;18:1714. doi: 10.3390/s18061714.
    1. Dunn J., Runge R., Snyder M. Wearables and the medical revolution. Per. Med. 2018;15:429–448. doi: 10.2217/pme-2018-0044.
    1. Reinertsen E., Clifford G.D. A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses. Physiol. Meas. 2018;39:05TR01. doi: 10.1088/1361-6579/aabf64.
    1. Tortelli R., Rodrigues F.B., Wild E.J. The use of wearable/portable digital sensors in Huntington’s disease: A systematic review. Parkinsonism Relat. Disord. 2021;83:93–104. doi: 10.1016/j.parkreldis.2021.01.006.
    1. Al-Libawy H., Al-Ataby A., Al-Nuaimy W., Al-Taee M.A. HRV-based operator fatigue analysis and classification using wearable sensors; Proceedings of the 2016 13th International Multi-Conference on Systems, Signals & Devices (SSD); Leipzig, Germany. 21–24 March 2016; pp. 268–273.
    1. Tsunoda K., Chiba A., Yoshida K., Watanabe T., Mizuno O. Predicting changes in cognitive performance using heart rate variability. IEICE Trans. Inf. Syst. 2017;100:2411–2419. doi: 10.1587/transinf.2016OFP0002.
    1. Huang S., Li J., Zhang P., Zhang W. Detection of mental fatigue state with wearable ECG devices. Int. J. Med. Inform. 2018;119:39–46. doi: 10.1016/j.ijmedinf.2018.08.010.
    1. AlGhatrif M., Lindsay J. A brief review: History to understand fundamentals of electrocardiography. J. Community Hosp. Intern. Med. Perspect. 2012;2:14383. doi: 10.3402/jchimp.v2i1.14383.
    1. Castaneda D., Esparza A., Ghamari M., Soltanpur C., Nazeran H. A review on wearable photoplethysmography sensors and their potential future applications in health care. Int. J. Biosens. Bioelectron. 2018;4:195–202. doi: 10.15406/ijbsbe.2018.04.00125.
    1. Sviridova N., Sakai K. Human photoplethysmogram: New insight into chaotic characteristics. Chaos Solitons Fractals. 2015;77:53–63. doi: 10.1016/j.chaos.2015.05.005.
    1. Lu S., Zhao H., Ju K., Shin K., Lee M., Shelley K., Chon K.H. Can photoplethysmography variability serve as an alternative approach to obtain heart rate variability information? J. Clin. Monit. Comput. 2008;22:23–29. doi: 10.1007/s10877-007-9103-y.
    1. Bent B., Goldstein B.A., Kibbe W.A., Dunn J.P. Investigating sources of inaccuracy in wearable optical heart rate sensors. NPJ Digit. Med. 2020;3:18. doi: 10.1038/s41746-020-0226-6.
    1. Schuurmans A.A.T., de Looff P., Nijhof K.S., Rosada C., Scholte R.H.J., Popma A., Otten R. Validity of the Empatica E4 wristband to measure heart rate variability (HRV) parameters: A comparison to electrocardiography (ECG) J. Med. Syst. 2020;44:190. doi: 10.1007/s10916-020-01648-w.
    1. Yu C., Liu Z., McKenna T., Reisner A.T., Reifman J. A method for automatic identification of reliable heart rates calculated from ECG and PPG waveforms. J. Am. Med. Inform. Assoc. 2006;13:309–320. doi: 10.1197/jamia.M1925.
    1. Hand D.J. Data mining: Statistics and more? Am. Stat. 1998;52:112–118. doi: 10.1080/00031305.1998.10480549.
    1. Hand D.J. Statistics and data mining. ACM SIGKDD Explor. Newsl. 1999;1:16–19. doi: 10.1145/846170.846171.
    1. L’Heureux A., Grolinger K., Elyamany H.F., Capretz M.A.M. Machine learning with big data: Challenges and approaches. IEEE Access. 2017;5:7776–7797. doi: 10.1109/ACCESS.2017.2696365.
    1. Smith G. The paradox of big data. SN Appl. Sci. 2020;2:1041. doi: 10.1007/s42452-020-2862-5.
    1. Chin-Yee B., Upshur R. Three problems with big data and artificial intelligence in medicine. Perspect. Biol. Med. 2019;62:237–256. doi: 10.1353/pbm.2019.0012.
    1. Adjerid I., Kelley K. Big data in psychology: A framework for research advancement. Am. Psychol. 2018;73:899–917. doi: 10.1037/amp0000190.
    1. He Q.P., Wang J. Application of systems engineering principles and techniques in biological big data analytics: A review. Processes. 2020;8:951. doi: 10.3390/pr8080951.
    1. Robotti E., Manfredi M., Marengo E. Biomarkers discovery through multivariate statistical methods: A review of recently developed methods and applications in proteomics. J. Proteom. Bioinform. 2013;S3:1–20. doi: 10.4172/jpb.S3-003.
    1. Gabrieli G., Azhari A., Esposito G. PySiology: A python package for physiological feature extraction. In: Esposito A., Faundez-Zanuy M., Morabito F.C., Pasero E., editors. Neural Approaches to Dynamics of Signal Exchanges; Smart Innovation, Systems and Technologies. Volume 151. Springer; Singapore: 2020. pp. 395–402.
    1. Epel E.S., Crosswell A.D., Mayer S.E., Prather A.A., Slavich G.M., Puterman E., Mendes W.B. More than a feeling: A unified view of stress measurement for population science. Front. Neuroendocrinol. 2018;49:146–169. doi: 10.1016/j.yfrne.2018.03.001.
    1. López-Núñez M.I., Rubio-Valdehita S., Diaz-Ramiro E.M., Aparicio-García M.E. Psychological capital, workload, and burnout: What’s new? The impact of personal accomplishment to promote sustainable working conditions. Sustainability. 2020;12:8124. doi: 10.3390/su12198124.
    1. Hart S.G. Nasa-task load index (NASA-TLX); 20 years later; Proceedings of the Human Factors and Ergonomics Society 50th Annual Meeting; San Fransisco, CA, USA. 16–20 October 2006; pp. 904–908.
    1. Hart S.G., Staveland L.E. Development of NASA-TLX (task load index): Results of empirical and theoretical research. In: Hancock P.A., Meshkati N., editors. Human Mental Workload; Advances in Psychology. Volume 52. North Holland Publishing Company; Amsterdam, The Netherlands: 1988. pp. 139–183.
    1. Xie B., Salvendy G. Prediction of mental workload in single and multiple tasks environments. Int. J. Cogn. Ergon. 2000;4:213–242. doi: 10.1207/S15327566IJCE0403_3.
    1. Kim H.-G., Cheon E.-J., Bai D.-S., Lee Y.H., Koo B.-H. Stress and heart rate variability: A meta-analysis and review of the literature. Psychiatry Investig. 2018;15:235–245. doi: 10.30773/pi.2017.08.17.
    1. Taelman J., Vandeput S., Vlemincx E., Spaepen A., Van Huffel S. Instantaneous changes in heart rate regulation due to mental load in simulated office work. Eur. J. Appl. Physiol. 2011;111:1497–1505. doi: 10.1007/s00421-010-1776-0.
    1. Lee K.F.A., Fox A.M., Notebaert L. The effects of anxiety, depressive, and obsessive-compulsive subclinical symptoms on performance monitoring. Int. J. Psychophysiol. 2020;158:362–369. doi: 10.1016/j.ijpsycho.2020.09.009.
    1. Varoquaux G., Poldrack R.A. Predictive models avoid excessive reductionism in cognitive neuroimaging. Curr. Opin. Neurobiol. 2019;55:1–6. doi: 10.1016/j.conb.2018.11.002.
    1. Trutschel U., Heinze C., Sirois B., Golz M., Sommer D., Edwards D. Heart rate measures reflect the interaction of low mental workload and fatigue during driving simulation; Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications AutomotiveUI ’12; Portsmouth, NH, USA. 17–19 October 2012; pp. 261–264.
    1. Brown D.M.Y., Bray S.R. Heart rate biofeedback attenuates effects of mental fatigue on exercise performance. Psychol. Sport Exerc. 2019;41:70–79. doi: 10.1016/j.psychsport.2018.12.001.
    1. Windthorst P., Mazurak N., Kuske M., Hipp A., Giel K.E., Enck P., Nieß A., Zipfel S., Teufel M. Heart rate variability biofeedback therapy and graded exercise training in management of chronic fatigue syndrome: An exploratory pilot study. J. Psychosom. Res. 2017;93:6–13. doi: 10.1016/j.jpsychores.2016.11.014.
    1. Abbott L.C., Taff D., Newman P., Benfield J.A., Mowen A.J. The influence of natural sounds on attention restoration. J. Park Recreat. Admi. 2016;34:5–15. doi: 10.18666/JPRA-2016-V34-I3-6893.

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