Forecasting the transition to sleep through HRV analysis: insights from ARIMA analysis and the concept of critical slowing down

References

• Abrahamsen A, Weihe P, Debes F, van Leeuwen WM. 2022. Sleep, sleepiness, and fatigue on board Faroese fishing vessels. Nat Sci Sleep. 14:347–362. doi: 10.2147/NSS.S342410.
   PubMed Web of Science ®Google Scholar
• Borbély AA, Daan S, Wirz-Justice A, Deboer T. 2016. The two-process model of sleep regulation: a reappraisal. J Sleep Res. 25(2):131–143. doi: 10.1111/jsr.12371.
   PubMed Web of Science ®Google Scholar
• Carpenter SR, Brock WA. 2006. Rising variance: a leading indicator of ecological transition. Ecol Lett. 9(3):311–318. doi: 10.1111/j.14610248.2005.00877.x.
   PubMed Web of Science ®Google Scholar
• Chua EC, Tan WQ, Yeo SC, Lau P, Lee I, Mien IH, Puvanendran K, Gooley JJ. 2012. Heart rate variability can be used to estimate sleepiness-related decrements in psychomotor vigilance during total sleep deprivation. Sleep. 35(3):325–334. doi: 10.5665/sleep.1688.
   PubMed Web of Science ®Google Scholar
• Demareva V, Viakhireva V, Zayceva I, Isakova I, Okhrimchuk Y, Zueva K, Demarev A, Nazarov N, Edeleva J. 2023. Temporal dynamics of subjective sleepiness: a convergence analysis of two scales. Biol Rhythm Res. 54(4):369–384. doi: 10.1080/09291016.2023.2193791.
   Web of Science ®Google Scholar
• de Mooij SMM, Blanken TF, Grasman RPPP, Ramautar JR, Van Someren EJW, van der Maas HLJ. 2020. Dynamics of sleep: exploring critical transitions and early warning signals. Comput Methods Programs Biomed. 193:105448. doi: 10.1016/j.cmpb.2020.105448.
   PubMed Web of Science ®Google Scholar
• Faal M, Almasganj F, Sinha GR. 2021. ECG signal modeling using volatility properties: its application in sleep apnea syndrome. J Healthc Eng. 2021:1–12. doi: 10.1155/2021/4894501.
   Web of Science ®Google Scholar
• Heydari S, Ayatollahi A, Najafi A, Poorjafari M. 2022. Detection of drowsiness using the pulse rate variability of finger. SN Comput Sci. 3(5):359. doi: 10.1007/s42979-022-01247-1.
   Google Scholar
• Huang F, Qin T, Wang L, Wan H, Ren J. 2019. An ECG signal prediction method based on ARIMA model and DWT. In Proceedings of 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chengdu, China, pp. 1298–1304. doi: 10.1109/IAEAC47372.2019.8997620.
   Google Scholar
• Jeklin AT, Perrotta AS, Davies HW, Bredin SSD, Paul DA, Warburton DER. 2021. The association between heart rate variability, reaction time, and indicators of workplace fatigue in wildland firefighters. international archives of occupational and environmental health. Int Arch Occup Environ Health. 94(5):823–831. doi: 10.1007/s00420-020-01641-3.
   PubMedGoogle Scholar
• Jung SJ, Shin HS, Chung WY. 2014. Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel. IET Intell Transp Syst. 8(1):43–50. doi: 10.1049/ietits.2012.0032.
   Web of Science ®Google Scholar
• Keller M, Roth WT, Petrowski K, Denise P. 2020. Supplementing sleep actigraphy with button pressing while awake. PLoS ONE. 15(6):e0234060. doi: 10.1371/journal.pone.0234060.
   PubMed Web of Science ®Google Scholar
• Ma Z, Li BC, Yan Z Wearable driver drowsiness detection using electrooculography signal. In Proceedings of 2016 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet), Austin, TX, USA, 2016. pp. 41–43. doi: 10.1109/WISNET.2016.7444317.
   Google Scholar
• Maturana MI, Meisel C, Dell K, Karoly PJ, D’Souza W, Grayden DB, Burkitt AN, Jiruska P, Kudlacek J, Hlinka J, et al. 2020. Critical slowing down as a biomarker for seizure susceptibility. Nat Commun. 11(1):2172. doi: 10.1038/s41467-02015908-3.
   PubMed Web of Science ®Google Scholar
• Ma Y, Zhu H, Gao T, Yu Y. 2020. Study on fatigue of urban railway transportation drivers based on eye movement characteristics and electrocardiogram. Resilience Sustain Transp Sys. 329–336. doi: 10.1061/9780784482902.038.
   Google Scholar
• Ospina R, Gondim JAM, Leiva V, Castro C. 2023. An overview of forecast analysis with ARIMA models during the COVID-19 pandemic: methodology and case study in Brazil. Mathematics. 2023(14):11, 3069. doi: 10.3390/math11143069.
   Google Scholar
• Scheffer M, Bascompte J, Brock WA, Brovkin V, Carpenter SR, Dakos V, Held H, van Nes EH, Rietkerk M, Sugihara G. 2009. Early-warning signals for critical transitions. Nature. 461(7260):53–59. doi: 10.1038/nature08227.
   PubMed Web of Science ®Google Scholar
• Smith SS, Horswill MS, Chambers B, Wetton M. 2009. Hazard perception in novice and experienced drivers: the effects of sleepiness. Accid Anal Prev. 41(4):729–733. doi: 10.1016/j.aap.2009.03.016.
   PubMed Web of Science ®Google Scholar
• Tsai C-Y, Cheong H, Houghton R, Hsu W-H, Lee K-Y, Kang J-H, Kuan Y-C, Lee H-C, Wu C-J, Li L-YJ, et al. 2023. Jun. Predicting fatigue-associated aberrant driving behaviors using a dynamic weighted moving average model with a long short-term memory network based on heart rate variability. Hum Factors. 30:187208231183874. doi: 10.1177/00187208231183874.
   Google Scholar
• Warren Liao T. 2005. Clustering of time series data—a survey. Pattern Recogn. 38(11):1857–1874. doi: 10.1016/j.patcog.2005.01.025.
   Web of Science ®Google Scholar
• Wichers M, Groot PC, Psychosystems, ESM Group, EWS Group. 2016. Critical slowing down as a personalized early warning signal for depression. Psychother Psychosom. 85(2):114–116. doi: 10.1159/000441458.
   PubMed Web of Science ®Google Scholar
• Yuda E, Yoshida Y, Sugita N, Yoshizawa M, Sakai M. 2023. Driver fatigue state estimation using time series analysis of body acceleration. 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). https://conf.papercept.net/conferences/conferences/SMC23/program/SMC23_ContentListWeb_3.html.
   Google Scholar