Abstract
Modeling biological rhythms helps understand the complex principles behind the physical and psychological abnormalities of human bodies, to plan life schedules, and avoid persisting fatigue and mood and sleep alterations due to the desynchronization of those rhythms. The first step in modeling biological rhythms is to identify their characteristics, such as cyclic periods, phase, and amplitude. However, human rhythms are susceptible to external events, which cause irregular fluctuations in waveforms and affect the characterization of each rhythm. In this paper, we present our exploratory work towards developing a computational framework for automated discovery and modeling of human rhythms. We first identify cyclic periods in time series data using three different methods and test their performance on both synthetic data and real fine-grained biological data. We observe consistent periods are detected by all three methods. We then model inner cycles within each period through identifying change points to observe fluctuations in biological data that may inform the impact of external events on human rhythms. The results provide initial insights into the design of a computational framework for discovering and modeling human rhythms.
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References
Empatica. E4 wristband User’s manual (2018a). Accessed 11 April 2018. https://empatica.app.box.com/v/E4-User-Manual
Abdullah, S., Matthews, M., Frank, E., Doherty, G., Gay, G., Choudhury, T.: Automatic detection of social rhythms in bipolar disorder. J. Am. Med. Inform. Assoc. 23(3), 538–543 (2016)
Abe, K., Kroning, J., Greer, M.A., Critchlow, V.: Effects of destruction of the suprachiasmatic nuclei on the circadian rhythms in plasma corticosterone, body temperature, feeding and plasma thyrotropin. Neuroendocrinology 29(2), 119–131 (1979)
Adamopoulos, S., et al.: Circadian pattern of heart rate variability in chronic heart failure patients effects of physical training. Eur. Heart J. 16(10), 1380–1386 (1995)
Aguzzi, J., Sarria, D., Garcia, J.A., del Rio, J., Sarda, F., Lzaro, A.: A new tracking system for the measurement of diel locomotor rhythms in the Norway lobster, nephrops norvegicus (l.). J. Neurosci. Methods 173, 215–224 (2008)
Aminikhanghahi, S., Cook, D.J.: A survey of methods for time series change point detection. Knowl. Inf. Syst. 51(2), 339–367 (2016). https://doi.org/10.1007/s10115-016-0987-z
Aschoff, J., Gerecke, U., Wever, R.: Desynchronization of human circadian rhythms. Jpn. J. Physiol. 17, 450–457 (1967)
Bosc, M., Heitz, F., Armspach, J.-P., Namer, I., Gounot, D., Rumbach, L.: Automatic change detection in multimodal serial MRI: application to multiple sclerosis lesion evolution. NeuroImage 20(2), 643–656 (2003)
Cornélissen, G.: Cosinor-based rhythmometry. Theor. Biol. Med. Model. 11, 16 (2014)
Doryab, A., Dey, A.K., Kao, G., Low, C.: Modeling biobehavioral rhythms with passive sensing in the wild: a case study to predict readmission risk after pancreatic surgery. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3(1), 1–21 (2019)
Enright, J.: The search for rhythmicity in biological time-series. J. Theor. Biol. 8, 426–468 (1965)
Frick, L.: Heart rate and skin temperature dateset, October 2016. https://data.world/laurie/skin-temperature/
Gale, J.E., Cox, H.I., Qian, J., Block, G.D., Colwell, C.S., Matveyenko, A.V.: Disruption of circadian rhythms accelerates development of diabetes through pancreatic beta-cell loss and dysfunction. J. Biol. Rhythms 26(5), 423–433 (2011)
Gani, J., Bloomfield, P.: Fourier analysis of time series: an introduction. Int. Stat. Rev./Revue Internationale de Statistique. 46, 116 (1978)
Germain, A., Kupfer, D.: Circadian rhythm disturbances in depression. Human Psychopharmacol. 23, 571–585 (2008)
Gery, S., Koeffler, H.P.: Circadian rhythms and cancer. Cell Cycle 9(6), 1097–1103 (2010)
Glynn, E.F., Chen, J., Mushegian, A.R.: Detecting periodic patterns in unevenly spaced gene expression time series using lomb-scargle periodograms. Bioinformatics 22(3), 310–316 (2006)
Green, P.: Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82, 09 (1995)
Gubin, D.G., et al.: Activity, sleep and ambient light have a different impact on circadian blood pressure, heart rate and body temperature rhythms. Chronobiol. Int. 34(5), 632–649 (2017)
Hadj-Amar, B., Rand, B.F., Fiecas, M., Levi, F., Huckstepp, R.: Bayesian model search for nonstationary periodic time series. J. Am. Stat. Assoc. 115, 1–36 (2019)
Halberg, F.: Some physiological and clinical aspects of 24-hour periodicity. J.-lancet 73, 20–32 (1953)
Halberg, F., Tong, Y.L., Johnson, E.A.: Circadian system phase-an aspect of temporal morphology; procedures and illustrative examples. In: The Cellular Aspects of Biorhythms, pp. 20–48. Springer (1967)
Kräuchi, K.: How is the circadian rhythm of core body temperature regulated? (2002)
Laguna, J.O., Olaya, A.G., Borrajo, D.: A dynamic sliding window approach for activity recognition. In: International Conference on User Modeling, Adaptation, and Personalization, pp. 219–230. Springer (2011)
Leise, T.: Analysis of nonstationary time series for biological rhythms research. J. Biol. Rhythms 32, 074873041770910 (2017)
Malladi, R., Kalamangalam, G.P., Aazhang, B.: Online Bayesian change point detection algorithms for segmentation of epileptic activity. In: 2013 Asilomar Conference on Signals, Systems and Computers, pp. 1833–1837. IEEE (2013)
Massin, M.M., Maeyns, K., Withofs, N., Ravet, F., Gérard, P.: Circadian rhythm of heart rate and heart rate variability. Arch. Dis. Child. 83(2), 179–182 (2000)
Moritz, S., Bartz-Beielstein, T.: impute TS: time series missing value imputation in R. R J. 9(1), 207 (2017)
Morris, C., Purvis, T., Kun, H., Scheer, F.: Circadian misalignment increases cardiovascular disease risk factors in humans. Proc. Nat. Acad. Sci. 113, 02 (2016)
Murnane, E.L., Abdullah, S., Matthews, M., Choudhury, T., Gay, G.: Social (media) jet lag: how usage of social technology can modulate and reflect circadian rhythms. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 843–854 (2015)
Peters, B.R.: Why does my heart rate spike when i’m asleep? (2018)
Peters, B.R., Joireman, J., Ridgway, R.L., Individual differences in the consideration of future consequences scale correlate with sleep habits, sleep quality, and GPA in university students. Psychol. Rep. 96(3), 817–824 (2005)
Pierson, E., Althoff, T., Leskovec, J.: Modeling individual cyclic variation in human behavior. In: Proceedings of the 2018 World Wide Web Conference, WWW 2018, pp. 107–116, Republic and Canton of Geneva, CHE, 2018. International World Wide Web Conferences Steering Committee (2018)
Rabiner, L., Juang, B.H.: An introduction to hidden Markov models. IEEE ASSP Mag. 3(1), 4–16 (1986)
Refinetti, R., Lissen, G., Halberg, F.: Procedures for numerical analysis of circadian rhythms. Biol. Rhythm Res. 38, 275–325 (2007)
Refinetti, R., Menaker, M.: The circadian rhythm of body temperature. Physiol. Behav. 51, 613–637 (1992)
Reinberg, A., Ashkenazi, I.: Concepts in human biological rhythms. Dialogues Clin. Neurosci. 5, 327–342 (2003)
Saner, C., Simonetti, G.D., Wühl, E., Mullis, P.E., Janner, M.: Circadian and ultradian cardiovascular rhythmicity in obese children. Eur. J. Pediatr. 175(8), 1031–1038 (2016). https://doi.org/10.1007/s00431-016-2736-4
Sokolove, P., Bushell, W.: The chi square periodogram: its utility for analysis of circadian rhythms. J. Theor. Biol. 72, 131–160 (1978)
Staudacher, M., Telser, S., Amann, A., Hinterhuber, H., Ritsch-Marte, M.: A new method for change-point detection developed for on-line analysis of the heart beat variability during sleep. Physica A Stat. Mech. Appl. 349(3–4), 582–596 (2005)
van Kasteren, T.L.M., Englebienne, G., Kröse, B.J.A.: Human activity recognition from wireless sensor network data: Benchmark and software. In: Activity Recognition in Pervasive Intelligent Environments, pp. 165–186. Springer (2011)
Vukolic, A., Antic, V., Van Vliet, B.N., Yang, Z., Albrecht, U., Montani, J.P.: Role of mutation of the circadian clock gene per2 in cardiovascular circadian rhythms. Am. J. Physiol.-Regul. Integr. Comp. Physiol. 298(3), R627–R634 (2010)
Yang, P., Dumont, G., Ansermino, J.M.: Adaptive change detection in heart rate trend monitoring in anesthetized children. IEEE Trans. Biomed. Eng. 53(11), 2211–2219 (2006)
Yoshizawa, M., Takasaki, W., Ohmura, R.: Parameter exploration for response time reduction in accelerometer-based activity recognition. In: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication, pp. 653–664 (2013)
Zielinski, T., Moore, A., Troup, E., Halliday, K., Millar, A.: Strengths and limitations of period estimation methods for circadian data. PloS one 9, e96462 (2014)
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Yan, R., Doryab, A. (2022). Towards a Computational Framework for Automated Discovery and Modeling of Biological Rhythms from Wearable Data Streams. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-030-82199-9_44
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DOI: https://doi.org/10.1007/978-3-030-82199-9_44
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