Detection and analysis of pulse waves during sleep via wrist-worn actigraphy


Autoři: Johannes Zschocke aff001;  Maria Kluge aff003;  Luise Pelikan aff003;  Antonia Graf aff003;  Martin Glos aff003;  Alexander Müller aff004;  Rafael Mikolajczyk aff001;  Ronny P. Bartsch aff005;  Thomas Penzel aff003;  Jan W. Kantelhardt aff002
Působiště autorů: Institute of Medical Epidemiology, Biostatistics and Informatics, Faculty of Medicine, Martin-Luther-University Halle-Wittenberg, Halle, Germany aff001;  Institute of Physics, Martin-Luther-University Halle-Wittenberg, Halle, Germany aff002;  Interdisziplinäres Schlafmedizinisches Zentrum, Charite - Universitätsmedizin Berlin, Berlin, Germany aff003;  Klinik und Poliklinik für Innere Medizin I, Technische Universität München, Munich, Germany aff004;  Department of Physics, Bar-Ilan University, Ramat Gan, Israel aff005
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0226843

Souhrn

The high temporal and intensity resolution of modern accelerometers gives the opportunity of detecting even tiny body movements via motion-based sensors. In this paper, we demonstrate and evaluate an approach to identify pulse waves and heartbeats from acceleration data of the human wrist during sleep. Specifically, we have recorded simultaneously full-night polysomnography and 3d wrist actigraphy data of 363 subjects during one night in a clinical sleep laboratory. The acceleration data was segmented and cleaned, excluding body movements and separating episodes with different sleep positions. Then, we applied a bandpass filter and a Hilbert transform to uncover the pulse wave signal, which worked well for an average duration of 1.7 h per subject. We found that 81 percent of the detected pulse wave intervals could be correctly associated with the R peak intervals from independently recorded ECGs and obtained a median Pearson cross-correlation of 0.94. While the low-frequency components of both signals were practically identical, the high-frequency component of the pulse wave interval time series was increased, indicating a respiratory modulation of pulse transit times, probably as an additional contribution to respiratory sinus arrhythmia. Our approach could be used to obtain long-term nocturnal heartbeat interval time series and pulse wave signals from wrist-worn accelerometers without the need of recording ECG or photoplethysmography. This is particularly useful for an ambulatory monitoring of high-risk cardiac patients as well as for assessing cardiac dynamics in large cohort studies solely with accelerometer devices that are already used for activity tracking and sleep pattern analysis.

Klíčová slova:

Age groups – Electrocardiography – Sleep disorders – Accelerometers – Motion – Heart rate – Sleep


Zdroje

1. Rechtschaffen A, Kales A. A Manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. vol. 204 of National Institutes of Health Publication. Bethesda, MD; 1968.

2. Berry RB, Albertario CL, Harding SM, Lloyd R M, Plante DT, Quan SF, et al. The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications, version 2.5. American Academy of Sleep Medicine, Darien, IL. 2018.

3. Penzel T, Kantelhardt JW, Bartsch RP, Riedl M, Kraemer JF, Wessel N, et al. Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography. Frontiers in Physiology. 2016;7:460. doi: 10.3389/fphys.2016.00460 27826247

4. Kupfer DJ, Weiss BL, Foster FG, Detre TP, Delgado J, McPartland R. Psychomotor activity in affective states. Archives of General Psychiatry. 1974;30(6):765–768. doi: 10.1001/archpsyc.1974.01760120029005 4832184

5. Koenig SM, Mack D, Alwan M. Sleep and sleep assessment technologies. In: Eldercare technology for clinical practitioners. Springer; 2008. p. 77–120.

6. Wohlfahrt P, Kantelhardt JW, Zinkhan M, Schumann AY, Penzel T, Fietze I, et al. Transitions in effective scaling behavior of accelerometric time series across sleep and wake. EPL (Europhysics Letters). 2013;103(6):68002. doi: 10.1209/0295-5075/103/68002

7. Zinkhan M, Berger K, Hense S, Nagel M, Obst A, Koch B, et al. Agreement of different methods for assessing sleep characteristics: a comparison of two actigraphs, wrist and hip placement, and self-report with polysomnography. Sleep Medicine. 2014;15(9):1107–1114. doi: 10.1016/j.sleep.2014.04.015 25018025

8. van Hees VT, Sabia S, Jones SE, Wood AR, Anderson KN, Kivimäki M, et al. Estimating sleep parameters using an accelerometer without sleep diary. Scientific Reports. 2018;8(1):12975. doi: 10.1038/s41598-018-31266-z 30154500

9. Ancoli-Israel S, Cole R, Alessi C, Chambers M, Moorcroft W, Pollak CP. The role of actigraphy in the study of sleep and circadian rhythms. Sleep. 2003;26(3):342–392. doi: 10.1093/sleep/26.3.342 12749557

10. Godfrey A, Conway R, Meagher D, ÓLaighin G. Direct measurement of human movement by accelerometry. Medical Engineering & Physics. 2008;30(10):1364–1386.

11. Sadeh A, Acebo C. The role of actigraphy in sleep medicine. Sleep Medicine Reviews. 2002;6(2):113–124. doi: 10.1053/smrv.2001.0182 12531147

12. Zinkhan M, Kantelhardt JW. Sleep assessment in large cohort studies with high-resolution accelerometers. Sleep Medicine Clinics. 2016;11(4):469–488. doi: 10.1016/j.jsmc.2016.08.006 28118871

13. Hu K, Ivanov PC, Chen Z, Hilton MF, Stanley HE, Shea SA. Novel multiscale regulation in human motor activity. In: Bezrukov SM, Frauenfelder H, Moss F, editors. Fluctuations and Noise in Biological, Biophysical, and Biomedical Systems. SPIE Proceedings. SPIE; 2003. p. 235.

14. Hu K, Ivanov PC, Chen Z, Hilton MF, Stanley HE, Shea SA. Non-random fluctuations and multi-scale dynamics regulation of human activity. Physica A. 2004;337(1-2):307–318. doi: 10.1016/j.physa.2004.01.042 15759365

15. Ivanov PC, Hu K, Hilton MF, Shea SA, Stanley HE. Endogenous circadian rhythm in human motor activity uncoupled from circadian influences on cardiac dynamics. Proceedings of the National Academy of Sciences of the United States of America. 2007;104(52):20702–20707. doi: 10.1073/pnas.0709957104 18093917

16. Hu K, Scheer FAJL, Ivanov PC, Buijs RM, Shea SA. The suprachiasmatic nucleus functions beyond circadian rhythm generation. Neuroscience. 2007;149(3):508–517. doi: 10.1016/j.neuroscience.2007.03.058 17920204

17. Malik M, Bigger JT, Camm AJ, Kleiger RE, Malliani A, Moss AJ, et al. Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. European Heart Journal. 1996;17(3):354–381. doi: 10.1093/oxfordjournals.eurheartj.a014868

18. O’Rourke MF, Pauca A, Jiang XJ. Pulse wave analysis. British Journal of Clinical Pharmacology. 2001;51(6):507–522. doi: 10.1046/j.0306-5251.2001.01400.x 11422010

19. Nichols WW. Clinical measurement of arterial stiffness obtained from noninvasive pressure waveforms. American Journal of Hypertension. 2005;18:3S–10S. doi: 10.1016/j.amjhyper.2004.10.009 15683725

20. Gong S, Schwalb W, Wang Y, Chen Y, Tang Y, Si J, et al. A wearable and highly sensitive pressure sensor with ultrathin gold nanowires. Nature Communications. 2014;5:3132. doi: 10.1038/ncomms4132 24495897

21. Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiological Measurement. 2007;28(3):R1–39. doi: 10.1088/0967-3334/28/3/R01 17322588

22. Karasik R, Sapir N, Ashkenazy Y, Ivanov PC, Dvir I, Lavie P, et al. Correlation differences in heartbeat fluctuations during rest and exercise. Physical Review E, Statistical, Nonlinear, and Soft Matter Physics. 2002;66(6 Pt 1):062902. doi: 10.1103/PhysRevE.66.062902 12513330

23. Will C, Shi K, Schellenberger S, Steigleder T, Michler F, Fuchs J, et al. Radar-Based Heart Sound Detection. Scientific Reports. 2018;8(1):11551. doi: 10.1038/s41598-018-29984-5 30068983

24. Zanetti JM, Salerno DM. Seismocardiography: a technique for recording precordial acceleration. In: Bankman IN, Tsitlik JE, editors. Computer-based medical systems. Los Alamitos, CA: IEEE Computer Society Press; 1991. p. 4–9.

25. Pandia K, Inan OT, Kovacs GTA, Giovangrandi L. Extracting respiratory information from seismocardiogram signals acquired on the chest using a miniature accelerometer. Physiological Measurement. 2012;33(10):1643–1660. doi: 10.1088/0967-3334/33/10/1643 22986375

26. Inan OT, Migeotte PF, Park KS, Etemadi M, Tavakolian K, Casanella R, et al. Ballistocardiography and seismocardiography: a review of recent advances. IEEE Journal of Biomedical and Health Informatics. 2015;19(4):1414–1427. doi: 10.1109/JBHI.2014.2361732 25312966

27. Jafari Tadi M, Lehtonen E, Hurnanen T, Koskinen J, Eriksson J, Pänkäälä M, et al. A real-time approach for heart rate monitoring using a Hilbert transform in seismocardiograms. Physiological Measurement. 2016;37(11):1885–1909. doi: 10.1088/0967-3334/37/11/1885 27681033

28. Paolo Castiglioni, Andrea Faini, Gianfranco Parati, Marco Di Rienzo. 2007 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway, NJ: IEEE Service Center; 2007.

29. Jafari Tadi M, Koivisto T, Pänkäälä M, Paasio A. Accelerometer-based method for extracting respiratory and cardiac gating information for dual gating during nuclear medicine imaging. International Journal of Biomedical Imaging. 2014;2014:690124. doi: 10.1155/2014/690124 25120563

30. Vähä-Ypyä H, Vasankari T, Husu P, Suni J, Sievänen H. A universal, accurate intensity-based classification of different physical activities using raw data of accelerometer. Clinical Physiology and Functional Imaging. 2015;35(1):64–70. doi: 10.1111/cpf.12127 24393233

31. Gabor D. Theory of communication. The analysis of information. Journal of the Institution of Electrical Engineers-Part III: Radio and Communication Engineering. 1946;93(26):429–441.

32. Ivanov PC, Rosenblum MG, Peng CK, Mietus J, Havlin S, Stanley HE, et al. Scaling behaviour of heartbeat intervals obtained by wavelet-based time-series analysis. Nature. 1996;383(6598):323–327. doi: 10.1038/383323a0 8848043

33. Ivanov PC, Rosenblum MG, Peng CK, Mietus JE, Havlin S, Stanley HE, et al. Scaling and universality in heart rate variability distributions. Physica A: Statistical Mechanics and its Applications. 1998;249(1-4):587–593. doi: 10.1016/s0378-4371(97)00522-0 11541514

34. Schneider R, Bauer A, Barthel P, Schmidt G. LibRasch: a programming framework for signal handling. In: Institute of Electrical and Electronics Engineers 2004 – Computers in Cardiology;. p. 53–56.

35. Schmitt DT, Stein PK, Ivanov PC. Stratification pattern of static and scale-invariant dynamic measures of heartbeat fluctuations across sleep stages in young and elderly. IEEE Transactions on Bio-Medical Engineering. 2009;56(5):1564–1573. doi: 10.1109/TBME.2009.2014819 19203874

36. Hernandez J, McDuff D, Picard R. BioWatch: Estimation of heart and breathing rates from wrist motions. In: Arnrich B, Ersoy C, Dey A, Kunze K, Berthouze N, editors. Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare. ICST; 20.05.2015–23.05.2015.

37. Haescher M, Matthies DJC, Trimpop J, Urban B. A study on measuring heart- and respiration-rate via wrist-worn accelerometer-based seismocardiography (SCG) in comparison to commonly applied technologies. In: Urban B, Kirste T, editors. Proceedings of the 2nd International Workshop on Sensor-based Activity Recognition and Interaction. New York: ACM Press; 2015. p. 1–6.

38. Haescher M, Matthies DJC, Trimpop J, Urban B. SeismoTracker: Upgrade any smart wearable to enable a sensing of heart rate, respiration rate, and microvibrations. In: Kaye J, Druin A, Lampe C, Morris D, Hourcade JP, editors. Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. New York, USA: ACM Press; 2016. p. 2209–2216.

39. Nitzan M, Khanokh B, Slovik Y. The difference in pulse transit time to the toe and finger measured by photoplethysmography. Physiological Measurement. 2002;23(1):85–93. doi: 10.1088/0967-3334/23/1/308 11876244

40. Schumann AY, Bartsch RP, Penzel T, Ivanov PC, Kantelhardt JW. Aging effects on cardiac and respiratory dynamics in healthy subjects across sleep stages. Sleep. 2010;33(7):943–955. doi: 10.1093/sleep/33.7.943 20614854

41. Gesche H, Grosskurth D, Küchler G, Patzak A. Continuous blood pressure measurement by using the pulse transit time: comparison to a cuff-based method. European Journal of Applied Physiology. 2012;112(1):309–315. doi: 10.1007/s00421-011-1983-3 21556814

42. Einbrodt P. Über den Einfluss der Athembewegungen auf Herzschlag und Blutdruck. –: Abhandlungen und Mittheilungen. Sitzungsberichte der Akademie der Wissenschaften mathematisch-naturwissenschaftliche Klasse. 1860;(40):361–418.

43. Hamann C, Bartsch RP, Schumann AY, Penzel T, Havlin S, Kantelhardt JW. Automated synchrogram analysis applied to heartbeat and reconstructed respiration. Chaos (Woodbury, NY). 2009;19(1):015106. doi: 10.1063/1.3096415


Článok vyšiel v časopise

PLOS One


2019 Číslo 12
Najčítanejšie tento týždeň