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Correlation Analysis of Electrocardiogram Signal according to Sleep Stage

수면 단계에 따른 심전도 신호의 상관관계 분석

  • Lee, JeeEun (Graduate Program of Biomedical Engineering, Yonsei University) ;
  • Yoo, Sun Kook (Dept. of Medical Engineering, Yonsei University College of Medicine)
  • Received : 2018.08.08
  • Accepted : 2018.11.11
  • Published : 2018.12.31

Abstract

There is a problem to measure neutral bio-signals during sleep because of inconvenience of attaching lots of sensors. In this study, we measured single electrocardiogram(ECG) signal and analyzed the correlation with sleep. After R-peak detection from ECG signal, we extracted 9 features from time and frequency domain of heart rate variability(HRV). Mean of HRV, RR intervals differing more than 50ms(NN50), and divided by the total number of all RR intervals(pNN50) have significant differences in each sleep stage. Specially, the mean HRV has an average of 87.8% accuracy in classifying sleep and awake status. In the future, the measurement ECG signal minimizes inconvenience of attaching sensors during sleep. Also, it can be substituted for the standard sleep measurement method.

Keywords

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Fig. 1. Experiments overview.

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Fig. 2. Flowchart for R-peak detection algorithm.

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Fig. 3. (a) Labeling, (b) Mean, (c) SDNN, (d) RMSSD, (e) NN50, (f) pNN50, (g) LF/HF, (h) TF, (i) HFn, (j) LFn (x: epoch, y: normalized unit)

Table 1. Features of heart rate variability

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Table 2. The number of each sleep stage

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Table 3. Mean and standard deviation of each feature of heart rate variability

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Table 4. Correlation between each feature and sleep stages

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Table 5. Regression coefficient of each feature of heart rate variability

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References

  1. V.M. Kumar, "Sleep and Sleep Disorders," Indian Journal of Chest Diseases and Allied Sciences, Vol. 50, No. 1, pp. 129-135, 2008.
  2. P. Hamet and J. Tremblay, "Genetics of the Sleep-Wake Cycle and Its Disorders," Metabolism, Vol. 55, pp. S7-S12, 2006. https://doi.org/10.1016/j.metabol.2006.07.006
  3. K. Susmakova, “Human Sleep and Sleep EEG,” Measurement Science Review, Vol. 4, No. 2, pp. 59-74, 2004.
  4. M.E. Tagluk, N. Sezgin, and M. Akin. "Estimation of Sleep Stages by an Artificial Neural Network Employing EEG, EMG and EOG," Journal of Medical Systems, Vol. 34, No. 4, pp. 717-725, 2010. https://doi.org/10.1007/s10916-009-9286-5
  5. P.D. Chazal, N. Fox, E. O'HARE, C. Heneghan, A. Zaffaroni, P. Boyle, et al., "Sleep/Wake Measurement Using a Non-Contact Biomotion Sensor," Journal of Sleep Research, Vol. 20, No. 2, pp. 356-366, 2011. https://doi.org/10.1111/j.1365-2869.2010.00876.x
  6. H.J. Burgess, J. Trinder, Y. Kim, and D. Luke, "Sleep and Circadian Influences on Cardiac Autonomic Nervous System Activity," American Journal of Physiology-Heart and Circulatory Physiology, Vol. 273, No. 4, pp. H1761-H1768, 1997. https://doi.org/10.1152/ajpheart.1997.273.4.H1761
  7. T. Penzel, “Is Heart Rate Variability the Simple Solution to Diagnose Sleep Apnoea?,” European Respiratory Journal, Vol. 22, No. 6, pp. 870-971, 2003. https://doi.org/10.1183/09031936.03.00102003
  8. A. Bunde, S. Havlin, J.W. Kantelhardt, T. Penzel, J.H. Peter, and K. Voigt, “Correlated and Uncorrelated Regions in Heart-Rate Fluctuations During Sleep,” Physical Review Letters, Vol. 85, No. 17, pp. 3736-3739, 2000. https://doi.org/10.1103/PhysRevLett.85.3736
  9. T. Hori, Y. Sugita, E. Koga, S. Shirakawa, K. Inoue, and Y. Tsuji, "Proposed Supplements and Amendments to'a Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects', the Rechtschaffen & Kales (1968) Standard," Psychiatry and Clinical Neurosciences, Vol. 55, No. 3, pp. 305-310, 2001. https://doi.org/10.1046/j.1440-1819.2001.00810.x
  10. D. Moser, P. Anderer, G. Gruber, S. Parapatics, E. Loretz, M. Boeck, et al., “Sleep Classification According to AASM and Rechtschaffen & Kales: Effects on Sleep Scoring Parameters,” Sleep, Vol. 32, No. 2, pp. 139-149, 2009. https://doi.org/10.1093/sleep/32.2.139
  11. B. Subramanian, “ECG Signal Classification and Parameter Estimation Using Multiwavelet Transform,” Biomedical Research, Vol. 28, No. 7, pp. 3187-3193, 2017.
  12. J.H. Kim, S.M. Lee, and K.H. Park, "P-Waves and T-Wave Detection Algorithm in the ECG Signals Using Step-by-Step Baseline Alignment," Journal of Korea Multimedia Society, Vol. 19, No. 6, pp. 1034-1042, 2016. https://doi.org/10.9717/kmms.2016.19.6.1034
  13. E. Werth, P. Achermann, and A.A. Borbely, “Brain Topography of the Human Sleep EEG: Antero-Posterior Shifts of Spectral Power,” Neuroreport, Vol. 8, No. 1, pp. 123-127, 1996. https://doi.org/10.1097/00001756-199612200-00025
  14. H.R. Variability, “Standards of Measurement, Physiological Interpretation, and Clinical Use,” Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiolog, Circulation, Vol. 93, No. 5, pp. 1043-1065, 1996.
  15. G.G. Berntson, J.T. Bigger, D.L. Eckberg, P. Grossman, P.G. Kaufmann, M. Malik, et al., “Heart Rate Variability: Origins, Methods, and Interpretive Caveats,” Psychophysiology, Vol. 34, No. 6, pp. 623-648, 1997. https://doi.org/10.1111/j.1469-8986.1997.tb02140.x

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