Actigraphic Sleep Detection for Real-World Data of Healthy Young Adults and People with Alzheimer’s Disease

Stefan Lüdtke, Albert Hein, Frank Krüger, Sebastian Bader, Thomas Kirste

2017

Abstract

Actigraphy can be used to examine the sleep pattern of patients during the course of the day in their common environment. However, conventional sleep detection algorithms may not be appropriate for real-world daytime sleep detection, since they tend to overestimate the sleep duration and have only been validated for nighttime sleep in a laboratory setting. Therefore, we evaluated the performance of a set of new sleep detection algorithms based on machine learning methods in a real-world setting and compared them to two conventional sleep detection algorithms (Cole’s algorithm and Sadeh’s algorithm). For that, we performed two studies with (1) healthy young adults and (2) nursing home residents with Alzheimer’s dementia. The conventional algorithms performed poorly for these real-world data sets, because they are imbalanced with respect to sensitivity and specificity. A more balanced Hidden Markov Model-based algorithm surpassed the conventional algorithms for both data sets. Using this algorithm leads to an improved accuracy of 4.1 percent points (pp) and 23.5 pp, respectively, compared to the conventional algorithms. The Youden-Index improved by 7.3 and 7.7, respectively. Overall, for a real-world setting, the HMM-based algorithm achieved a performance similar to conventional algorithms in a laboratory environment.

References

  1. Ancoli-Israel, S. (2009). Sleep and its disorders in aging populations. Sleep medicine, 10:S7-S11.
  2. Ancoli-Israel, S., Cole, R., Alessi, C., Chambers, M., Moorcroft, W., and Pollak, C. (2003). The role of actigraphy in the study of sleep and circadian rhythms. american academy of sleep medicine review paper. Sleep, 26(3):342-392.
  3. Bieber, G., Kirste, T., and Gaede, M. (2014). Low sampling rate for physical activity recognition. In Proceedings of the 7th International Conference on Pervasive Technologies Related to Assistive Environments, pages 15:1-15:8. ACM.
  4. Chawla, N. V. (2005). Data mining for imbalanced datasets: An overview. In Data mining and knowledge discovery handbook, pages 853-867. Springer.
  5. Cole, R., Kripke, D., Gruen, W., Mullaney, D. J., and Gillin, J. C. (1992). Automatic sleep/wake identi-fi cation from wrist activity. Sleep, 15(3):461-469.
  6. Cook, K., Lichstein, K., Donaldson, J., Nau, S., Lester, K., and Aguillard, R. (2004). An exploratory validation of actigraphic measures of insomnia. Sleep, 27:270-270.
  7. de Souza, L., Benedito-Silva, A. A., Pires, M. N., Poyares, D., Tufik, S., and Calil, H. M. (2003). Further validation of actigraphy for sleep studies. Sleep, 26(1):81- 85.
  8. Domingues, A., Paiva, T., and Sanches, J. M. (2014). Sleep and wakefulness state detection in nocturnal actigraphy based on movement information. IEEE Transactions on Biomedical Engineering, 61(2):426-434.
  9. Hedner, J., Pillar, G., Pittman, S. D., Zou, D., Grote, L., and White, D. P. (2004). A novel adaptive wrist actigraphy algorithm for sleep-wake assessment in sleep apnea patients. Sleep, 27(8):1560-1566.
  10. Kushida, C. A., Chang, A., Gadkary, C., Guilleminault, C., Carrillo, O., and Dement, W. C. (2001). Comparison of actigraphic, polysomnographic, and subjective assessment of sleep parameters in sleep-disordered patients. Sleep medicine, 2(5):389-396.
  11. Le Bon, O., Staner, L., Hoffmann, G., Dramaix, M., San Sebastian, I., Murphy, J. R., Kentos, M., Pelc, I., and Linkowski, P. (2001). The first-night effect may last more than one night. Journal of psychiatric research, 35(3):165-172.
  12. Lichstein, K. L., Stone, K. C., Donaldson, J., Nau, S. D., Soeffing, J. P., Murray, D., Lester, K. W., and Aguillard, R. N. (2006). Actigraphy validation with insomnia. Sleep, 29(2):232.
  13. Martin, J. L. and Hakim, A. D. (2011). Wrist actigraphy. Chest Journal, 139(6):1514-1527.
  14. McCurry, S. M. and Ancoli-Israel, S. (2003). Sleep dysfunction in alzheimers disease and other dementias. Current Treatment Options in Neurology, 5(3):261- 272.
  15. Middelkoop, H. A., Dam, E. M., Smilde-Van den Doel, D. A., and Dijk, G. (1997). 45-hour continuous quintuple-site actimetry: Relations between trunk and limb movements and effects of circadian sleep-wake rhythmicity. Psychophysiology, 34(2):199-203.
  16. Mishima, K., Okawa, M., Hishikawa, Y., Hozumi, S., Hori, H., and Takahashi, K. (1994). Morning bright light therapy for sleep and behavior disorders in elderly patients with dementia. Acta Psychiatrica Scandinavica, 89(1):1-7.
  17. Nakazaki, K., Kitamura, S., Motomura, Y., Hida, A., Kamei, Y., Miura, N., and Mishima, K. (2014). Validity of an algorithm for determining sleep/wake states using a new actigraph. Journal of physiological anthropology, 33(1):1.
  18. Orellana, G., Held, C., Estevez, P., Perez, C., Reyes, S., Algarin, C., and Peirano, P. (2014). A balanced sleep/wakefulness classification method based on actigraphic data in adolescents. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 4188-4191. IEEE.
  19. Paquet, J., Kawinska, A., and Carrier, J. (2007). Wake detection capacity of actigraphy during sleep. Sleep, 30(10):1362.
  20. Rechtschaffen, A. and Kales, A. (1968). A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects.
  21. Sadeh, A., Alster, J., Urbach, D., and Lavie, P. (1989). Actigraphically based automatic bedtime sleep-wake scoring: validity and clinical applications. Journal of Ambulatory Monitoring, 2(3):209-216.
  22. Sloane, P. D., Brooker, D., Cohen, L., Douglass, C., Edelman, P., Fulton, B. R., Jarrott, S., Kasayka, R., Kuhn, D., Preisser, J. S., et al. (2007). Dementia care mapping as a research tool. International journal of geriatric psychiatry, 22(6):580-589.
  23. Taibi, D. M., Landis, C. A., and Vitiello, M. V. (2013). Concordance of polysomnographic and actigraphic measurement of sleep and wake in older women with insomnia. J Clin Sleep Med, 9(3):217-225.
  24. Tilmanne, J., Urbain, J., Kothare, M. V., Wouwer, A. V., and Kothare, S. V. (2009). Algorithms for sleep-wake identification using actigraphy: a comparative study and new results. Journal of sleep research, 18(1):85- 98.
  25. Van Someren, E. J., Lazeron, R. H., Vonk, B. F., Mirmiran, M., and Swaab, D. F. (1996). Gravitational artefact in frequency spectra of movement acceleration: implications for actigraphy in young and elderly subjects. Journal of neuroscience methods, 65(1):55-62.
  26. Viterbi, A. (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE transactions on Information Theory, 13(2):260- 269.
  27. Youden, W. J. (1950). Index for Rating Diagnostic Tests. Cancer, 3(1):32-35.
  28. Zhang, C.-L. and Popp, F.-A. (1994). Log-normal distribution of physiological parameters and the coherence of biological systems. Medical Hypotheses, 43(1):11- 16.
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Paper Citation


in Harvard Style

Lüdtke S., Hein A., Krüger F., Bader S. and Kirste T. (2017). Actigraphic Sleep Detection for Real-World Data of Healthy Young Adults and People with Alzheimer’s Disease . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017) ISBN 978-989-758-212-7, pages 185-192. DOI: 10.5220/0006158801850192


in Bibtex Style

@conference{biosignals17,
author={Stefan Lüdtke and Albert Hein and Frank Krüger and Sebastian Bader and Thomas Kirste},
title={Actigraphic Sleep Detection for Real-World Data of Healthy Young Adults and People with Alzheimer’s Disease},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)},
year={2017},
pages={185-192},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006158801850192},
isbn={978-989-758-212-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)
TI - Actigraphic Sleep Detection for Real-World Data of Healthy Young Adults and People with Alzheimer’s Disease
SN - 978-989-758-212-7
AU - Lüdtke S.
AU - Hein A.
AU - Krüger F.
AU - Bader S.
AU - Kirste T.
PY - 2017
SP - 185
EP - 192
DO - 10.5220/0006158801850192