Skip to main content

Advertisement

SpringerLink
Log in

Automatic identification of activity–rest periods based on actigraphy

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

We describe a novel algorithm for identification of activity/rest periods based on actigraphy signals designed to be used for a proper estimation of ambulatory blood pressure monitoring parameters. Automatic and accurate determination of activity/rest periods is critical in cardiovascular risk assessment applications including the evaluation of dipper versus non-dipper status. The algorithm is based on adaptive rank-order filters, rank-order decision logic, and morphological processing. The algorithm was validated on a database of 104 subjects including actigraphy signals for both the dominant and non-dominant hands (i.e., 208 actigraphy recordings). The algorithm achieved a mean performance above 94.0%, with an average number of 0.02 invalid transitions per 48 h.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

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

    PubMed  Google Scholar 

  2. Blood ML, Sack RL, Percy DC, Pen JC (1997) A comparison of sleep detection by wrist actigraphy, behavioral response, and polysomnography. Sleep 20: 388–395

    PubMed  CAS  Google Scholar 

  3. Boggia J, Li Y, Thijs L, Hansen TW, Kikuya M, Bjorklund-Bodegsrd K, Richart T, Ohkubo T, Kuznetsova T, Torp-Pedersen C et al (2007) Prognostic accuracy of day versus night ambulatory blood pressure: a cohort study. Lancet 370: 1219–1229

    Article  PubMed  Google Scholar 

  4. Cole RJ, Kripke DF, Gruen W, Mullaney DJ, Gillin JC (1992) Automatic sleep/wake identification from wrist actigraphy Sleep 15: 461–469

    CAS  Google Scholar 

  5. Cuspidi C, Macca G, Sampieri L, Fusi V, Severgnini B, Michev I, Salerno M, Magrini F, Zanchetti A (2001) Target organ damage and non-dipping pattern defined by two sessions of ambulatory blood pressure monitoring in recently diagnosed essential hypertensive patients. J Hypertens 19: 1539–1545

    Article  PubMed  CAS  Google Scholar 

  6. Cuspidi C, Meani S, Salerno M, Valerio C, Fusi V, Severgnini B, Lonati L, Magrini F, Zanchetti A (2004) Cardiovascular target organ damage in essential hypertensives with or without reproducible nocturnal fall in blood pressure. J Hypertens 22: 273–280

    Article  PubMed  CAS  Google Scholar 

  7. Cuspidi C, Michev I, Meani S, Severgnini B, Fusi V, Corti C, Salerno M, Valerio C, Magrini F, Zanchetti A (2003) Reduced nocturnal fall in blood pressure, assessed by two ambulatory blood pressure monitorings and cardiac alterations in early phases of untreated essential hypertension. J Hum Hypertens 17: 245–251

    Article  PubMed  CAS  Google Scholar 

  8. Dolan E, Stanton A, Thijs L, Hinedi K, Atkins N, McClory S, Hond ED, McCormack P, Staessen JA, O’Brien E (2005) Superiority of ambulatory over clinic blood pressure measurement in predicting mortality: the Dublin outcome study. Hypertension 46: 156–161

    Article  PubMed  CAS  Google Scholar 

  9. Hermida RC (2007) Ambulatory blood pressure monitoring in the prediction of cardiovascular events and effects of chronotherapy: rationale and design of the MAPEC study. Chronobiol Int 24: 749–775

    Article  PubMed  Google Scholar 

  10. Hermida RC, Chayán L, Ayala DE, Mojón A, Domfnguez MJ, Fontao MJ, Soler R, Alonso I, Fernßndez JR (2009) Association of metabolic syndrome and blood pressure nondipping profile in untreated hypertension. Am J Hypertens 22: 307–313

    Article  PubMed  Google Scholar 

  11. Hilten JJV, Middelkoop HA, Kuiper SI, Kramer CG, Roos RA (1993) Where to record motor activity: an evaluation of commonly used sites of placement for activity monitors. Electroencephalogr Clin Neurophysiol 89: 359–362

    Article  PubMed  Google Scholar 

  12. Hyde M, O’Driscoll DM, Binette S, Galang C, Tan SK, Verginis N, Davey MJ, Horne RSC (2007) Validation of actigraphy for determining sleep and wake in children with sleep disordered breathing. J Sleep Res 16: 213–216

    Article  PubMed  Google Scholar 

  13. Jones SH, Hare DJ, Evershed K (2005) Actigraphic assessment of circadian activity and sleep patterns in bipolar disorder. Bipolar Disord 7: 176–186

    Article  PubMed  Google Scholar 

  14. Karlen W, Mattiussi C, Floreano D (2008) Improving actigraph sleep/wake classification with cardio-respiratory signals. Conf Proc IEEE Eng Med Biol Soc 2008: 5262–5265

    PubMed  Google Scholar 

  15. Krakoff LR (1993) Ambulatory blood pressure monitoring can improve cost-effective management of hypertension. Am J Hypertens 6: 220–224

    PubMed  CAS  Google Scholar 

  16. Krouse HJ, Yarandi H, McIntosh J, Cowen C, Selim V (2008) Assessing sleep quality and daytime wakefulness in asthma using wrist actigraphy. J Asthma 45: 389–395

    Article  PubMed  Google Scholar 

  17. Lee JK, Park EJ (2011) 3D spinal motion analysis during staircase walking using an ambulatory inertial and magnetic sensing system. Med Biol Eng Comput 49: 755–764

    Article  PubMed  Google Scholar 

  18. Lee JK, Park EJ (2011) Quasi real-time gait event detection using shank-attached gyroscopes. Med Biol Eng Comput 49: 707–712

    Article  PubMed  Google Scholar 

  19. Liu M, Takahashi H, Morita Y, Maruyama S, Mizuno M, Yuzawa Y, Watanabe M, Toriyama T, Kawahara H, Matsuo S (2003) Non-dipping is a potent predictor of cardiovascular mortality and is associated with autonomic dysfunction in haemodialysis patients. Nephrol Dial Transplant 18: 563–569

    Article  PubMed  Google Scholar 

  20. Lockley SW, Skene DJ, Arendt J (1999) Comparison between subjective and actigraphic measurement of sleep and sleep rhythms. J Sleep Res 8: 175–183

    Article  PubMed  CAS  Google Scholar 

  21. Morgenthaler T, Alessi C, Friedman L, Owens J, Kapur V, Boehlecke B, Brown T, Chesson A, Coleman J, Lee-Chiong T et al (2007) Practice parameters for the use of actigraphy in the assessment of sleep and sleep disorders: an update for 2007. Sleep 30: 519–529

    PubMed  Google Scholar 

  22. Ohinata J, Suzuki N, Araki A, Takahashi S, Fujieda K, Tanaka H (2008) Actigraphic assessment of sleep disorders in children with chronic fatigue syndrome. Brain Dev 30: 329–333

    Article  PubMed  Google Scholar 

  23. Sadeh A, Acebo C (2002) The role of actigraphy in sleep medicine. Sleep Med Rev 6: 113–124

    Article  PubMed  Google Scholar 

  24. Sadeh A, Hauri PJ, Kripke DF, Lavie P (1995) The role of actigraphy in the evaluation of sleep disorders. Sleep 18: 288–302

    PubMed  CAS  Google Scholar 

  25. Sadeh A, Sharkey KM, Carskadon MA (1994) Activity-based sleep-wake identification: an empirical test of methodological issues. Sleep 17: 201–207

    PubMed  CAS  Google Scholar 

  26. Schepers HM, Roetenberg D, Veltink PH (2010) Ambulatory human motion tracking by fusion of inertial and magnetic sensing with adaptive actuation. Med Biol Eng Comput 48: 27–37

    Article  PubMed  Google Scholar 

  27. Tonetti L, Pasquini F, Fabbri M, Belluzzi M, Natale V (2008) Comparison of two different actigraphs with polysomnography in healthy young subjects. Chronobiol Int 25: 145–153

    Article  PubMed  Google Scholar 

  28. ValliFres A, Morin CM (2003) Actigraphy in the assessment of insomnia. Sleep 26: 902–906

    Google Scholar 

Download references

Acknowledgments

This independent investigator-promoted research was funded by unrestricted grants from Ministerio de Ciencia e Innovación (SAF2009-7028-FEDER); Consellería de Economía e Industria, Dirección Xeral de Investigación e Desenvolvemento, Xunta de Galicia (INCITE07-PXI-322003ES; INCITE08-E1R-322063ES; INCITE09-E2R-322099ES; IN845B-2010/114; 09CSA018322PR); and Vicerrectorado de Investigación, University of Vigo.

Author information

Authors and Affiliations

  1. EERE Department, Oregon Institute of Technology, Portland, OR, 97006, USA

    Cristina Crespo & Mateo Aboy

  2. Bioengineering and Chronobiology Laboratories, University of Vigo, Vigo, Spain

    José Ramón Fernández & Artemio Mojón

Authors
  1. Cristina Crespo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mateo Aboy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José Ramón Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Artemio Mojón
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cristina Crespo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Crespo, C., Aboy, M., Fernández, J.R. et al. Automatic identification of activity–rest periods based on actigraphy. Med Biol Eng Comput 50, 329–340 (2012). https://doi.org/10.1007/s11517-012-0875-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-012-0875-y

Keywords

Search

Navigation