Skip to main content

Advertisement

Log in

Movement analysis by accelerometry of newborns and infants for the early detection of movement disorders due to infantile cerebral palsy

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

Abstract

So far, developed diagnostic strategies for the early detection of movement disorders due to infantile cerebral palsy (ICP) in newborns are not easily applicable in clinical settings. They are either difficult to acquire or they are too expensive to be established in pediatric clinics and are not sufficiently usable to be integrated into daily routine. The aim of this study therefore was to develop a methodology that allows the objective diagnosis of developing movement disorders in newborns due to ICP. It should be applicable to pediatric offices and should easily integrate in daily routine. To achieve this, a simple to use and low-cost system based on accelerometers was developed to evaluate the newborn’s movement. Afterward, a classificator based on a decision tree algorithm was implemented to differentiate between healthy and pathological data in order to propose the most likely diagnosis. The developed methodology was validated in a clinical study with 19 healthy and 4 affected subjects that were evaluated at the first, third and fifths month after birth (corrected age). The overall detection rate of the developed methodology reached between 88 and 92% for all evaluated measurements. The developed methodology is simple to use, therefore is applicable for the objective diagnosis of developing movement disorders in newborns due to ICP and can be established in pediatric offices for use in daily routine.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Adde L, Rygg M, Lossius K, Oberg GK, Stoen R (2007) General movement assessment: predicting cerebral palsy in clinical practise. Early Hum Dev 83:13–18

    Article  PubMed  Google Scholar 

  2. Albert AE, Gardner LA (1967) Stochastic approximation and nonlinear regression. MIT Press, Cambridge, Mass

    Google Scholar 

  3. Aminian K, Robert P, Buchser EE, Rutschmann B, Hayoz D, Depairon M (1999) Physical activity monitoring based on accelerometry: validation and comparison with video observation. Med Biol Eng Comput 37:304–308

    Article  CAS  PubMed  Google Scholar 

  4. Ancel PY, Livinec F, Larroque B, Marret S, Arnaud C, Pierrat V, Dehan M, N’Guyen S, Escande B, Burguet A, Thiriez G, Picaud JC, André M, Bréart G, Kaminski M, EPIPAGE Study Group (2006) Cerebral palsy among very preterm children in relation to gestational age and neonatal ultrasound abnormalities: the EPIPAGE Cohort Study. Pediatrics 117:828–835

    Article  PubMed  Google Scholar 

  5. Aßmann B, Romano MC, Thiel M, Niemitz C (2007) Hierarchical organization of a reference system in newborn spontaneous movements. Infant Behav Dev 30:568–586

    Article  PubMed  Google Scholar 

  6. Berge PR, Adde L, Espinosa G, Stavdahl Ø (2008) ENIGMA––enhanced interactive general movement assessment. Expert Syst Appl 34:2664–2672

    Article  Google Scholar 

  7. Blauw-Hospers CH, Hadders-Algra M (2005) A systematic review of the effects of early intervention on motor development. Dev Med Child Neurol 47:421–432

    Article  PubMed  Google Scholar 

  8. Brunnekreef JJ, van Uden CJ, van Moorsel S, Kooloos JG (2005) Reliability of videotaped observational gait analysis in patients with orthopaedic impairments. BMC Musculoskelet Disord 6:17–25

    Article  PubMed  Google Scholar 

  9. Bussmann JBJ, van de Laar YM, Neeleman MP, Stam HJ (1998) Ambulatory accelerometry to quantify motor behaviour in patients after failed back surgery: a validation study. Pain 74(2):153–161

    Article  CAS  PubMed  Google Scholar 

  10. Culhane KM, O’Conner M, Lyons GM (2005) Accelerometers in rehabilitation medicine for older adults. Age Ageing 34:556–560

    Article  CAS  PubMed  Google Scholar 

  11. Duda RO, Hart PE, Stork DG (1999) Pattern classification. Wiley, New York

    Google Scholar 

  12. Einspieler C, Prechtl HF (2005) Prechtl′s assessment of general movements: a diagnostic tool for the functional assessment of the young nervous system. Ment Retard Develop Disabil Res Rev 11:61–67

    Article  Google Scholar 

  13. Foerster F, Smeja M, Fahrenberg J (1999) Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring. Comput Hum Behav 15:571–583

    Article  Google Scholar 

  14. Geurts P (2002) Contributions to decision tree induction: bias/variance trade-off and time series classification. University of Liège, Belgium

    Google Scholar 

  15. Groen SE, Blecourt AC, de Postema K, Hadders-Algra M (2005) General movements in early infancy predict neuromotor development at 9 to 12 years of age. Dev Med Child Neurol 47:731–738

    Article  PubMed  Google Scholar 

  16. Hoos MB, Kuipers H, Gerver WJ, Westerterp KR (2004) Physical activity pattern of children assessed by triaxial accelerometry. Eur J Clin Nutr 58:1425–1428

    Article  CAS  PubMed  Google Scholar 

  17. Janssen WG, Bussmann JB, Horemans HL, Stam HJ (2008) Validity of accelerometry in assessing the duration of the sit-to-stand movement. Med Biol Eng Comput 46(9):879–887

    Article  PubMed  Google Scholar 

  18. Kaaresen PI, Rønning JA, Tunby J, Nordhov SM, Ulvund SE, Dahl LB (2008) A randomized controlled trial of an early intervention program in low birth weight children: outcome at 2 years. Early Hum Dev 84(3):201–209

    Article  PubMed  Google Scholar 

  19. Kadaba MP, Ramakrishnan HK, Wootten ME, Gainey J, Gorton G, Cochran GVB (1989) Repeatability of kinematic, kinetic, and electromyographic data in normal adult gait. J Orthop Res 7:849–860

    Article  CAS  PubMed  Google Scholar 

  20. Kavanagh JJ, Morrison S, James DA, Barrett R (2006) Reliability of segmental accelerations measured using a new wireless gait analysis system. J Biomech 39:2863–2872

    Article  PubMed  Google Scholar 

  21. Keijsers NL, Horstink MW, Gielen SC (2006) Ambulatory motor assessment in Parkinson’s disease. Mov Disord 2l:34–44

    Article  Google Scholar 

  22. Kosmidou VE, Hadjileontiadis LI (2010) Using sample entropy for automated sign language recognition on sEMG and accelerometer data. Med Biol Eng Comput 48(3):255–267

    Google Scholar 

  23. Krägeloh-Mann I (2007) Zerebralparesen update (cerebral palsy update). Monatsschrift Kinderheilkunde 155:523–528

    Article  Google Scholar 

  24. Lau HY, Tong KY, Zhu H (2008) Support vector machine for classification of walking conditions using miniature kinematic sensors. Med Biol Eng Comput 46(6):563–573

    Article  PubMed  Google Scholar 

  25. LeMoyne R, Jafari R (2008) Quantified deep tendon reflex device, second generation. J Mech Med Biol 8:75–85

    Article  Google Scholar 

  26. LeMoyne R, Coroian C, Mastroianni T, Grundfest W (2008) Accelerometers for quantification of gait and movement disorders: a perspective review. J Mech Med Biol 8(2):137–152

    Article  Google Scholar 

  27. Lennon S, Johnson L (2000) The modified rivermead mobility index: validity and reliability. Disabil Rehabil 22:833–839

    Article  CAS  PubMed  Google Scholar 

  28. Lötters JC, Schipper J, Veltink PH, Olthuis W (1999) Procedure for in-use calibration of triaxial accelerometers applications. Sens Actuators 68:221–228

    Article  Google Scholar 

  29. Luca P (1997) Fast feature selection with genetic algorithms a filter approach. Lanzi Dipartimento di Elettronica e Informazione

  30. Mathie MJ, Celler BG, Lovell NH, Coster AC (2004) Classification of basic daily movements using a triaxial accelerometer. Med Biol Eng Comput 42(5):679–687

    Article  CAS  PubMed  Google Scholar 

  31. Mayagoitia RE, Nene AV, Veltink PH (2002) Accelerometer and rate gyroscope measurement of kinematics: an inexpensive alternative to optical motion analysis systems. J Biomech 35(4):537–542

    Article  PubMed  Google Scholar 

  32. Meinecke L, Breitbach-Faller N, Bartz C, Damen R, Rau G, Disselhorst-Klug C (2006) Movement analysis in the early detection of newborns at risk for developing spasticity due to infantile cerebral palsy. Hum Mov Sci 25:125–144

    Article  CAS  PubMed  Google Scholar 

  33. Menz HB, Lord SR, Fitzpatrick RC (2003) Age-related differences in walking stability. Age Ageing 32:137–142

    Article  PubMed  Google Scholar 

  34. Moe-Nilssen R, Helbostad JL (2004) Estimation of gait cycle characteristics by trunk accelerometry. J Biomech 37:121–126

    Article  PubMed  Google Scholar 

  35. Ohgi S, Morita S, Loo KK, Mizuike C (2008) Time series analysis of spontaneous upper-extremity movements of premature infants with brain injuries. Phys Ther [Epub ahead of print]

  36. Palmer FB (2004) Strategies for the early diagnosis of cerebral palsy. J Pediatr 145(2):8–11

    Google Scholar 

  37. Patrick SK, Denington AA, Gauthier MJ, Gillard DM, Prochazka A (2001) Quantification of the UPDRS rigidity scale. IEEE Trans Neural Syst Rehabil Eng 9(1):31–41

    Article  CAS  PubMed  Google Scholar 

  38. Perry J (1992) Gait analysis: normal and pathological function. Slack, Thorofare, NJ

    Google Scholar 

  39. Prechtl HFR (2001) General movement assessment as a method of developmental neurology: new paradigms and their consequences. Dev Med Child Neurol 43:836–842

    Article  CAS  PubMed  Google Scholar 

  40. Prechtl HFR, Einspieler C, Bos A, Cioni G, Ferrari F (1997) Spontaneous motor activity as a diagnostic tool––function assessment of the young nervous system––a scientific illustration of Prechtl’s method. The GM Trust, London, Graz

    Google Scholar 

  41. Saremi K, Marehbian J, Yan X, Regnaux JP, Elashoff R, Bussel B, Dobkin BH (2006) Reliability and validity of bilateral thigh and foot accelerometry measures of walking in healthy and hemiparetic subjects. Neurorehabil Neural Repair 20:297–305

    Article  PubMed  Google Scholar 

  42. Stahlmann N, Härtel C, Knopp A, Gehring B, Kiecksee H, Thyen U (2007) Predictive value of neurodevelopmental assessment versus evaluation of general movements for motor outcome in preterm infants with birth weights <1500 g. Neuropediatrics 38:91–99

    Article  CAS  PubMed  Google Scholar 

  43. Vaillancourt DE, Newell KM (2000) The dynamic of resting and postural tremor in Parkinson’s disease. Clin Neurophysiol 111:2046–2056

    Article  CAS  PubMed  Google Scholar 

  44. van den Bogert AJ, Read L, Nigg BM (1996) A method for inverse dynamic analysis using accelerometry. J Biomech 29:949–954

    Article  PubMed  Google Scholar 

  45. Vincer MJ, Allen AC, Joseph KS, Stinson DA, Scott H, Wood E (2006) Increasing prevalence of cerebral palsy among very preterm infants: a population-based study. Pediatrics 118:1621–1626

    Article  Google Scholar 

  46. Warner HR, Sorenson DK, Bouhaddou O (1997) Knowledge engineering in health informatics. Springer, New York

    Google Scholar 

  47. Yang J, Honavar V (1998) Feature subject selection using a genetic algorithm. Iowa State University

  48. Yanga J (2006) A simple approach to integration of acceleration data for dynamic soil-structure interaction analysis. Soil Dyn Earthquake Eng 26(8):725–734

    Article  Google Scholar 

Download references

Acknowledgments

The authors gratefully acknowledge the financial support provided by the German Research Council (Deutsche Forschungsgemeinschaft DFG, DI 596/5-1).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Franziska Heinze.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Heinze, F., Hesels, K., Breitbach-Faller, N. et al. Movement analysis by accelerometry of newborns and infants for the early detection of movement disorders due to infantile cerebral palsy. Med Biol Eng Comput 48, 765–772 (2010). https://doi.org/10.1007/s11517-010-0624-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-010-0624-z

Keywords

Navigation