SwePub
Sök i LIBRIS databas

  Utökad sökning

id:"swepub:oai:DiVA.org:liu-46363"
 

Sökning: id:"swepub:oai:DiVA.org:liu-46363" > Classification of N...

Classification of Neck Movement Patterns Related to Whiplash-Associated Disorders Using Neural Networks

Grip, Helena (författare)
Umeå universitet,Institutionen för strålningsvetenskaper,Department of Biomedical Engineering and Informatics, University Hospital, Umeå, Sweden
Öhberg, Fredrik (författare)
Umeå universitet,Radiofysik,Department of Biomedical Engineering and Informatics, University Hospital, Umeå, Sweden
Wiklund, Urban (författare)
Umeå universitet,Radiofysik,Department of Biomedical Engineering and Informatics, University Hospital, Umeå, Sweden
visa fler...
Sterner, Ylva (författare)
Karolinska Institutet
Karlsson, J Stefan (författare)
Umeå universitet,Institutionen för strålningsvetenskaper,Department of Biomedical Engineering and Informatics, University Hospital, Umeå, Sweden
Gerdle, Björn (författare)
Östergötlands Läns Landsting,Linköpings universitet,Rehabiliteringsmedicin,Hälsouniversitetet,Smärt- och rehabiliteringscentrum
visa färre...
 (creator_code:org_t)
IEEE, 2003
2003
Engelska.
Ingår i: IEEE transactions on information technology in biomedicine. - : IEEE. - 1089-7771 .- 1558-0032. ; 7:4, s. 412-418
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • This paper presents a new method for classification of neck movement patterns related to Whiplash-associated disorders (WAD) using a resilient backpropagation neural network (BPNN). WAD are a common diagnosis after neck trauma, typically caused by rear-end car accidents. Since physical injuries seldom are found with present imaging techniques, the diagnosis can be difficult to make. The active range of the neck is often visually inspected in patients with neck pain, but this is a subjective measure, and a more objective decision support system, that gives a reliable and more detailed analysis of neck movement pattern, is needed. The objective of this study was to evaluate the predictive ability of a BPNN, using neck movement variables as input. Three-dimensional (3-D) neck movement data from 59 subjects with WAD and 56 control subjects were collected with a ProReflex system. Rotation angle and angle velocity were calculated using the instantaneous helical axis method and motion variables were extracted. A principal component analysis was performed in order to reduce data and improve the BPNN performance. BPNNs with six hidden nodes had a predictivity of 0.89, a sensitivity of 0.90 and a specificity of 0.88, which are very promising results. This shows that neck movement analysis combined with a neural network could build the basis of a decision support system for classifying suspected WAD, even though further evaluation of the method is needed.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Medicinteknik -- Annan medicinteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Medical Engineering -- Other Medical Engineering (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Neurovetenskaper (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Neurosciences (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Fysiologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Physiology (hsv//eng)

Nyckelord

Decision support system
Head rotation
Instantaneous helical axis
Motion analysis
Neural network
Resilient backpropagation
Whiplash-associated disorders (WAD)

Publikations- och innehållstyp

ref (ämneskategori)
art (ämneskategori)

Hitta via bibliotek

Till lärosätets databas

Sök utanför SwePub

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy