PMID: 12518302: Automatic assessment of levodopa-induced dyskinesias

[janet paterson <janet313@xxxxxxxxxxx>]

hi all

'revealed several new variables' -
more cockle warming words!

janet

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Automatic assessment of levodopa-induced dyskinesias in daily life by
neural networks.

We developed an objective and automatic procedure to assess the severity of
levodopa-induced dyskinesia (LID) in patients with Parkinson's disease
during daily life activities.

13 patients were continuously monitored in a home-like situation for a
period of approximately 2.5 hours. During this time period, the patients
performed approximately 35 functional daily life activities.

Behavior of the patients was measured using triaxial accelerometers, which
were placed at six different positions on the body.

A neural network was trained to assess the severity of LID using various
variables of the accelerometer signals.

Neural network scores were compared with the assessment by physicians, who
evaluated the continuously videotaped behavior of the patients off-line.

The neural network correctly classified dyskinesia or the absence of
dyskinesia in 15-minute intervals in 93.7, 99.7, and 97.0% for the arm,
trunk, and leg, respectively.

In the few cases of misclassification, the rating by the neural network was
in the class next to that indicated by the physicians using the AIMS score
(scale 0-4).

Analysis of the neural networks revealed several new variables, which are
relevant for assessing the severity of LID.

The results indicate that the neural network can accurately assess the
severity of LID and could distinguish LID from voluntary movements in daily
life situations.

Mov Disord 2003 Jan;18(1):70-80
Keijsers NL, Horstink MW, Gielen SC.
Department of Biophysics, University of Nijmegen, Nijmegen, The Netherlands.
PMID: 12518302
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janet paterson: an akinetic rigid subtype, albeit primarily perky, parky
pd: 56-41-37 cd: 56-44-43 tel: 613-256-8340 email: janet313@xxxxxxxxxxx
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