Automatic assessment of dyskinesia in Parkinsons disease in daily
life
N. Keijsers, M.W.I.M. Horstink and C.C.A.M. Gielen
Radboud University, Nijmegen,
the Netherlands
During the first years of levodopa treatment, patients with Parkinsons
disease (PD) have a stable response to levodopa. However, after several
years of levodopa treatment, an increasing number of patients show fluctuations
in motor response (on-off fluctuations) and levodopa
induced dyskinesias (abnormal involuntary movements). These complications
constitute a major problem in the long-term management of PD and add substantially
to the patients disability. New pharmacological and surgical treatments
to reduce levodopa induced dyskinesias are becoming of more and more interest.
Therefore, an automatic and portable device that can assess LID automatically
and objectively in daily life is highly useful.
Methods
13 patients were continuously monitored in a home-like situation for a
period of approximately 2.5 hours. During this 2.5-hour period, the patients
performed about 35 functional daily-life activities. Behavior of the patients
was measured using triaxial accelerometers, which were placed on 6 different
positions of 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.
Results
Neural network correctly classi.ed 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 few cases of misclassi.cation, the rating by the
neural network was in the class next to that indicated by the physician
using the AIMS-score (0-4). The percentage of time that a segment was
moving was the most important parameter used by the neural network. For
the leg mainly parameters of both legs were important. For the arm and
especially for the trunk, parameters, related to movements of other body
segments, were relevant. Dyskinesia appeared to be more dominant in the
lower frequencies than in higher frequencies.
Conclusions
The neural network could accurately assess the severity of LID and distinguish
LID from voluntary movements in daily life situations. The results suggest
that the method could be operating successfully in unsupervised ambulatory
conditions.
Paper presented
at Measuring Behavior 2005
, 5th International Conference on Methods and Techniques
in Behavioral Research, 30 August - 2 September 2005, Wageningen, The
Netherlands.
© 2005 Noldus
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