Detection and assessment of the severity of Levodopa-induced dyskinesia in patients with Parkinson's disease by neural networks

• Published in: Movement disorders Vol. 15; no. 6; pp. 1104 - 1111
• Main Authors: Keijsers, N. L. W., Horstink, M. W. I. M., van Hilten, J. J., Hoff, J. I., Gielen, C. C. A. M.
• Format: Journal Article
• Language: English
• Published: New York John Wiley & Sons, Inc 01.11.2000; Wiley
• Subjects: Accelerometers; Activities of Daily Living; Adult; Antiparkinson Agents - adverse effects; Automatic assessment; Biological and medical sciences; Degenerative and inherited degenerative diseases of the nervous system. Leukodystrophies. Prion diseases; Diagnosis, Computer-Assisted - methods; Diagnosis, Differential; Dyskinesia, Drug-Induced - diagnosis; Feasibility Studies; Humans; Levodopa - adverse effects; Levodopa-induced dyskinesia; Medical sciences; Movement - drug effects; Neural networks; Neural Networks (Computer); Neurology; Parkinson Disease - diagnosis; Parkinson Disease - drug therapy; Parkinson's disease; Predictive Value of Tests; Severity of Illness Index; Human; Nervous system diseases; Toxicity; Quantitative character; Parkinson disease; Neural network; Antiparkinson agent; Cerebral disorder; Involuntary movement; Chemotherapy; Treatment; Central nervous system disease; Body movement; Degenerative disease; Severity score; Diagnosis; Levodopa; Technique; Neurological disorder; Accelerometry; Extrapyramidal syndrome; Dyskinesia
• DOI: 10.1002/1531-8257(200011)15:6<1104::AID-MDS1007>3.0.CO;2-E
• ISSN: 0885-3185

Levodopa‐induced dyskinesias (LID) in Parkinson's disease (PD) have remained a clinical challenge. We evaluated the feasibility of neural networks to detect LID and to quantify their severity in 16 patients with PD at rest and during various activities of daily living. The movements of the patients were measured using four pairs of accelerometers mounted on the wrist, upper arm, trunk, and leg on the most affected side. Using parameters obtained from the accelerometer signals, neural networks were trained to detect and to classify LID corresponding to the modified Abnormal Involuntary Movement Scale. Important parameters for classification appeared to be the mean segment velocity and the cross‐correlation between accelerometers on the arm, trunk, and leg. Neural networks were able to distinguish voluntary movements from LID and to assess the severity of LID in various activities. Based on the results in this study, we conclude that neural networks are a valid and reliable method to detect and to assess the severity of LID corresponding to the modified Abnormal Involuntary Movement Scale.