Classification and characterisation of movement patterns during levodopa therapy for parkinson's disease

Abstract

Parkinson's disease is a chronic neurodegenerative condition that manifests clinically with various movement disorders. These are often treated with the dopamine-replacement drug levodopa. However, the dosage of levodopa must be kept as low as possible in order to avoid the drug's side e↵ects, such as the involuntary, and often violent, muscle spasms called dyskinesia, or levodopa-induced dyskinesia. In this paper, we investigate the use of genetic programming for training classifiers that can monitor the e↵ectiveness of levodopa therapy. In particular, we evolve classifiers that can recognise tremor and dyskinesia, movement states that are indicative of insu cient or excessive doses of levodopa, respectively. The evolved classifiers achieve clinically useful rates of discrimination, with AUC>0.9. We also find that temporal classifiers generally out-perform spectral classifiers. By using classifiers that respond to low-level features of the data, we identify the conserved patterns of movement that are used as a basis for classification, showing how this approach can be used to characterise as well as classify abnormal movement.

Computational approaches for understanding the diagnosis and treatment of Parkinson's disease

“…Provisional results show that the device has excellent accuracy (AUC 0.9) for monitoring severe and moderate degrees of dyskinesia. Providing this kind of objective clinical information to clinicians would enable them to make more informed decisions regarding administration of medication and also could be used to evaluate new drugs for its treatment [50].…”