Abstract
Parkinsons disease (PD) is currently incurable, however the proper treatment can ease the symptoms and significantly improve the quality of patients life. Since PD is a chronic disease, its efficient monitoring and management is very important. The objective of this paper is to investigate the feasibility of using the features and methodology of a spirography device, originally designed to measure early Parkinsons disease (PD) symptoms, for assessing motor symptoms of advanced PD patients suffering from motor fluctuations. More specifically, the aim is to objectively assess motor symptoms related to bradykinesias (slowness of movements occurring as a result of under-medication) and dyskinesias (involuntary movements occurring as a result of over-medication). The work combines spirography data and clinical assessments from a longitudinal clinical study in Sweden with the features and pre-processing methodology of a Slovenian spirography application. The target outcome was to learn to predict the “cause” of upper limb motor dysfunctions as assessed by a clinician who observed animated spirals in a web interface. Using the machine learning methods with feature descriptions from the Slovenian application resulted in 86% classification accuracy and over 90% AUC, demonstrating the usefulness of this approach for objective monitoring of PD patients.
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Sadikov, A., Žabkar, J., Možina, M., Groznik, V., Nyholm, D., Memedi, M. (2015). Feasibility of Spirography Features for Objective Assessment of Motor Symptoms in Parkinson’s Disease. In: Holmes, J., Bellazzi, R., Sacchi, L., Peek, N. (eds) Artificial Intelligence in Medicine. AIME 2015. Lecture Notes in Computer Science(), vol 9105. Springer, Cham. https://doi.org/10.1007/978-3-319-19551-3_35
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DOI: https://doi.org/10.1007/978-3-319-19551-3_35
Publisher Name: Springer, Cham
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