NCLGMar 10, 2022

A multimodal approach for Parkinson disease analysis

arXiv:2203.15517v15 citationsh-index: 44
Originality Synthesis-oriented
AI Analysis

This work addresses diagnostic challenges for Parkinson's disease patients, but it is incremental as it focuses on correlating existing simple analyses with established methods.

The paper tackles the problem of diagnosing swallowing and balance impairments in Parkinson's disease by evaluating voice and handwriting analysis as predictors, aiming to provide simpler and less stressful diagnostic tests compared to the gold standard video-fluoroscopic analysis.

Parkinson's disease (PD) is the second most frequent neurodegenerative disease with prevalence among general population reaching 0.1-1 %, and an annual incidence between 1.3-2.0/10000 inhabitants. The mean age at diagnosis of PD is 55 and most patients are between 50 and 80 years old. The most obvious symptoms are movement-related; these include tremor, rigidity, slowness of movement and walking difficulties. Frequently these are the symptoms that lead to the PD diagnoses. Later, thinking and behavioral problems may arise, and other symptoms include cognitive impairment and sensory, sleep and emotional problems. In this paper we will present an ongoing project that will evaluate if voice and handwriting analysis can be reliable predictors/indicators of swallowing and balance impairments in PD. An important advantage of voice and handwritten analysis is its low intrusiveness and easy implementation in clinical practice. Thus, if a significant correlation between these simple analyses and the gold standard video-fluoroscopic analysis will imply simpler and less stressing diagnostic test for the patients as well as the use of cheaper analysis systems.

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