Fuzzy recurrence plot-based analysis of dynamic and static spiral tests of Parkinson's disease patients

Parkinson's disease (PD) is a chronic and progressive neurological illness affecting millions of people in the world. The cure for PD is not available. Drug therapies can handle some symptoms of disease like reducing tremor. PD is diagnosed with decrease in dopamine concentrations in the brain by using clinical tests. Early detection of the disease is important for the treatment. In this study, dynamic spiral test (DST) and static spiral test (SST) of PD patients were analyzed with pre-trained deep learning algorithms for early detection of PD. Fuzzy recurrence plot (FRP) technique was used to convert time-series signals to grayscale texture images. Several time-series signals were tested to observe the performances. The deep learning algorithms were employed as classifiers and feature extractors. Drawing and signal types' performances for classifying PD were comprehensively investigated. In short, according to the experimental results Y signal produced the best results in DST approach and arithmetic combination of the Y and P signals performed better in SST method.

Automated summary

Parkinson's disease is a chronic and progressive neurological illness with no cure currently available, making early detection crucial for treatment. A study analyzed tests of PD patients using deep learning algorithms, finding that the Y signal produced the best results in the DST approach and the combination of Y and P signals performed better in the SST method.