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.

[1]  Arun Sharma,et al.  Diagnosis of Parkinson’s disease using modified grey wolf optimization , 2019, Cognitive Systems Research.

[2]  Luiz C F Ribeiro,et al.  Bag of Samplings for computer-assisted Parkinson's disease diagnosis based on Recurrent Neural Networks , 2019, Comput. Biol. Medicine.

[3]  Clayton R. Pereira,et al.  A recurrence plot-based approach for Parkinson's disease identification , 2019, Future Gener. Comput. Syst..

[4]  Jesús Francisco Vargas-Bonilla,et al.  Analysis and evaluation of handwriting in patients with Parkinson's disease using kinematic, geometrical, and non-linear features , 2019, Comput. Methods Programs Biomed..

[5]  Guandong Xu,et al.  Refining Parkinson’s neurological disorder identification through deep transfer learning , 2019, Neural Computing and Applications.

[6]  Betul Erdogdu Sakar,et al.  Improved spiral test using digitized graphics tablet for monitoring Parkinson's disease , 2014 .

[7]  Fethullah Karabiber,et al.  A Machine Learning System for the Diagnosis of Parkinson’s Disease from Speech Signals and Its Application to Multiple Speech Signal Types , 2016, Arabian Journal for Science and Engineering.

[8]  Clayton R. Pereira,et al.  Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification , 2018, Artif. Intell. Medicine.

[9]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[10]  Max A. Little,et al.  Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease , 2012, IEEE Transactions on Biomedical Engineering.

[11]  Rinkle Rani,et al.  Diagnosis of Parkinson's Disease Using Principle Component Analysis and Deep Learning , 2019 .

[12]  N. Arunkumar,et al.  Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson’s disease , 2019, Cognitive Systems Research.

[13]  Ana Madureira,et al.  Automatic detection of Parkinson's disease based on acoustic analysis of speech , 2019, Eng. Appl. Artif. Intell..

[14]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Dimitrios Hristu-Varsakelis,et al.  Machine learning-based classification of simple drawing movements in Parkinson's disease , 2017, Biomed. Signal Process. Control..

[16]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[17]  Jürgen Kurths,et al.  Recurrence plots for the analysis of complex systems , 2009 .

[18]  M. Samuel,et al.  Handwriting as an objective tool for Parkinson’s disease diagnosis , 2013, Journal of Neurology.

[19]  Claudio Gallicchio,et al.  Deep Echo State Networks for Diagnosis of Parkinson's Disease , 2018, ESANN.

[20]  Tuan D. Pham,et al.  Fuzzy recurrence plots , 2016, Fuzzy Recurrence Plots and Networks with Applications in Biomedicine.

[21]  İsmail Cantürk,et al.  A Deep Learning-CNN Based System for Medical Diagnosis: An Application on Parkinson’s Disease Handwriting Drawings , 2018, 2018 6th International Conference on Control Engineering & Information Technology (CEIT).

[22]  Fikret S. Gürgen,et al.  Collection and Analysis of a Parkinson Speech Dataset With Multiple Types of Sound Recordings , 2013, IEEE Journal of Biomedical and Health Informatics.

[23]  J. Jankovic.  Parkinson’s disease: clinical features and diagnosis , 2008, Journal of Neurology, Neurosurgery, and Psychiatry.

[24]  Shalini Jha,et al.  The diagnosis of Parkinson's disease. , 2003, Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology.

[25]  Clayton R. Pereira,et al.  Deep Learning-Aided Parkinson's Disease Diagnosis from Handwritten Dynamics , 2016, 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI).

[26]  C. Tanner,et al.  Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030 , 2007, Neurology.

[27]  H. Kirshner,et al.  Swallowing and speech production in Parkinson's disease , 1986, Annals of neurology.

[28]  Fadi A. Aloul,et al.  ParkNosis: Diagnosing Parkinson's disease using mobile phones , 2016, 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom).

[29]  Rytis Maskeliunas,et al.  Detecting Parkinson's disease with sustained phonation and speech signals using machine learning techniques , 2019, Pattern Recognit. Lett..

[30]  Neha Singh,et al.  Advances in the treatment of Parkinson's disease , 2007, Progress in Neurobiology.

[31]  Poonam Zham,et al.  Distinguishing Different Stages of Parkinson’s Disease Using Composite Index of Speed and Pen-Pressure of Sketching a Spiral , 2017, Front. Neurol..

[32]  Tuan D. Pham,et al.  Texture Classification and Visualization of Time Series of Gait Dynamics in Patients With Neuro-Degenerative Diseases , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[33]  D. Ruelle,et al.  Recurrence Plots of Dynamical Systems , 1987 .

[34]  Giuseppe Pirlo,et al.  Dynamic Handwriting Analysis for the Assessment of Neurodegenerative Diseases: A Pattern Recognition Perspective , 2019, IEEE Reviews in Biomedical Engineering.