Classification of Parkinson’s Disease Using Dynamic Time Warping

Deteriorations in handwriting or in basic shape sketching are one of the most referenced symptoms for early diagnosis of Parkinson’s disease (PD). For this reason, the design of a fair, trustworthy and efficacious Computer-aided Diagnosis (CAD) model has supportive importance for the early diagnosis of PD. In this study we investigate the effectiveness of Dynamic Time Warping (DTW) algorithm, which is applied to Archimedean spiral drawings of patients with PD and healthy controls (HC), on PD and healthy subject classification problem. Leave-one-subject-out (LOSO) cross validation scheme is used while training and testing in support vector machine (SVM) and k-nearest neighbors (k-NN) classifiers with various parameters. The accuracy results of% 94.44 (% 95.83) and % 97.52 (% 94.44) are achieved by k-NN and SVM classifiers respectively for static (dynamic) spiral test.

[1]  J. C. Vásquez-Correa,et al.  Word accuracy and dynamic time warping to assess intelligibility deficits in patients with Parkinsons disease , 2016, 2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA).

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

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

[4]  Werner Poewe,et al.  Role of dopamine transporter imaging in investigation of parkinsonian syndromes in routine clinical practice , 2003, Movement disorders : official journal of the Movement Disorder Society.

[5]  Susanne Pirker,et al.  Dopamine transporter imaging in autopsy‐confirmed Parkinson's disease and multiple system atrophy , 2012, Movement disorders : official journal of the Movement Disorder Society.

[6]  R. Lipton,et al.  Validity of spiral analysis in early Parkinson's disease , 2008, Movement disorders : official journal of the Movement Disorder Society.

[7]  Rohit J. Kate.  Using dynamic time warping distances as features for improved time series classification , 2016, Data Mining and Knowledge Discovery.

[8]  Zdenek Smekal,et al.  Decision Support Framework for Parkinson’s Disease Based on Novel Handwriting Markers , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  Poonam Zham,et al.  Efficacy of Guided Spiral Drawing in the Classification of Parkinson's Disease , 2018, IEEE Journal of Biomedical and Health Informatics.

[10]  Wenyu Zhang,et al.  Multivariate Time Series Classification Using Dynamic Time Warping Template Selection for Human Activity Recognition , 2015, 2015 IEEE Symposium Series on Computational Intelligence.

[11]  Marcos Faúndez-Zanuy,et al.  Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease , 2016, Artif. Intell. Medicine.

[12]  Joav Merrick,et al.  Neurological Disorders: Public Health Challenges , 2007 .

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

[14]  Sezer Ulukaya,et al.  Musical Feature Based Classification of Parkinson's Disease Using Dysphonic Speech , 2018, 2018 41st International Conference on Telecommunications and Signal Processing (TSP).

[15]  Yi-Chi Huang,et al.  Intelligent fall prevention for Parkinson's disease patients based on detecting posture instabilily and freezing of gait , 2015, 2015 12th International Conference on Informatics in Control, Automation and Robotics (ICINCO).

[16]  D. Bergen,et al.  Read Neurological Disorders Public Health Challenges Neurological Disorders Public Health Challenges , 2017 .

[17]  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).

[18]  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).