Feature selection for an improved Parkinson's disease identification based on handwriting

Abstract

Parkinson's disease (PD) is a neurological disorder associated with a progressive decline in motor skills, speech, and cognitive processes. Since the diagnosis of Parkinson's disease is difficult, researchers have worked to develop a support tool based on algorithms to differentiate healthy controls from PD patients. Online handwriting analysis is one of the methods that can be used to diagnose PD. The aim of this study is to find a subset of handwriting features suitable for efficiently identifying subjects with PD. Data was taken from PDMultiMC database collected in Lebanon, and consisting of 16 medicated PD patients and 16 age matched controls. Seven handwriting tasks were collected such as copying patterns, copying words in Arabic, and writing full names. For each task kinematic and spatio-temporal, pressure, energy, entropy, and intrinsic features were extracted. Feature selection was done in two stages, the first stage selected a subset using statistical analysis, and the second step select the most relevant features of this subset, by a suboptimal approach. The selected features were fed to a support vector machine classifier with RBF kernel, whose aim is to identify the subjects suffering from PD. The accuracy of the classification of PD was as high as 96.875%, with sensitivity and specificity equal to 93.75 % and 100%. The results as well as the selected features suggest that handwriting can be a valuable marker as a diagnosis tool.

Characterization of the Handwriting Skills as a Biomarker for Parkinson’s Disease

“…Finally, Taleb et al report results of 96.87% when classifying between PD patients and HC subjects. Seven tasks are considered from a corpus with 16 PD/16 HC [11]. All these efforts and others have been summarized in recent surveys [12] [13].…”

Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification

“…Memedi et al [12] measured the disease progression in PD patients, which were asked to perform some handwritten exams at home, and Drotár et al [13] and Taleb et al [14] also considered handwriting features for PD evaluation, but focused on finding a subset of that features that really matter when diagnosing PD. Lones et al [15] employed evolutionary algorithms for combining classifiers aiming at the automatic identification of Parkinson's Disease.…”

Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting

“…Kotsavasilogloua et al [21] achieved an average prediction accuracy of 91% using simple horizontal lines and features describing the variability in the pen tip's velocity, a deviation from the horizontal plane, and the trajectory's entropy. Other works report even higher classification accuracies (approximately 97%), e.g., Loconsole et al [18], who used computer vision and electromyography signal processing techniques, or Taleb et al [22], who used a combination of features related to the correlation between kinematic and pressure characteristics (but, in this case, applied to a very small dataset). Another promising approach was published by Moetesum et al [23], who reached an 83% classification accuracy by employing convolutional neural networks (CNN) that were used to extract discriminating visual features from handwriting data transformed into the offline mode.…”