Peter Drotár1, Jiří Mekyska1, Irena Rektorová2, Lucia Masarová3, Zdeněk Smékal1, Marcos Faundez-Zanuy4. 1. Department of Telecommunications, Brno University of Technology, Technická 12, 61200 Brno, Czech Republic. 2. First Department of Neurology, Faculty of Medicine, St. Anns University Hospital, Pekarska 664, 66591 Brno, Czech Republic. Electronic address: rektorova@fnusa.cz. 3. First Department of Neurology, Faculty of Medicine, St. Anns University Hospital, Pekarska 664, 66591 Brno, Czech Republic. 4. Signal Processing Group, Tecnocampus, Escola Universitaria Politecnica de Mataro, Avda. Ernest Llunch 32, 08302 Mataro, Spain.
Abstract
OBJECTIVE: We present the PaHaW Parkinson's disease handwriting database, consisting of handwriting samples from Parkinson's disease (PD) patients and healthy controls. Our goal is to show that kinematic features and pressure features in handwriting can be used for the differential diagnosis of PD. METHODS AND MATERIAL: The database contains records from 37 PD patients and 38 healthy controls performing eight different handwriting tasks. The tasks include drawing an Archimedean spiral, repetitively writing orthographically simple syllables and words, and writing of a sentence. In addition to the conventional kinematic features related to the dynamics of handwriting, we investigated new pressure features based on the pressure exerted on the writing surface. To discriminate between PD patients and healthy subjects, three different classifiers were compared: K-nearest neighbors (K-NN), ensemble AdaBoost classifier, and support vector machines (SVM). RESULTS: For predicting PD based on kinematic and pressure features of handwriting, the best performing model was SVM with classification accuracy of Pacc=81.3% (sensitivity Psen=87.4% and specificity of Pspe=80.9%). When evaluated separately, pressure features proved to be relevant for PD diagnosis, yielding Pacc=82.5% compared to Pacc=75.4% using kinematic features. CONCLUSION: Experimental results showed that an analysis of kinematic and pressure features during handwriting can help assess subtle characteristics of handwriting and discriminate between PD patients and healthy controls.
OBJECTIVE: We present the PaHaW Parkinson's disease handwriting database, consisting of handwriting samples from Parkinson's disease (PD) patients and healthy controls. Our goal is to show that kinematic features and pressure features in handwriting can be used for the differential diagnosis of PD. METHODS AND MATERIAL: The database contains records from 37 PDpatients and 38 healthy controls performing eight different handwriting tasks. The tasks include drawing an Archimedean spiral, repetitively writing orthographically simple syllables and words, and writing of a sentence. In addition to the conventional kinematic features related to the dynamics of handwriting, we investigated new pressure features based on the pressure exerted on the writing surface. To discriminate between PDpatients and healthy subjects, three different classifiers were compared: K-nearest neighbors (K-NN), ensemble AdaBoost classifier, and support vector machines (SVM). RESULTS: For predicting PD based on kinematic and pressure features of handwriting, the best performing model was SVM with classification accuracy of Pacc=81.3% (sensitivity Psen=87.4% and specificity of Pspe=80.9%). When evaluated separately, pressure features proved to be relevant for PD diagnosis, yielding Pacc=82.5% compared to Pacc=75.4% using kinematic features. CONCLUSION: Experimental results showed that an analysis of kinematic and pressure features during handwriting can help assess subtle characteristics of handwriting and discriminate between PDpatients and healthy controls.
Authors: João W M de Souza; Shara S A Alves; Elizângela de S Rebouças; Jefferson S Almeida; Pedro P Rebouças Filho Journal: Comput Intell Neurosci Date: 2018-04-24