TY - JOUR A1 - Pereira, Clayton R. A1 - Pereira, Danilo R. A1 - Rosa, Gustavo H. A1 - Albuquerque, Victor Hugo C. A1 - Weber, Silke A. T. A1 - Hook, Christian A1 - Papa, João Paulo T1 - Handwritten dynamics assessment through convolutional neural networks BT - An application to Parkinson’s disease identification JF - Artificial Intelligence in Medicine N2 - Background and objective Parkinson’s disease (PD) is considered a degenerative disorder that affects the motor system, which may cause tremors, micrography, and the freezing of gait. Although PD is related to the lack of dopamine, the triggering process of its development is not fully understood yet. Methods In this work, we introduce convolutional neural networks to learn features from images produced by handwritten dynamics, which capture different information during the individual’s assessment. Additionally, we make available a dataset composed of images and signal-based data to foster the research related to computer-aided PD diagnosis. Results The proposed approach was compared against raw data and texture-based descriptors, showing suitable results, mainly in the context of early stage detection, with results nearly to 95%. Conclusions The analysis of handwritten dynamics using deep learning techniques showed to be useful for automatic Parkinson’s disease identification, as well as it can outperform handcrafted features. KW - Convolutional neural networks KW - Handwritten dynamics KW - Parkinson’s disease Y1 - 2018 U6 - https://doi.org/10.1016/j.artmed.2018.04.001 VL - 37 IS - May SP - 67 EP - 77 PB - Elsevier ER - TY - JOUR A1 - Afonso, Luis Claudio Sugi A1 - Rosa, Gustavo H. A1 - Pereira, Clayton R. A1 - Weber, Silke A. T. A1 - Hook, Christian A1 - Albuquerque, Victor Hugo C. A1 - Papa, Joao Paulo T1 - A recurrence plot-based approach for Parkinson's disease identification JF - Future generation computer systems - The international journal of escience N2 - Parkinson's disease (PD) is a neurodegenerative disease that affects millions of people worldwide, causing mental and mainly motor dysfunctions. The negative impact on the patient's daily routine has moved the science in search of new techniques that can reduce its negative effects and also identify the disease in individuals. One of the main motor characteristics of PD is the hand tremor faced by patients, which turns out to be a crucial information to be used towards a computer-aided diagnosis. In this context, we make use of handwriting dynamics data acquired from individuals when submitted to some tasks that measure abilities related to writing skills. This work proposes the application of recurrence plots to map the signals onto the image domain, which are further used to feed a Convolutional Neural Network for learning proper information that can help the automatic identification of PD. The proposed approach was assessed in a public dataset under several scenarios that comprise different combinations of deep-based architectures, image resolutions, and training set sizes. Experimental results showed significant accuracy improvement compared to our previous work with an average accuracy of over 87%. Moreover, it was observed an improvement in accuracy concerning the classification of patients (i.e., mean recognition rates above to 90%). The promising results showed the potential of the proposed approach towards the automatic identification of Parkinson's disease. KW - Classification KW - Convolutional neural networks KW - diagnosis KW - Optimum-path forest KW - Parkinson's disease KW - Recurrence plot Y1 - 2019 U6 - https://doi.org/10.1016/j.future.2018.11.054 VL - 94 IS - May SP - 282 EP - 292 PB - Elsevier ER -