A new online Arabic handwriting dataset for analyzing Parkinson’s disease

Allebawi, Mohammed F.; Dhieb, Thameur; Jarraya, Islem; Neji, Mohamed; Farhat, Nouha; Smaoui, Emna; Moalla, Khadija; Dammak, Mariem; Hamdani, Tarek M.; Mhiri, Chokri; Alimi, Adel M.

doi:10.1109/cw58918.2023.00019

Cited by 2 publications

(3 citation statements)

References 36 publications

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“…To address this gap, we collected a novel online Arabic handwriting dataset, at the Neurology Department, Habib Bourguiba Hospital, Sfax, Tunisia, designed to detect Parkinson's disease. Through this initiative, we provide researchers with access to this dataset for exploration and study [17].…”

Section: Proposed Online Arabic Handwriting Dataset For Parkinson's D...mentioning

confidence: 99%

“…Figure 8 provides a visual representation of an LSTM network, with a single input, a single output, and a single memory block. Input Gate: (15) Forget Gate: (16) Output Gate: (17) Input Modulation: (18) Memory cell Update: (19) Output: where:…”

Section: E Classification Based On Blstmmentioning

confidence: 99%

“…In the offline method, handwritten content on paper is captured using a scanner. In the online method, a specialized device like a graphic tablet is used to track sequential data such as pressure [17], [18]. In this present work, we focus on the online method.…”

Section: Introductionmentioning

confidence: 99%

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Parkinson’s Disease Detection From Online Handwriting Based on Beta-Elliptical Approach and Fuzzy Perceptual Detector

Allebawi,

Dhieb,

Neji

et al. 2024

IEEE Access

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The increasing age of our society is connected to a rising number of people suffering from disorders. One such disorder is Parkinson's disease (PD). Predictions indicate that the number of individuals affected by PD will more, than double in the future. Neurologists and data scientists consider handwriting as one of the motor symptoms of PD and recognize it as a valuable resource for detecting this disorder. Within this framework, we introduce an innovative system for Parkinson's disease detection, which encompasses several key stages. The process commences with data augmentation and preprocessing, subsequently leading to the segmentation of online handwriting into Beta strokes. Following that, feature extraction is carried out utilizing the Beta-elliptical approach and the fuzzy perceptual detector. Finally, we employ bidirectional long short-term memory (BLSTM) for the classification task. To assess the performance of our system, we created a new online Arabic handwriting dataset designed for detecting Parkinson's disease. The results we obtained affirm the efficacy of our proposed system. Through comprehensive evaluations conducted on the PaHaW dataset, we achieved good accuracy, thereby highlighting that our system surpasses the performance of existing systems.

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Section: Proposed Online Arabic Handwriting Dataset For Parkinson's D...mentioning

confidence: 99%