Dynamic and Static Handwriting Assessment in Parkinson's Disease: A Synergistic Approach with C-Bi-GRU and VGG19

Abstract

Parkinson's disease (PD) is a neurodegenerative disorder causing a decline in dopamine levels, impacting the peripheral nervous system and motor functions. Current detection methods often identify PD at advanced stages. This study addresses early-stage detection using handwriting analysis, specifically exploring the PaHaW dataset for pen pressure and stroke movement data. Evaluating online and offline features, the research employs pre-trained CNN models (VGG 19 and AlexNet) for offline datasets, achieving an overall accuracy of 0.53. For online datasets, velocity, and acceleration features are extracted and classified using Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and recurrent neural networks (RNN), with GRU yielding the highest accuracy at 0.57. Notably, the convolution-based model C-Bi-GRU surpasses other architectures with a remarkable 0.75 accuracy, emphasizing its efficacy in early PD detection. These findings underscore the potential of handwriting analysis as a diagnostic tool for PD, contributing valuable insights for further research and development in medical diagnostics.

Multi-label Classification of Indonesian Al-Quran Translation based CNN, BiLSTM, and FastText

“…Convolutional Neural Networks (CNNs) are artificial neural networks that utilize feedforward connections to establish a hierarchical structure in data [34]. Through the learning process, CNN can capture the internal feature representation of data and generalize from these features to new data, especially in the context of object recognition and computer vision problems in images [35]. While commonly used for images processing related applications, CNNs have also been broadly used in solving natural language processing and speech recognition problems [36].…”

SS-DRPL: self-supervised deep representation pattern learning for voice-based Parkinson's disease detection

“…Recent developments in the field of deep learning have demonstrated potential for enhancing the precision and effectiveness of PD identification (Ali et al, 2023 ). The use of Self Supervised Representation learning (SSRL) has become increasingly prominent as an effective methodology for acquiring representations from data that lacks labels (Ericsson et al, 2021 ).…”