A Fine Evaluation Method for Cube Copying Test for Early Detection of Alzheimer's Disease
Xinyu Jiang, Cuiyun Gao, Wenda Huang, Yiyang Jiang, Binwen Luo, Yuxin Jiang, Mengting Wang, Haoran Wen, Yang Zhao, Xuemei Chen, and Songqun Huang

TL;DR
This paper introduces a novel, detailed evaluation method for the Cube Copying Test using dynamic handwriting features and deep learning, improving early detection of Alzheimer's disease with high accuracy.
Contribution
It proposes a new fine-grained assessment approach based on dynamic handwriting analysis and BiLSTM-Attention model for early Alzheimer's detection.
Findings
Achieved 86.69% classification accuracy.
Identified significant correlations between scores, age, and education.
Demonstrated superiority over similar methods.
Abstract
Background: Impairment of visual spatial cognitive function is the most common early clinical manifestation of Alzheimer's Disease (AD). When the Montreal Cognitive Assessment (MoCA) uses the "0/1" binary method ("pass/fail") to evaluate the visual spatial cognitive ability represented by the Cube Copying Test(CCT), the elder with less formal education generally score 0 point, resulting in serious bias in the evaluation results. Therefore, this study proposes a fine evaluation method for CCT based on dynamic handwriting feature extraction of DH-SCSM-BLA. method : The Cogni-CareV3.0 software independently developed by our team was used to collect dynamic handwriting data of CCT. Then, the spatial and motion features of segmented dynamic handwriting were extracted, and feature matrix with unequal dimensions were normalized. Finally, a bidirectional long short-term memory network model…
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Taxonomy
TopicsDementia and Cognitive Impairment Research · Spatial Neglect and Hemispheric Dysfunction · Spatial Cognition and Navigation
