Abstract
Alzheimer's disease detection requires expensive neuroimaging or invasive procedures, limiting accessibility. This study explores whether deep learning can enable non-invasive Alzheimer's disease detection through handwriting analysis. Using a dataset of 34 distinct handwriting tasks collected from healthy controls and Alzheimer's disease patients, we evaluate and compare three recurrent neural architectures (LSTM, GRU, RNN) against traditional machine learning models. A crucial distinction of our approach is that the recurrent models process pre-extracted features from discrete strokes, not raw temporal signals. This violates the assumption of a continuous temporal flow that recurrent networks are designed to capture. Results reveal that they exhibit poor specificity and high variance. Traditional ensemble methods significantly outperform all deep architectures, achieving higher accuracy with balanced metrics. This demonstrates that recurrent architectures, designed for continuous temporal sequences, fail when applied to feature vectors extracted from ambiguously segmented strokes. Despite their complexity, deep learning models cannot overcome the fundamental disconnect between their architectural assumptions and the discrete, feature-based nature of stroke-level handwriting data. Although performance is limited, the study highlights several critical issues in data representation and model compatibility, pointing to valuable directions for future research.
Abstract (translated)
阿尔茨海默病的检测通常需要昂贵的神经影像技术或侵入性操作，这限制了其在实际中的应用。本研究探索了一种通过分析手写来实现非侵入式阿尔茨海默病诊断的可能性，并使用深度学习技术进行这项任务。我们采用了一个包含34个不同手写任务的数据集，这些数据来自健康对照组和阿尔茨海默病患者。在这项研究中，评估并比较了三种递归神经网络架构（LSTM、GRU、RNN）与传统机器学习模型的性能。 本研究的一个关键特点是，递归模型处理的是从离散笔划中预先提取出来的特征，而不是原始的时间序列信号。这违背了递归网络设计初衷——用于捕捉连续时间流的特点。实验结果显示，这些模型表现出较差的特异性和较高的方差。 相比之下，传统的集成方法在所有深度架构上都表现出了显著的优势，并且能够实现更高的准确度以及平衡的性能指标。这一结果表明，当应用于从模糊划分笔划中提取出来的特征向量时，设计用于连续时间序列的递归架构无法有效工作。尽管这些深度学习模型复杂度很高，但它们未能克服其结构假设与基于离散、特征化的笔画级手写数据之间本质上的不兼容性。 虽然这项研究显示了性能限制，但它揭示了一些在数据表示和模型兼容性方面的重要问题，并为未来的研究指明了有价值的方向。
URL
https://arxiv.org/abs/2508.03773
