Background and objectives: Dynamic handwriting analysis, due to its non-invasive and readily accessible nature, has recently emerged as a vital adjunctive method for the early diagnosis of Parkinson's disease. In this study, we design a compact and efficient network architecture to analyse the distinctive handwriting patterns of patients' dynamic handwriting signals, thereby providing an objective identification for the Parkinson's disease diagnosis. Methods: The proposed network is based on a hybrid deep learning approach that fully leverages the advantages of both long short-term memory (LSTM) and convolutional neural networks (CNNs). Specifically, the LSTM block is adopted to extract the time-varying features, while the CNN-based block is implemented using one-dimensional convolution for low computational cost. Moreover, the hybrid model architecture is continuously refined under ablation studies for superior performance. Finally, we evaluate the proposed method with its generalization under a five-fold cross-validation, which validates its efficiency and robustness. Results: The proposed network demonstrates its versatility by achieving impressive classification accuracies on both our new DraWritePD dataset ($96.2\%$) and the well-established PaHaW dataset ($90.7\%$). Moreover, the network architecture also stands out for its excellent lightweight design, occupying a mere $0.084$M of parameters, with a total of only $0.59$M floating-point operations. It also exhibits near real-time CPU inference performance, with inference times ranging from $0.106$ to $0.220$s. Conclusions: We present a series of experiments with extensive analysis, which systematically demonstrate the effectiveness and efficiency of the proposed hybrid neural network in extracting distinctive handwriting patterns for precise diagnosis of Parkinson's disease.

翻译：背景与目标：动态手写分析因其非侵入性和易于获取的特性，近年来已成为帕金森病早期诊断的重要辅助手段。本研究设计了一种紧凑高效的网络架构，用于分析患者动态手写信号中的独特书写模式，从而为帕金森病诊断提供客观识别依据。方法：所提出的网络基于混合深度学习方法，充分结合了长短期记忆网络（LSTM）与卷积神经网络（CNN）的优势。具体而言，采用LSTM模块提取时变特征，而基于CNN的模块则通过一维卷积实现以降低计算成本。此外，通过消融研究持续优化混合模型架构以提升性能。最后，采用五折交叉验证评估所提方法的泛化能力，验证其高效性与鲁棒性。结果：该网络在新构建的DraWritePD数据集（准确率96.2%）与成熟PaHaW数据集（准确率90.7%）上均展现出卓越的分类性能。其轻量化设计尤为突出：参数量仅0.084M，总浮点运算量仅0.59M。同时，该网络在CPU上实现近实时推理，推理时间范围为0.106至0.220秒。结论：通过一系列实验与深入分析，本研究系统论证了所提混合神经网络在提取帕金森病精准诊断所需独特手写模式方面的有效性与高效性。