将手写时序数据转为图像，提升神经退行性疾病检测效果
Cross-Corpus and Cross-domain Handwriting Assessment of NeuroDegenerative Diseases via Time-Series-to-Image Conversion
- 将手写时序数据转化为图像，用预训练ResNet50联合分类
- 在NLS数据集上，画钟和螺旋任务的识别准确率显著提升
- 跨数据集检测表现稳定，帕金森病识别F1最高达98
手写能力受帕金森病（PD）和阿尔茨海默病（AD）等神经退行性疾病显著影响。以往研究多采用基于特征或计算机视觉的方法分析手写任务，但难以在多个数据集间泛化，尤其在时序特征与图像表示之间存在差距。本文提出一种融合时序与图像信息的联合分类框架，基于ImageNet-1k预训练的ResNet50模型。二分类实验在现有时序与图像数据集上均达到领先性能，尤其在神经信号（NLS）数据集的画钟和螺旋任务中表现突出。跨数据集与多数据集实验持续获得高F1分数，帕金森病检测最高达98，表明该模型具备良好泛化能力，可有效识别神经退行性疾病相关的运动功能障碍。
原文摘要 · Abstract (English)
Handwriting is significantly affected by neurological disorders (ND) such as Parkinson's disease (PD) and Alzheimer's disease (AD). Prior works have analyzed handwriting tasks using feature-based approaches or computer-vision techniques, but these methods have struggled to generalize across multiple datasets, particularly between temporal features represented as time-series and images. We propose a framework that leverages both time-series and images of handwriting through a joint classifier, based on a ResNet50 pretrained on ImageNet-1k. Binary classification experiments demonstrate state-of-the-art performances on existing time-series and image datasets, with significant improvement on specific drawing and writing tasks from the NeuroLogical Signals (NLS) dataset. In particular, the proposed model demonstrates improved performance on Draw Clock and Spiral tasks. Additionally, cross-dataset and multi-dataset experiments were consistently able to achieve high F1 scores, up to 98 for PD detection, highlighting the potential of the proposed model to generalize over different forms of handwriting signals, and enhance the detection of motor deficits in ND.
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