用步态信号评估帕金森病严重程度，准确率达96.6%。
InceptoFormer: A Multi-Signal Neural Framework for Parkinson's Disease Severity Evaluation from Gait
- 融合1D卷积与Transformer，捕捉步态多尺度时序特征。
- 通过过采样缓解病情分级数据不平衡问题，提升模型表现。
- 适合医学智能诊断、神经退行性疾病评估方向的研究者。
我们提出InceptoFormer，一种基于步态动态分析的帕金森病（PD）严重程度评估多信号神经框架。该架构引入1D版的Inception模型（Inception1D），结合基于Transformer的框架，按Hoehn和Yahr（H&Y）量表对PD严重程度进行分阶。Inception1D通过并行使用不同核大小的1D卷积滤波器，捕获多尺度时序特征；Transformer组件则有效建模步态序列中的长程依赖关系，全面理解局部与全局模式。针对PD严重程度分级中的类别不平衡问题，我们设计了一种基于过采样的数据结构化预处理策略，增强低频严重等级的表示能力。整体设计能有效捕捉步态信号中的细粒度时序变化与全局动态，显著提升分类性能。大量实验表明，InceptoFormer在PD严重程度评估中达到96.6%的准确率，优于现有最先进方法。代码已开源：https://github.com/SafwenNaimi/InceptoFormer。
原文摘要 · Abstract (English)
We present InceptoFormer, a multi-signal neural framework designed for Parkinson's Disease (PD) severity evaluation via gait dynamics analysis. Our architecture introduces a 1D adaptation of the Inception model, which we refer to as Inception1D, along with a Transformer-based framework to stage PD severity according to the Hoehn and Yahr (H&Y) scale. The Inception1D component captures multi-scale temporal features by employing parallel 1D convolutional filters with varying kernel sizes, thereby extracting features across multiple temporal scales. The transformer component efficiently models long-range dependencies within gait sequences, providing a comprehensive understanding of both local and global patterns. To address the issue of class imbalance in PD severity staging, we propose a data structuring and preprocessing strategy based on oversampling to enhance the representation of underrepresented severity levels. The overall design enables to capture fine-grained temporal variations and global dynamics in gait signal, significantly improving classification performance for PD severity evaluation. Through extensive experimentation, InceptoFormer achieves an accuracy of 96.6%, outperforming existing state-of-the-art methods in PD severity assessment. The source code for our implementation is publicly available at https://github.com/SafwenNaimi/InceptoFormer
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