用小波分解增强Transformer，让生物信号分类更精准
WaveFormer: Wavelet Embedding Transformer for Biomedical Signals
- 小波变换嵌入时频特征，构建含时频信息的令牌
- 动态小波位置编码适配信号特性的时序结构
- 在8个数据集上表现优异，适合长序列生物信号
生物信号分类因序列长、时序动态复杂及多尺度频率模式，难以被标准Transformer有效捕捉。我们提出WaveFormer，通过在两个关键阶段融合小波分解：嵌入构建阶段采用多通道离散小波变换（DWT）提取频率特征，生成同时包含时域与频域信息的令牌；位置编码阶段采用动态小波位置编码（DyWPE），通过单通道DWT分析自适应调整位置嵌入以匹配信号特有的时序结构。我们在涵盖人体活动识别与脑信号分析的8个多样化数据集上评估该模型，序列长度为50至3000个时间步，通道数为1至144。实验结果表明，通过全面的频率感知处理，WaveFormer实现了具有竞争力的性能。该方法为将频域知识系统性融入基于Transformer的时间序列分类提供了原理性框架。
原文摘要 · Abstract (English)
Biomedical signal classification presents unique challenges due to long sequences, complex temporal dynamics, and multi-scale frequency patterns that are poorly captured by standard transformer architectures. We propose WaveFormer, a transformer architecture that integrates wavelet decomposition at two critical stages: embedding construction, where multi-channel Discrete Wavelet Transform (DWT) extracts frequency features to create tokens containing both time-domain and frequency-domain information, and positional encoding, where Dynamic Wavelet Positional Encoding (DyWPE) adapts position embeddings to signal-specific temporal structure through mono-channel DWT analysis. We evaluate WaveFormer on eight diverse datasets spanning human activity recognition and brain signal analysis, with sequence lengths ranging from 50 to 3000 timesteps and channel counts from 1 to 144. Experimental results demonstrate that WaveFormer achieves competitive performance through comprehensive frequency-aware processing. Our approach provides a principled framework for incorporating frequency-domain knowledge into transformer-based time series classification.
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