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The paper introduces WaveFormer, a transformer architecture tailored for biomedical signal classification, addressing limitations of standard transformers in capturing multi-scale frequency patterns in long sequences. WaveFormer incorporates wavelet decomposition in both the embedding construction via multi-channel DWT and positional encoding via Dynamic Wavelet Positional Encoding (DyWPE). Experiments across eight datasets for human activity recognition and brain signal analysis demonstrate WaveFormer's competitive performance by effectively integrating frequency-domain information.
By integrating wavelet decomposition into transformer embeddings and positional encodings, WaveFormer achieves state-of-the-art performance in biomedical signal classification, demonstrating the power of frequency-domain knowledge in time series analysis.
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.