Channel-Imposed Fusion: A Simple yet Effective Method for Medical Time Series Classification
Journal:
arXiv
Published Date:
May 31, 2025
Abstract
The automatic classification of medical time series signals, such as
electroencephalogram (EEG) and electrocardiogram (ECG), plays a pivotal role in
clinical decision support and early detection of diseases. Although Transformer
based models have achieved notable performance by implicitly modeling temporal
dependencies through self-attention mechanisms, their inherently complex
architectures and opaque reasoning processes undermine their trustworthiness in
high stakes clinical settings. In response to these limitations, this study
shifts focus toward a modeling paradigm that emphasizes structural
transparency, aligning more closely with the intrinsic characteristics of
medical data. We propose a novel method, Channel Imposed Fusion (CIF), which
enhances the signal-to-noise ratio through cross-channel information fusion,
effectively reduces redundancy, and improves classification performance.
Furthermore, we integrate CIF with the Temporal Convolutional Network (TCN),
known for its structural simplicity and controllable receptive field, to
construct an efficient and explicit classification framework. Experimental
results on multiple publicly available EEG and ECG datasets demonstrate that
the proposed method not only outperforms existing state-of-the-art (SOTA)
approaches in terms of various classification metrics, but also significantly
enhances the transparency of the classification process, offering a novel
perspective for medical time series classification.
