Channel-Imposed Fusion: A Simple yet Effective Method for Medical Time Series Classification

TL;DR

This paper introduces Channel Imposed Fusion (CIF), a transparent and effective method for medical time series classification that improves performance and interpretability by fusing cross-channel information and integrating with TCN.

Contribution

The study presents CIF, a novel cross-channel fusion technique, combined with TCN, to enhance medical time series classification with increased transparency and accuracy.

Findings

CIF outperforms state-of-the-art methods on EEG and ECG datasets.

The combined CIF and TCN framework improves classification accuracy.

The method enhances interpretability of the classification process.

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.