MSCGN: Multiscale complementary gating network for time series classification

doi:10.1016/j.bspc.2025.108563

2025-09-06

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DOI：10.1016/j.bspc.2025.108563

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时间序列数据的分类由于其在不同领域和任务中固有的多样性，仍然是一个严峻的挑战。本研究提出了一种新颖的多尺度互补门控网络（MSCGN），该网络在不同领域中表现出卓越的泛化能力和鲁棒性。该模型采用多尺度残差减法策略，通过整合多粒度信息来提取更全面且鲁棒的特征表示，使网络能够感知粗粒度和细粒度差异信息。该模型在六个时间序列分类任务上展现出优异的准确性和泛化能力，包括心房颤动识别、人体活动识别、睡眠监测、癫痫识别、心音检测和手势识别，其性能超越了不同时间序列领域中的基准模型。所提出的MSCGN为时间序列分类提供了一个强大且灵活的工具，解决了时间序列数据的复杂性和变异性问题。

期刊

Biomedical Signal Processing and Control

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4.9

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1.0W

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2.4W

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