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<p>High-fidelity signal acquisition underpins next-generation healthcare bioelectronics, yet motion artefacts severely impair both signal integrity and measurement reliability. Existing mitigation strategies primarily target a single artefact type or a fixed frequency range, limiting scalability and generality. Here we report a meta-topological hydrogel that combines programmable phononic metastructure filtering with topology-tunable ion diffusion to suppress multisource mechanical and biopotential artefacts across tailored frequency ranges. This artefact-mitigating platform enables simultaneous, artefact-free acquisition of haemodynamic and electrophysiological signals, achieving ISO-grade A blood pressure accuracy and an electrocardiograph signal-to-noise ratio of 37.36 dB during daily activities. The platform supports robust feature extraction from physiological signals for fatigue profiling, achieving a deep learning classification accuracy of 92.04%. We further demonstrate effective artefact suppression across diverse biosignals modalities, including heart and respiratory sounds, voice, electroencephalogram and electrooculogram, highlighting its potential for scalable and kinematic-tolerant monitoring in motion-intensive scenarios.</p>

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Meta-topological hydrogel enables multisource and frequency-tailored artefact mitigation for bioelectronics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月24日

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  • Guo Tian,
  • Longchao Huang,
  • Xinglong Pan,
  • Zhiwei Li,
  • Wanheng Lu,
  • Wei Li Ong,
  • Chang Liu,
  • Yi Zhou,
  • Yue Sun,
  • Weili Deng,
  • Weiqing Yang,
  • Wei Gao,
  • Ghim Wei Ho

摘要

High-fidelity signal acquisition underpins next-generation healthcare bioelectronics, yet motion artefacts severely impair both signal integrity and measurement reliability. Existing mitigation strategies primarily target a single artefact type or a fixed frequency range, limiting scalability and generality. Here we report a meta-topological hydrogel that combines programmable phononic metastructure filtering with topology-tunable ion diffusion to suppress multisource mechanical and biopotential artefacts across tailored frequency ranges. This artefact-mitigating platform enables simultaneous, artefact-free acquisition of haemodynamic and electrophysiological signals, achieving ISO-grade A blood pressure accuracy and an electrocardiograph signal-to-noise ratio of 37.36 dB during daily activities. The platform supports robust feature extraction from physiological signals for fatigue profiling, achieving a deep learning classification accuracy of 92.04%. We further demonstrate effective artefact suppression across diverse biosignals modalities, including heart and respiratory sounds, voice, electroencephalogram and electrooculogram, highlighting its potential for scalable and kinematic-tolerant monitoring in motion-intensive scenarios.