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Updated: Feb 14, 2026

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Ambulatory ECG Recording in Mice
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一个轻量级的残留扩张时变压器块用于边缘设备上的ECG分类
G Antoni Gracy1, Sheena Christabel Pravin2
1School of Electronics Engineering, Vellore Institute of Technology, Chennai, 600 127, India.
Scientific reports
|February 12, 2026
概括
一个新的深度学习模型在可穿戴设备上有效地分类心律失常 (ARR),充血性心力衰竭 (CHF) 和正常鼻节律 (NSR). 这种保护隐私的解决方案提供了高准确度,最小的计算资源,用于实时健康监测.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 可穿戴技术可穿戴技术
背景情况:
- 可穿戴设备使心血管疾病管理的持续生理监测成为可能.
- 挑战包括能源效率,处理延迟和对设备内深度学习的数据隐私.
- 现有的基于云的解决方案面临着高通讯开销,而在设备上的方法则面临着计算和热制约的困难.
研究的目的:
- 开发一个高效,资源意识的深度学习模型,用于边缘设备上的ECG分类.
- 为满足对实时心脏监测的轻量级,准确和隐私意识的解决方案的需求.
- 为低功耗,延迟受限制的可穿戴平台优化深度学习.
主要方法:
- 提出了一个深度学习模型,结合了剩余扩展卷和变压器架构.
- 整合了位置编码,通道注意力 (SE块) 和类平衡数据增强 (SMOTE,动噪声).
- 优化了模型,以便在具有严格资源限制的边缘设备上部署.
主要成果:
- 在心律失常,充血性心力衰竭和正常的鼻腔节律方面取得了99.34%的分类准确性.
- 证明了高性能指标:宏观AUROC (0.9996),科恩的卡帕 (0.9891),日志损失 (0.0495),哈明损失 (0.0062) 和MCC (0.9891).
- 该模型仅具有692K参数 (2.64 MB) 和0.234 GFLOPs,表明其高效率.
结论:
- 拟议的模型在ECG分析的准确性,复杂性和推断速度之间提供了一个特殊的权衡.
- 它非常适合用于可穿戴边缘设备和无线身体传感器网络的实时,保护隐私的心脏监测.
- 这项研究提升了边缘计算的持续,低功耗心血管健康监测能力.
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