产品增强的神经网络,用于在嵌入式设备上有效检测心律失常
Haotian Tang1, Mingke Yan1, Xidong Wu1
1College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, Shenyang, China.
Computer methods and programs in biomedicine
|October 11, 2025
概括
这项研究引入了一种轻量级的深度学习模型,用于嵌入式设备上的高效的实时心律失常检测,实现高精度,以改善心脏监测.
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
- 心血管技术的心血管技术
背景情况:
- 心血管疾病需要准确的心律失常检测,以改善患者的治疗结果.
- 深度学习模型显示出基于心电图的心律失常分类的前景.
- 计算效率对于在资源受限的嵌入式设备上部署模型至关重要.
研究的目的:
- 开发一种轻量级,计算效率高的模型,用于嵌入式平台上的实时心律失常检测.
- 解决在资源有限的环境中部署深度学习用于心脏监测的挑战.
主要方法:
- 开发了一种元素对产品增强的轻量级模型 (EPLM),具有形架构和深度可分离的卷积.
- 一个新的元素产品融合机制增强了高维特征表示.
- 该模型在MIT-BIH,SVDB,INCART和PTB数据库上进行了评估,评估了准确性,精度,回忆,F1得分,计算复杂性,推断时间和功耗.
- 实践验证包括在Raspberry Pi 5和智能手机上部署,以进行实时适用性评估.
主要成果:
- EPLM实现了很高的分类准确率 (MIT-BIH上为99.10%,PTB上为98.85%).
- 该模型需要最小的参数 (25,981) 和FLOP (525,400),具有2.29 ms的快速推断时间.
- 实时运行通过嵌入式系统的部署得到证实,证明适合于可穿戴应用.
结论:
- EPLM框架提供了超高效的心律失常检测,低计算开销,用于持续的心脏监测.
- 这一进步促进了早期临床干预和改善了在资源有限的环境中患者的治疗结果.
- 该模型的效率和准确性有可能改变实时可穿戴医疗保健技术.
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