实时实施加速的HCP-MMA用于基于深度学习的ECG心律失常分类,使用基于轮的可视化.
IEEE journal of biomedical and health informatics
|May 22, 2025
概括
本研究介绍了从多尺度多分片分析 (HCP-MMA) 进行加速的Hurst轮投影,用于实时ECG心律失常的分类. 优化的方法实现了高精度,并使其能够在嵌入式系统上部署,用于先进的诊断.
科学领域:
- 生物医学工程 生物医学工程
- 计算科学 计算科学
- 数据科学数据科学数据科学
背景情况:
- 传统的心率可变性分析在捕捉复杂的缩放模式方面存在局限性,导致诊断不一致.
- 多分法分析提供了更全面的方法,但由于计算成本高,阻碍了实时应用.
- 来自多尺度多分体分析 (HCP-MMA) 的快速轮投影提高了可解释性,但需要大量的计算资源.
研究的目的:
- 开发HCP-MMA的实时实现,以改善心电图失常症的分类.
- 克服传统的多元分析的计算局限性,以便在实际诊断中使用.
- 为了使在资源有限的环境中部署先进的多元分析技术.
主要方法:
- 实现了HCP-MMA的运行时优化的并行计算管道,利用单数值分解 (SVD) 和矢量化处理.
- 将加速的HCP-MMA与AlexNet深度学习模型集成在一起,用于ECG心律失常分类.
- 在PhysioNet,MIT-BIH和CU基准数据集上验证了该方法,并在Raspberry Pi 5上测试了实时性能.
主要成果:
- 在英特尔系统上实现了比基线MMA实现的730\times$加快速度.
- 达到超过98%的分类准确度和高达99.3%的ECG心律失常分类F1分数.
- 在嵌入式硬件 (Raspberry Pi 5) 上演示了$\sim 199\times$的加快速度,推断时间低 (0.0668s) 和内存足迹 (220 MB).
结论:
- 加速的HCP-MMA框架为实时ECG心律失常分类提供了计算效率高,准确的解决方案.
- 该方法的稳定性和通用性得到了统计验证 (p < 0.05).
- 这一进步有助于将复杂的多元分析集成到可穿戴设备,远程医疗和其他实时生理监测应用中.
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