有效的边缘人工智能模型用于在资源有限的硬件上强大的心电图异常检测
Zhaojing Huang1, Luis Fernando Herbozo Contreras2, Wing Hang Leung2
1School of Biomedical Engineering, The University of Sydney, NSW 2008, Sydney, Australia. zhaojing.huang@sydney.edu.au.
Journal of cardiovascular translational research
|March 13, 2024
概括
两个新的AI模型,CLTC和CCfC,有效地识别心电图 (ECG) 数据中的异常. 在微控制器上部署,这些模型为边缘医疗保健应用提供高效,可通用的解决方案.
科学领域:
- 医疗保健中的人工智能
- 生物医学信号处理
- 对于医疗器械的机器学习
背景情况:
- 电心电图 (ECG) 分析对于诊断心脏疾病至关重要.
- 开发高效的人工智能模型来实时检测心电图异常对于远程患者监测和边缘计算至关重要.
- 现有的模型经常面临资源限制和跨不同数据集的概括性挑战.
研究的目的:
- 引入和评估两种新型深度学习模型,即ConvLSTM2D-液态时间恒定网络 (CLTC) 和ConvLSTM2D-封闭形式的连续时间神经网络 (CCfC),用于ECG异常的识别.
- 评估这些模型的性能,通用性和弹性.
- 为了证明在边缘应用程序的资源有限的微控制器上部署这些模型的可行性.
主要方法:
- 开发了两个不同的深度学习架构:CLTC和CCfC,两者都基于ConvLSTM2D.
- 使用Minas Gerais (TNMG) 远程医疗网络子集数据集的模型的培训和评估.
- 使用2018年中国生理信号挑战 (CPSC) 数据集对概括性的验证.
- 使用F1分数,AUROC值和精度评估模型性能.
- 对微控制器部署的资源利用 (内存和闪存) 的评估.
主要成果:
- 两种CLTC和CCfC模型在识别心电图异常方面表现相似,达到相似的F1分数和AUROC值.
- CCfC模型的整体准确性略高.
- 在处理数据集时,CLTC模型在使用空心电图通道时表现出卓越的性能.
- 在资源有限的微控制器上成功部署,证实了边缘计算的可行性.
- 模型在独立数据集 (CPSC) 上测试时显示出强大的概括能力.
- 证实了高效的资源利用,模型占用了70.6%的内存和9.4%的闪存.
结论:
- 开发的CLTC和CCfC模型代表了人工智能驱动的ECG异常识别的重大进展.
- 这些模型适合在边缘设备上部署,因为它们高效的资源利用和已被证明的通用性.
- 该研究支持将先进的人工智能集成到现实世界医疗保健应用中,特别是用于远程监控和诊断.
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