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Manal Alghieth¹

¹Department of Information Technology, College of Computer, Qassim University, Buraydah, 51452, Saudi Arabia. mgietha@qu.edu.sa.

Scientific reports

|July 2, 2025

概括

DeepECG-Net是一种新的深度学习模型,通过有效捕获远程依赖,增强实时心电图 (ECG) 异常检测. 这种基于变压器的方法为心脏监测应用提供了卓越的准确性和低延迟.

科学领域:

人工智能的人工智能
生物医学工程生物医学工程
心脏病学心脏病学

背景情况:

实时电心电图 (ECG) 异常检测对于诊断心脏疾病和实现及时干预至关重要.
目前的CNN和LSTM等模型面临着长距离依赖性,概括性和实时推理延迟等挑战.
这些局限性阻碍了人工智能在持续心脏监测中的有效应用.

研究的目的:

引入DeepECG-Net,这是一个混合深度学习模型,旨在准确有效地实时检测心电图异常.
克服现有模型在捕捉远程依赖性和最小化计算成本方面的局限性.
为了使临床和可穿戴应用可进行可靠和可解释的心电图分析.

主要方法:

开发了DeepECG-Net,这是一个融合卷积神经网络 (CNN) 和变压器架构的混合模型.
在变压器内使用了多头自我注意机制,以有效地学习本地和全球ECG信号变化.
实施了层次化的嵌入策略,以增强特征表示和依赖性捕获.

主要成果:

深度ECG-Net的准确率达到了98.2%,明显超过了ECGNet (88%) 和LSTM模型 (90%).
在信号重建方面取得了实质性的改进,信号噪声比 (SNR) 从5.2dB增加到14.5dB,平均平方误差 (MSE) 从0.042减少到0.007.
实现了高性能指标:98.2%的精度,96.8%的回忆率和97.5%的F1分数.
该模型在Raspberry Pi 4B上部署时显示了低内存使用量 (30 MB) 和50毫秒以下的延迟,非常适合边缘设备.

结论:

DeepECG-Net为实时ECG异常检测提供了强大而高效的解决方案,解决了先前方法的关键局限性.
该模型的低资源要求和高精度使其适合在可穿戴设备和临床边缘设备上部署.
联合学习的整合进一步增强了保护隐私的分布式心电图分析能力.

关键词:

在DeepECG-Net的基础上.有效的深度学习框架.全球依赖学习全球依赖学习混合变压器-CNN模型实时ECG异常检测检测.

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