用卷积式编码器-解码器用剩余和反复连接划分12导电图代表节拍
Vessela Krasteva1, Todor Stoyanov1, Ramun Schmid2
1Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl. 105, 1113 Sofia, Bulgaria.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
这项研究比较了四个深度神经网络架构用于12导电图划分,发现CED-Net强大而准确. 这些模型准确地复制了参考测量结果,并且符合标准公差,即使在噪声方面也是如此.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 准确的心电图 (ECG) 划分对于诊断心脏病的诊断至关重要.
- 深度神经网络 (DNN) 提供了自动ECG分析的潜力.
- 在开发基于DNN的强大而精确的ECG划分算法方面仍然存在挑战.
研究的目的:
- 为了比较四个不同的编码器-解码器DNN架构的性能,用于12-leadECG划分.
- 评估这些模型的准确性和稳定性与参考算法和标准公差相比.
- 评估噪声对不同架构划分性能的影响.
主要方法:
- 实施了四个编码器-解码器DNN:CED-Net,CED-LSTM-Net,CED-U-Net和CED-Res-Net. 这四个DNN包括:CED-Net,CED-LSTM-Net,CED-U-Net和CED-Res-Net.
- 模型在PhysioNet ECG数据库 (PTB-XL) 上使用基于机器的监督学习与参考算法 (ETM) 进行训练.
- 使用CSE数据库验证性能,并进行噪声测试 (高频,低频,电线噪声).
主要成果:
- 所有DNN架构在复制参考测量时都表现出可比的精度,平均P波检测精度为96.6%.
- 该CED-Net模型满足了P-持续时间,PQ-间隔,QRS-持续时间和QT-间隔测量的所有标准公差.
- CED-Net,CED-Res-Net和CED-LSTM-Net显示出对各种噪音类型的稳定性,保持低的平均持续时间错误.
结论:
- 开发的DNN架构,特别是CED-Net,具有很高的准确性和可靠性的12导电图划分能力.
- 与CED-U-Net架构相比,CED-Net在噪声方面表现出更高的稳定性.
- 该研究强调了这些DNN在自动ECG分析中的竞争性表现,实现了与已发表研究相比较的低时间误差.
相关概念视频
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Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
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Parts of an ECG
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An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...
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