基于使用深度学习的单II心电图的心跳分类.
Mohamed F Issa1,2, Ahmed Yousry3, Gergely Tuboly2
1Department of Scientific Computing, Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13511, Egypt.
Heliyon
|August 4, 2023
概括
这项研究引入了带有残余块 (DNN-RB) 的深度神经网络,用于准确的心电图 (ECG) 信号分类. DNN-RB模型实现了高精度,超过了其他用于心血管疾病诊断的方法.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 心脏病学 心脏病学
背景情况:
- 电心电图 (ECG) 信号分析对于诊断心血管疾病至关重要.
- 手动ECG解释是复杂且耗时的.
- 机器学习为自动ECG分类提供了潜力.
研究的目的:
- 开发和验证一个具有残余块 (DNN-RB) 的深度神经网络模型,用于将心脏周期分为六个心电图节拍类别.
- 评估DNN-RB模型的性能与最先进的算法对比.
主要方法:
- 设计了一个包含残余块 (DNN-RB) 的深度神经网络模型.
- 该模型使用MIT-BIH数据集进行了训练和验证.
- 性能指标包括测试准确度,平均灵敏度和平均特异性.
主要成果:
- DNN-RB模型的测试准确率达到了99.51%.
- 平均灵敏度为99.7%,平均特异性为98.2%.
- 拟议的方法在同一数据集上的其他最先进的算法相比显示出更高的性能.
结论:
- DNN-RB模型对于自动ECG信号分类是有效的.
- 该方法在使用移动心电图设备的临床和外科医院监测方面表现有前途.
- 一个集成DNN-RB模型的Web应用程序方便了ECG分析和诊断.
相关概念视频
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