利用心电图信号为深度学习驱动的心血管疾病分类模型
Hamed Alqahtani1, Ghadah Aldehim2, Nuha Alruwais3
1Department of Information Systems, College of Computer Science, Center of Artificial Intelligence, Unit of Cybersecurity, King Khalid University, Abha, Saudi Arabia.
Heliyon
|September 3, 2024
概括
本研究介绍了一种自动化的深度学习技术,用于ECG信号识别以检测心血管疾病. 该ADL-ECGSR方法实现了91.24%的准确性,改进了现有的心律失常检测方法.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 心脏病学 心脏病学
背景情况:
- 电心电图 (ECG) 是诊断心血管疾病 (CVD) 的关键非侵入性工具.
- 准确和快速检测心律失常,如心房动和心室低心率对于患者的结果至关重要.
- 电脑心电图分析的挑战包括高波形变化和噪声,影响自动识别模型的性能.
研究的目的:
- 引入自动化深度学习启用心电图信号识别 (ADL-ECGSR) 技术用于心血管疾病检测和分类.
- 在临床决策系统中提高自动化心电图分析的准确性和效率.
- 为了应对电心电图信号处理中波形变化和噪声的挑战.
主要方法:
- ADL-ECGSR技术整合了预处理,特征提取,参数调整和分类.
- 一个双向长短期内存 (BiLSTM) 网络作为特征提取器,使用Adamax优化器进行优化.
- 龙算法 (DFA) 与堆叠稀疏自编码器 (SSAE) 模块相结合,用于信号识别和分类.
主要成果:
- 在PTB-XL基准数据集上,ADL-ECGSR技术表现出了91.24%准确度的显著性能.
- 对比分析证实了拟议方法的ECG识别效率比现有方法更高.
- 该研究验证了DL模型在改善ECG信号的心血管疾病检测方面的有效性.
结论:
- 开发的ADL-ECGSR技术为自动ECG信号识别和CVD分类提供了强大而准确的解决方案.
- 整合BiLSTM,Adamax,DFA和SSAE显示出在推进自动心脏诊断方面具有重大潜力.
- 这种深度学习方法有望提高心脏病学医疗保健决策系统的可靠性.
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An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
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