用先进的深度学习技术对数字化心电图数据集进行心律失常的分类
Shoaib Sattar1, Rafia Mumtaz1, Mamoon Qadir2
1School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
这项研究将心电图像数字化为时间序列信号,用于深度学习分析. 一个卷积神经网络 (CNN) 在分类心律失常方面达到~92%的准确性,从而实现实时监测.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 生物医学信号处理
背景情况:
- 电心电图 (ECG) 的分类对于诊断心脏病至关重要.
- 深度学习 (DL) 提供了分析ECG信号的先进工具,以帮助专家诊断.
- 将心电图记录数字化成时间序列数据使复杂的计算分析成为可能.
研究的目的:
- 将一组ECG记录图像的数据集数字化成时间序列信号.
- 应用和比较最先进的深度学习技术用于ECG信号分类.
- 评估卷积神经网络 (CNN),长期短期记忆 (LSTM) 和自我监督学习 (SSL) 模型对心律不整的分类的性能.
主要方法:
- 来自巴基斯坦医疗机构的心电图像被数字化.
- II心跳被从数字化的心电图信号细分出来.
- 训练和比较多个DL模型,包括CNN,LSTM和基于SSL的自动编码模型.
主要成果:
- 拟议的CNN模型实现了最高的分类准确率,约为92%.
- DL模型是根据来自不同患者心电图的数据集进行训练的.
- 美国有线电视新闻网 (CNN) 模型展示了实时ECG监测的快速推断能力.
结论:
- 通过DL模型处理的数字化心电图信号为基于图像的分析提供了可行的替代方案,用于心律失常的分类.
- 开发的CNN模型为ECG信号提供了准确和高效的实时监控.
- 这种方法可直接使用带有心电图机器输出的DL模型来加强心脏病患者监测.
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