通过ECHOView图像检测霍尔特心电图记录中的心房动:深度转移学习研究
Vessela Krasteva1, Todor Stoyanov1, Stefan Naydenov2
1Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl. 105, 1113 Sofia, Bulgaria.
Diagnostics (Basel, Switzerland)
|April 12, 2025
概括
使用ImageNet深度神经网络 (DNN) 的深度转移学习显著改善了从短期ECHOView图像中检测心房动 (AF). 这种方法增强了自动诊断,并为心脏病学家提供了可解释的结果.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 及时检测心房动 (AF) 对患者的治疗结果至关重要.
- 在霍尔特心电图记录中检测短暂的AF存在重大挑战.
- 短暂或不频繁的症状发作使诊断复杂化.
研究的目的:
- 用ImageNet深度神经网络 (DNN) 探索深度转移学习.
- 为了提高对AF检测的短期ECHOView图像的解释.
- 评估重新培训和微调DNN用于AF诊断的有效性.
主要方法:
- 对于18个预训练的ImageNet DNN,ECHOView图像 (堆叠的心跳幅度) 的尺寸被重新调整.
- 转移学习涉及到顶层的再培训或微调整个网络.
- 模型在IRIDIA-AF的霍尔特心电图数据库上进行了训练和验证.
主要成果:
- 经过重新调整的EfficientNetV2B1实现了96.3%的准确率;微调模型达到97.6%.
- 模型在较短的心电图情节 (20秒,10秒) 中保持了可容忍的准确性.
- GradCAM热图提供了模型的解释性,突出了相关的ECG区域.
结论:
- 使用ImageNet DNN进行深度转移学习显著提高了自动化AF检测.
- 重新训练和微调DNN可以提高使用ECHOView图像的诊断准确性.
- GradCAM提供了有意义的解释性,将模型重点与临床专业知识对齐.
关键词:
处理ECG信号的过程.霍尔特电图监测监控 霍尔特电图监控图像网 DNN 在线节律失常 (arrhythmia) 是一种心律失常.深度神经网络是一个神经网络.电心电矩阵 (电心电矩阵) 是一个心电矩阵.精细调整 精细调整图像处理是图像处理的过程.重新培训,重新培训的人.更多相关视频
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