可解释的基于注意力的深度学习,用于分类和解释心脏声,使用心电图
Bollapalli Althaph1, Nagendra Panini Challa2
1School of Computer Science and Engineering (SCOPE), VIT-AP University, Amaravati, Vijayawada, 522237, Andhra Pradesh, India.
Scientific reports
|October 31, 2025
概括
这项研究引入了一个可解释的深度学习模型,用于使用心电图 (PCG) 信号准确检测心脏声. 该框架提高了诊断可靠性,并为临床信任提供了视觉解释.
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:
- 心血管疾病 (CVD) 构成了全球卫生挑战,传统的心脏声诊断依赖主观听觉.
- 现有的自动化方法往往缺乏可重现性和临床解释性,阻碍了广泛采用.
- 迫切需要准确,可复制和透明的诊断工具来诊断心脏声.
研究的目的:
- 开发和验证可解释的基于注意力的深度学习框架,用于心脏声的分类和解释.
- 提高诊断准确度和对自动化心电图 (PCG) 分析的临床信心.
- 为模型预测提供视觉解释,突出突出关键的语段.
主要方法:
- 利用变压器架构从PCG信号 (光谱图,MFCC) 中提取时间频率特征.
- 集成梯度加权类激活映射 (Grad-CAM) 用于生成模型预测的视觉解释.
- 在多个大规模数据集 (HeartWave,CirCor DigiScope,PhysioNet,深) 中使用强大的A-Test方法验证了框架.
主要成果:
- 实现了高诊断性能:96.7%的准确性,95.5%的宏F1得分和AUC> 0.97.
- 在精度方面表现比10个基线模型高3-5%,在宏观F1得分方面表现比2-4%.
- 在模型生成的解释和专家注释之间显示出强烈的对齐,证实了可解释性.
结论:
- 拟议的可解释的基于注意力的深度学习框架在自动心脏声诊断方面取得了重大进展.
- 该模型提供了高精度和关键的临床解释性,促进了对人工智能驱动的心脏诊断的更大信任.
- 未来的工作旨在提高可扩展性和整合多式联络数据,以提供全面的临床决策支持.
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