对CNN和变压器架构进行比较分析,并将心脏周期规范化用于自动化声心图分类
概括
专业的CNN在使用心电图 (PCG) 数据检测心脏声方面优于变压器模型. 规范化策略显著影响性能,指导临床AI开发.
科学领域:
- 心血管诊断心血管诊断服务
- 人工智能在医学中的应用
- 生物医学信号处理
背景情况:
- 声心电图 (PCG) 记录的自动分类对于推进心血管诊断至关重要.
- 现有的模型经常与生理信号的变化作斗争,影响诊断的准确性.
研究的目的:
- 系统地比较专用卷积神经网络 (CNN) 和零射击通用音频变压器 (BEAT) 的心脏声检测性能.
- 为了评估固定长度窗口与定制心脏周期正常化对模型性能的影响.
- 为在临床环境中选择AI架构提供基于证据的指导方针.
主要方法:
- 利用PhysioNet2022数据集进行心脏声检测.
- 实现并比较了两个专门的CNN模型和两个零射击BEATs变压器模型.
- 评估了使用固定长度窗口和针对个体心律量身定制的新型定制心脏周期正常化方法的模型.
主要成果:
- 与BEATs变压器相比,CNN模型实现了较高的接收器运行特征曲线下面面积 (AUROC) 值.
- 有固定长度窗口的CNN:79.5%AUROC;有心脏周期正常化的CNN:75.4%AUROC.
- 带有固定长度窗口的BEATs变压器:65.7%AUROC;带有心脏周期正常化的BEATs变压器:70.1%AUROC.
结论:
- 生理信号正常化策略显著影响自动诊断模型的性能.
- 专门的CNN为心脏声检测提供了卓越的准确性,而变压器模型则具有潜在的开发效率优势.
- 研究结果支持使用自动分类系统来加强心脏诊断和患者护理,强调准确性和效率之间的平衡.
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