异常识别辅助和发病补偿意识网络用于病理性可穿戴式心电图划分
Yue Zhang1, Jiewei Lai1, Chenyu Zhao1
1School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China.
Artificial intelligence in medicine
|October 6, 2024
概括
这项研究引入了用于心电图 (ECG) 划分的新型网络,改善了可穿戴设备中异常心脏状况的检测. 该方法增强了精确的波形定位,并很好地泛化到各种病态心电图模式.
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) 划分对于诊断心脏病,特别是通过可穿戴设备进行远程监测至关重要.
- 鉴定多种病理性心电图模式,由于心脏异常的复杂性,因此具有重大挑战.
研究的目的:
- 开发一个先进的网络,用于准确的心电图划分和异常模式识别.
- 为了提高精确的波形本地化,提高诊断能力.
主要方法:
- 建立了一个双分支框架,以ECG划分为主要任务,并以异常识别辅助网络作为辅助任务.
- 一个开始-补偿意识损失函数被设计为专注于精确的ECG波形特征的定位.
- 为了培训和验证,使用了包含4,913个信号的大型可穿戴12导电图数据集.
主要成果:
- 拟议的方法在测试数据集上实现了高灵敏度 (94.97%和94.27%).
- 优秀的定位准确性被证明是低于20毫秒的容错率.
- 该模型在识别各种异常的心电图信号,包括ST细分变化和捆绑分支块方面被证明是有效的.
结论:
- 联合学习方法有效地将心电图划分与异常识别联系在一起,增强模型的概括性.
- 开始-抵消意识损失显著提高了波形定位的精度.
- 这种方法为可穿戴健康监测系统中精确的ECG分析提供了强大的解决方案.
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