一个同类和异质的多视图患者间适应性网络,用于检测心律失常
Zhaoyang Ma1, Jing Wang2, Jinghang Yue1
1School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China; Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing 100044, China.
Computer methods and programs in biomedicine
|August 11, 2023
概括
这项研究引入了一种用于检测心律失常的新型网络,使用心电图 (ECG) 信号和图像. 该方法通过适应个体患者的差异来提高准确性.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 目前的心律失常检测算法主要使用基于信号的数据,与通常依赖于基于图像的数据的临床实践形成差距.
- 个体生理差异对使用传统方法准确检测心律失常提出了挑战.
- 现有的方法很难有效地整合各种数据类型,以进行可靠的心律失常诊断.
研究的目的:
- 通过整合心电图 (ECG) 信号和心电图像来开发一种先进的心律失常检测网络.
- 为了克服在心律失常检测中的个体生理变异所带来的局限性.
- 在临床环境中提高心律失常评估的准确性和可靠性.
主要方法:
- 设计了一个同类和异质的多视图患者间适应性网络.
- 开发了一个多视图表示学习模块,从心电图信号和图像中提取动态和形态特征.
- 实施了一种新的损失函数,以对齐特征嵌入,并最大限度地减少患者间的变化.
主要成果:
- 与MIT-BIH心律失常数据库上最先进的基线相比,拟议的方法显示出更高的性能.
- 关键的绩效指标,包括准确性,积极的预测值,灵敏度和F1得分得到了显著改善.
- 实验结果证实了该方法在准确检测心律失常方面的有效性.
结论:
- 开发的网络有效地集成了ECG信号和图像数据,用于检测心律失常.
- 该方法成功地减轻了个体生理差异对检测准确性的影响.
- 这种方法具有显著的潜力,可以改善心律失常的早期诊断和治疗结果.
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