使用1D卷积神经网络优化节拍智能输入以检测心律失常:现实世界的心电图研究
Sunghan Lee1, Guangyao Zheng2, Jeonghwan Koh3
1Cerebrovascular Disease Research Center, Hallym University, Chuncheon, 24252, Republic of Korea.
Computer methods and programs in biomedicine
|June 24, 2025
概括
穿戴式设备的最佳心律失常检测使用四次心跳,平衡准确性和效率. 这种机器学习方法增强了实时移动健康应用程序,用于不规则的心跳诊断.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 心律失常在现实环境中存在诊断挑战.
- 机器学习有助于检测心律失常,但最佳输入大小尚未研究.
- 评估跨患者间和患者内疾病的表现至关重要.
研究的目的:
- 确定心律失常的最佳数量,以准确地进行心律失常的分类.
- 在不同的患者条件下评估机器学习模型的性能.
- 评估移动健康 (mHealth) 部署的绩效和资源的权衡.
主要方法:
- 预处理的心电图 (ECG) 信号使用节拍细分和重新采样.
- 采用1D卷积神经网络来分类8种多标签的心律失常.
- 通过对现实世界和数据库ECG数据的组合进行五倍交叉验证,验证了模型性能.
主要成果:
- 在患者间条件下,最高准确度 (94.82%) 达到4次心跳.
- 观察到超过四次节拍的最小性能增长.
- 在大规模模拟中实时检测的可行性已被证明 (约. 5000名患者) 的患者.
结论:
- 四次心跳为心律失常的分类提供了准确性和计算效率之间的最佳平衡.
- 这些发现对于资源有限的实时可穿戴心电图设备至关重要.
- 为设计可扩展和高效的mHealth心律失常检测系统提供参考.
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