在患者间的环境中使用富里埃分解和机器学习来检测心电图失常
Binish Fatimah1, Amit Singhal2, Pushpendra Singh3
1MaxEye Technologies Private Limited, Bengaluru, India.
Medical engineering & physics
|February 28, 2024
概括
这项研究改进了人工智能 (AI) 用于用患者间方法检测心电图 (ECG) 失律. 这种新方法提高了超腹外心跳 (SVEB) 检测精度,达到98.03%的整体精度.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 电心电图 (ECG) 节拍分类对于监测心脏异常至关重要.
- 现有的AI算法用于心律失常检测面临的挑战是未见的数据和超腹外心跳 (SVEB) 检测精度.
- 对于强大的AI模型性能评估,需要一种患者间验证方法.
研究的目的:
- 开发基于AI的ECG节拍分类方法,解决现有算法的局限性.
- 为了提高心律失常监测中超腹外心跳 (SVEB) 检测的准确性.
- 通过使用患者间范式来验证拟议的AI模型,以提高通用性.
主要方法:
- 使用富里埃分解法 (FDM) 对心电图信号进行多尺度分析.
- 从通过FDM获得的窄频信号组件中提取时间域和统计特征.
- 使用克鲁斯卡尔-瓦利斯测试和最小冗余最大相关性 (mRMR) 进行特征选择和减少冗余.
- 应用了一个支持向量机 (SVM) 分类器,用于节拍分类的线性内核.
主要成果:
- 拟议的方法在SVEB检测方面获得了89.35%的F1得分,超过了现有的算法.
- 在MIT-BIH心律失常数据集上获得了98.03%的整体准确率和91.84%的马修相关系数 (MCC).
- 患者间验证范式证明了该模型在未见数据上的有效性.
结论:
- 开发的AI方法有效地对心电图节拍进行分类,并以更高的准确度检测心律失常,特别是SVEB.
- 患者间验证策略确保更可靠地评估AI模型在临床环境中的性能.
- 这项研究为使用心电图数据监测心脏异常提供了强大而准确的AI解决方案.
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