通过机器学习指导,对超心室和心室起源的光聚体吸收波形之间的差异化
Martin Manninger1, Ingmar Lercher2, Astrid N L Hermans3
1Division of Cardiology, Department of Internal Medicine, Medical University of Graz, Graz, Austria; Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Centre, Maastricht, the Netherlands.
一个神经网络可以使用光聚光学 (PPG) 波形从可穿戴设备区分超心室和心室心律失常. 这项技术显示出对非侵入性心律失常检测和起源确定有前途.
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:
- 使用可穿戴光电显微镜 (PPG) 信号区分超心室与心室节律失常仍然具有挑战性.
- 调查PPG波形对心律失常起源分类的有用性对于非侵入性诊断至关重要.
研究的目的:
- 评估神经网络分类器是否能够准确地区分PPG脉冲波形的起源.
- 为了评估卷积神经网络在将PPG信号分类为超心室 (心房节奏) 或心室的性能.
主要方法:
- 在电生理学 (EP) 研究中,使用腕带设备记录了30名患者的PPG波形.
- 波形与心电图和心内电图同步,并标记为心房节奏 (AP) 或心室节奏 (VP).
- 在25,221个PPG波形样本上开发和验证了一个残余神经网络.
主要成果:
- 在独立的患者层面上,分类器实现了大约73%的AP准确率和59%的VP准确率.
- 通过适应性,针对患者的注释,分类器的准确性提高到AP的~97%和VP的~95%.
- 这项研究包括27名患者 (74%为女性,平均年龄为53岁) 患有各种动脉节律失常症.
结论:
- 在EP衍生的PPG数据上训练的神经网络可以区分脑上和心室起源.
- 仅仅通过神经网络分析的PPG波形就显示了识别心律失常类型的潜力.
- 这种方法可能提供一种使用可穿戴技术进行心律失常诊断的非侵入性方法.
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