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首页> 外文期刊>Biomedical Circuits and Systems, IEEE Transactions on >Photoplethysmography-Based Method for Automatic Detection of Premature Ventricular Contractions
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Photoplethysmography-Based Method for Automatic Detection of Premature Ventricular Contractions

机译：基于光体积描记法的心室早搏自动检测方法

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摘要

This work introduces a method for detection of premature ventricular contractions (PVCs) in photoplethysmogram (PPG). The method relies on 6 features, characterising PPG pulse power, and peak-to-peak intervals. A sliding window approach is applied to extract the features, which are later normalized with respect to an estimated heart rate. Artificial neural network with either linear and non-linear outputs was investigated as a feature classifier. PhysioNet databases, namely, the MIMIC II and the MIMIC, were used for training and testing, respectively. After annotating the PPGs with respect to synchronously recorded electrocardiogram, two main types of PVCs were distinguished: with and without the observable PPG pulse. The obtained sensitivity and specificity values for both considered PVC types were 92.4/99.9% and 93.2/99.9%, respectively. The achieved high classification results form a basis for a reliable PVC detection using a less obtrusive approach than the electrocardiography-based detection methods.
机译：这项工作介绍了一种检测光体积描记图（PPG）中室性早搏（PVC）的方法。该方法依赖于6个功能，分别表征PPG脉冲功率和峰峰值间隔。应用滑动窗口方法提取特征，然后相对于估计的心率将其标准化。研究了具有线性和非线性输出的人工神经网络作为特征分类器。 PhysioNet数据库，即MIMIC II和MIMIC，分别用于培训和测试。在针对同步记录的心电图对PPG进行注释之后，区分了两种主要类型的PVC：有和没有可观察到的PPG脉冲。对于两种考虑的PVC类型，获得的灵敏度和特异性值分别为92.4 / 99.9％和93.2 / 99.9％。与基于心电图的检测方法相比，所获得的高分类结果为使用较少干扰的方法进行可靠的PVC检测奠定了基础。

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