Atrial fibrillation (AF) is the most frequently occurring form of arrhythmia, which induces multiple fatal diseases and impairs the quality of life in patients; thus, the study of the diagnostic methods for detecting AF is clinically important. Here we present a feature extraction method for the detection of AF using a ballistocardiogram (BCG), which is based on a physiological signal database collected by a non-contact sensor. The BCG signals, including both with AF and sinus rhythm (SR), were collected from 37 subjects during overnight sleep (approximately 8 hours). The signals were split into 2915 one-minute segments (AF: 1494, SR: 1421) without overlap and labeled as AF and SR. BCG signals were transformed into BCG energy signals in order to highlight the features of AF and SR BCG signals; and four new data sequences representing different characteristics of the BCG energy signals were generated. The mean value, variance, skewness, and kurtosis of the four data sequences were calculated and 16 features were extracted for each segment. Five machine learning algorithms were used for classification. The results of this study show that the support vector machine (SVM) performed the best among the five tested classifiers and achieved sensitivity, precision, and accuracy of 0.968, 0.928, and 0.945, respectively. These results indicate that the proposed feature extraction method can be well applied to AF and SR classification and may lay foundations for the development of systems for long-term home cardiac monitoring and AF screening.

中文翻译：

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一种从BCG检测房颤的可行特征提取方法。

心房纤颤（AF）是心律失常最常发生的形式，可诱发多种致命性疾病并损害患者的生活质量。因此，研究检测AF的诊断方法具有重要的临床意义。在这里，我们介绍一种基于心电图（BCG）的AF检测功能的特征提取方法，该方法基于非接触式传感器收集的生理信号数据库。BCG信号包括AF和窦性心律（SR）均在夜间睡眠（约8小时）期间从37位受试者中收集。信号被分成2915个一分钟的片段（AF：1494，SR：1421），没有重叠，并标记为AF和SR。BCG信号被转换为BCG能量信号，以突出AF和SR BCG信号的特征；并生成了代表BCG能量信号不同特性的四个新数据序列。计算四个数据序列的平均值，方差，偏度和峰度，并为每个段提取16个特征。使用五种机器学习算法进行分类。这项研究的结果表明，支持向量机（SVM）在五个经过测试的分类器中表现最佳，灵敏度，精度和准确度分别达到0.968、0.928和0.945。这些结果表明，所提出的特征提取方法可以很好地应用于AF和SR分类，并且可以为开发用于长期家庭心脏监测和AF筛查的系统奠定基础。计算四个数据序列的峰度和峰度，并为每个片段提取16个特征。使用五种机器学习算法进行分类。这项研究的结果表明，支持向量机（SVM）在五个经过测试的分类器中表现最佳，灵敏度，精度和准确度分别达到0.968、0.928和0.945。这些结果表明，所提出的特征提取方法可以很好地应用于AF和SR分类，并且可以为开发用于长期家庭心脏监测和AF筛查的系统奠定基础。计算四个数据序列的峰度和峰度，并为每个片段提取16个特征。使用五种机器学习算法进行分类。这项研究的结果表明，支持向量机（SVM）在五个经过测试的分类器中表现最佳，灵敏度，精度和准确度分别达到0.968、0.928和0.945。这些结果表明，所提出的特征提取方法可以很好地应用于AF和SR分类，并且可以为开发用于长期家庭心脏监测和AF筛查的系统奠定基础。这项研究的结果表明，支持向量机（SVM）在五个经过测试的分类器中表现最佳，灵敏度，精度和准确度分别达到0.968、0.928和0.945。这些结果表明，所提出的特征提取方法可以很好地应用于AF和SR分类，并且可以为开发用于长期家庭心脏监测和AF筛查的系统奠定基础。这项研究的结果表明，支持向量机（SVM）在五个经过测试的分类器中表现最佳，灵敏度，精度和准确度分别达到0.968、0.928和0.945。这些结果表明，所提出的特征提取方法可以很好地应用于AF和SR分类，并且可以为开发用于长期家庭心脏监测和AF筛查的系统奠定基础。