Objective

In this paper, we proposed new methods for feature extraction in machine learning-based classification of atrial fibrillation from ECG signal.

Methods

Our proposed methods improved conventional 1-dimensional local binary pattern method in two ways. First, we proposed a dynamic threshold LBP code generation method for use with 1-dimensional signals, enabling the generated LBP codes to have a more detailed representation of the signal morphological pattern. Second, we introduced a variable step value into the LBP code generation algorithm to better cope with a high sampling frequency input signal without a downsampling process. The proposed methods do not employ computationally expensive processes such as filtering, wavelet transform, up/downsampling, or beat detection, and can be implemented using only simple addition, division, and compare operations.

Results

Combining these two approaches, our proposed variable step dynamic threshold local binary pattern method achieved 99.11% sensitivity and 99.29% specificity when used as a feature generation algorithm in support vector machine classification of atrial fibrillation from MIT-BIH Atrial Fibrillation Database dataset. When applied on signals from MIT-BIH Arrhythmia Database, our proposed method achieved similarly good 99.38% sensitivity and 98.97% specificity.

Conclusion

Our proposed methods achieved one of the best results among published works in atrial fibrillation classification using the same dataset while using less computationally expensive calculations, without significant performance degradation when applied on signals from multiple databases with different sampling frequencies.

中文翻译：

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用于房颤分类的可变步长动态阈值局部二元模式

目标

在本文中，我们提出了基于机器学习的 ECG 信号心房颤动分类中特征提取的新方法。

方法

我们提出的方法以两种方式改进了传统的一维局部二进制模式方法。首先，我们提出了一种用于一维信号的动态阈值 LBP 代码生成方法，使生成的 LBP 代码能够更详细地表示信号形态模式。其次，我们在 LBP 代码生成算法中引入了可变步长值，以更好地处理高采样频率输入信号，而无需下采样过程。所提出的方法不使用计算上昂贵的过程，例如滤波、小波变换、上/下采样或节拍检测，并且可以仅使用简单的加法、除法和比较操作来实现。

结果

结合这两种方法，我们提出的可变步长动态阈值局部二进制模式方法在用作来自 MIT-BIH 心房颤动数据库数据集的心房颤动支持向量机分类中的特征生成算法时，实现了 99.11% 的灵敏度和 99.29% 的特异性。当应用于来自 MIT-BIH 心律失常数据库的信号时，我们提出的方法实现了同样良好的 99.38% 灵敏度和 98.97% 特异性。

结论

我们提出的方法在使用相同数据集的房颤分类中取得了最好的结果之一，同时使用计算成本较低的计算，当应用于来自具有不同采样频率的多个数据库的信号时，性能没有显着下降。