Variable step dynamic threshold local binary pattern for classification of atrial fibrillation

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.