Atrial fibrillation (AF) is the most frequently encountered cardiac arrhythmia and is often associated with other cardiovascular and cerebrovascular diseases, such as ischemic heart disease, chronic heart failure, and stroke. Automatic detection of AF by analyzing electrocardiogram (ECG) signals has an important application value. Using the contaminated and actual ECG signals, it is not enough to only analyze the atrial activity of disappeared P wave and appeared F wave in the TQ segment. Moreover, the best analysis method is to combine nonlinear features analyzing ventricular activity based on the detection of R peak. In this paper, to utilize the information of the P-QRS-T waveform generated by atrial and ventricular activity, frequency slice wavelet transform (FSWT) is adopted to conduct time-frequency analysis on short-term ECG segments from the MIT-BIH Atrial Fibrillation Database. The two-dimensional time-frequency matrices are obtained. Furthermore, an average sliding window is used to convert the two-dimensional time-frequency matrices to the one-dimensional feature vectors, which are classified using five machine learning (ML) techniques. The experimental results show that the classification performance of the Gaussian-kernel support vector machine (GKSVM) based on the Bayesian optimizer is better. The accuracy of the training set and validation set are 100% and 93.4%. The accuracy, sensitivity, and specificity of the test set without training are 98.15%, 96.43%, and 100%, respectively. Compared with previous research results, our proposed FSWT-GKSVM model shows stability and robustness, and it could achieve the purpose of automatic detection of AF.

中文翻译：

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基于频率切片小波变换和机器学习技术的短时心房颤动段自动检测

房颤（AF）是最常见的心律失常，常与其他心脑血管疾病有关，如缺血性心脏病、慢性心力衰竭和中风。通过分析心电图（ECG）信号自动检测AF具有重要的应用价值。使用污染的和实际的心电信号，仅分析TQ段消失的P波和出现的F波的心房活动是不够的。此外，最好的分析方法是结合非线性特征分析基于R峰检测的心室活动。在本文中，为了利用心房和心室活动产生的 P-QRS-T 波形信息，采用频率切片小波变换（FSWT）对来自MIT-BIH心房颤动数据库的短期心电图段进行时频分析。获得二维时频矩阵。此外，平均滑动窗口用于将二维时频矩阵转换为一维特征向量，这些向量使用五种机器学习 (ML) 技术进行分类。实验结果表明，基于贝叶斯优化器的高斯核支持向量机（GKSVM）的分类性能更好。训练集和验证集的准确率分别为 100% 和 93.4%。未经训练的测试集的准确率、灵敏度和特异性分别为 98.15%、96.43% 和 100%。与以往的研究结果相比，