School of Control Science and Engineering, Shandong University, Jinan 250061, China.
School of Information Science and Engineering, Fujian University of Technology, Fuzhou 350118, China.
J Healthc Eng. 2018 Jul 2;2018:2102918. doi: 10.1155/2018/2102918. eCollection 2018.
Atrial fibrillation (AF) is a serious cardiovascular disease with the phenomenon of beating irregularly. It is the major cause of variety of heart diseases, such as myocardial infarction. Automatic AF beat detection is still a challenging task which needs further exploration. A new framework, which combines modified frequency slice wavelet transform (MFSWT) and convolutional neural networks (CNNs), was proposed for automatic AF beat identification. MFSWT was used to transform 1 s electrocardiogram (ECG) segments to time-frequency images, and then, the images were fed into a 12-layer CNN for feature extraction and AF/non-AF beat classification. The results on the MIT-BIH Atrial Fibrillation Database showed that a mean accuracy (Acc) of 81.07% from 5-fold cross validation is achieved for the test data. The corresponding sensitivity (Se), specificity (Sp), and the area under the ROC curve (AUC) results are 74.96%, 86.41%, and 0.88, respectively. When excluding an extremely poor signal quality ECG recording in the test data, a mean Acc of 84.85% is achieved, with the corresponding Se, Sp, and AUC values of 79.05%, 89.99%, and 0.92. This study indicates that it is possible to accurately identify AF or non-AF ECGs from a short-term signal episode.
心房颤动（AF）是一种严重的心血管疾病，其特征是不规则跳动。它是多种心脏病的主要病因，如心肌梗死。自动 AF 节拍检测仍然是一个具有挑战性的任务，需要进一步探索。提出了一种新的框架，将改进的频切片小波变换（MFSWT）和卷积神经网络（CNNs）相结合，用于自动 AF 节拍识别。MFSWT 用于将 1 秒心电图（ECG）段转换为时频图像，然后将图像输入到 12 层 CNN 中进行特征提取和 AF/非 AF 节拍分类。在 MIT-BIH 心房颤动数据库上的结果表明，对于测试数据，通过 5 倍交叉验证实现了平均准确率（Acc）为 81.07%。相应的灵敏度（Se）、特异性（Sp）和 ROC 曲线下面积（AUC）结果分别为 74.96%、86.41%和 0.88。当排除测试数据中一个信号质量极差的 ECG 记录时，平均 Acc 达到 84.85%，对应的 Se、Sp 和 AUC 值分别为 79.05%、89.99%和 0.92。这项研究表明，从短期信号发作中准确识别 AF 或非 AF ECG 是有可能的。
