In this article, we propose a novel ECG classification framework for atrial fibrillation (AF) detection using spectro-temporal representation (i.e., time varying spectrum) and deep convolutional networks. In the first step we use a Bayesian spectro-temporal representation based on the estimation of time-varying coefficients of Fourier series using Kalman filter and smoother. Next, we derive an alternative model based on a stochastic oscillator differential equation to accelerate the estimation of the spectro-temporal representation in lengthy signals. Finally, after comparative evaluations of different convolutional architectures, we propose an efficient deep convolutional neural network to classify the 2D spectro-temporal ECG data. The ECG spectro-temporal data are classified into four different classes: AF, non-AF normal rhythm (Normal), non-AF abnormal rhythm (Other), and noisy segments (Noisy). The performance of the proposed methods is evaluated and scored with the PhysioNet/Computing in Cardiology (CinC) 2017 dataset. The experimental results show that the proposed method achieves the overall F1 score of 80.2%, which is in line with the state-of-the-art algorithms.

在本文中，我们提出了一种新的心电图分类框架，用于使用频谱时态表示（即时变频谱）和深度卷积网络进行心房颤动（AF）检测。第一步，我们使用贝叶斯光谱-时间表示法，该方法基于卡尔曼滤波器和平滑器对傅里叶级数随时间变化的系数的估计。接下来，我们导出基于随机振荡器微分方程的替代模型，以加快对长信号中的频谱时间表示的估计。最后，在对不同卷积架构进行比较评估之后，我们提出了一种有效的深度卷积神经网络，以对二维时空心电图数据进行分类。心电图时空数据分为四个不同的类别：AF，非AF正常心律（Normal），非AF异常​​节律（其他）和嘈杂声段（嘈杂）。使用PhysioNet /心脏病学计算（CinC）2017数据集对提出的方法的性能进行评估和评分。实验结果表明，该方法的F1总体得分为80.2％，与最新算法相符。