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In this article, we propose a novel ECG classification framework for atrialfibrillation (AF) detection using spectro-temporal representation (i.e., timevarying spectrum) and deep convolutional networks. In the first step we use aBayesian spectro-temporal representation based on the estimation oftime-varying coefficients of Fourier series using Kalman filter and smoother.Next, we derive an alternative model based on a stochastic oscillatordifferential equation to accelerate the estimation of the spectro-temporalrepresentation in lengthy signals. Finally, after comparative evaluations ofdifferent convolutional architectures, we propose an efficient deepconvolutional 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 noisysegments (Noisy). The performance of the proposed methods is evaluated andscored with the PhysioNet/Computing in Cardiology (CinC) 2017 dataset. Theexperimental results show that the proposed method achieves the overall F1score of 80.2%, which is in line with the state-of-the-art algorithms.

Kalman-based Spectro-Temporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation Detection