Abstract
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.
Highlights
Atrial fibrillation (AF) is the most common cardiac arrhythmia, and its prevalence is around 1–2% worldwide [7]
3) For AF detection, we evaluate the proposals using PhysioNet/Computing in Cardiology (CinC) 2017 dataset [8], which is considered to be a challenging dataset that resembles practical applications, and our results are in line with the state-of-the-art
We proposed a spectro-temporal representation of ECG signals, based on state-space models, for application in deep network based atrial fibrillation detection
Summary
Atrial fibrillation (AF) is the most common cardiac arrhythmia, and its prevalence is around 1–2% worldwide [7]. This results to an irregular ventricular response which is one of the main characteristics of AF [49]
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