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The electrocardiogram (ECG) is one of the most extensively employed signalsused in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECGsignals can capture the heart's rhythmic irregularities, commonly known asarrhythmias. A careful study of ECG signals is crucial for precise diagnoses ofpatients' acute and chronic heart conditions. In this study, we propose atwo-dimensional (2-D) convolutional neural network (CNN) model for theclassification of ECG signals into eight classes; namely, normal beat,premature ventricular contraction beat, paced beat, right bundle branch blockbeat, left bundle branch block beat, atrial premature contraction beat,ventricular flutter wave beat, and ventricular escape beat. The one-dimensionalECG time series signals are transformed into 2-D spectrograms throughshort-time Fourier transform. The 2-D CNN model consisting of fourconvolutional layers and four pooling layers is designed for extracting robustfeatures from the input spectrograms. Our proposed methodology is evaluated ona publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-artaverage classification accuracy of 99.11\%, which is better than those ofrecently reported results in classifying similar types of arrhythmias. Theperformance is significant in other indices as well, including sensitivity andspecificity, which indicates the success of the proposed method.

Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation