An efficient deep learning approach for arrhythmia classification using 3D temporal SVCG

The classification of heart arrhythmias through recordings of its electrical activity (ECGs) represent a coveted non-invasive diagnostic tool for the detection of life threatening conditions. Nevertheless, the design of fast and effective automatic deep learning procedures solving this task is not trivial. In this work, we developed an automatic pipeline for heart rhythm classification relying on a lighter temporal representation based on a scalar vectorcardiogram (SVCG). The signal is preliminarly downsampled using a Fast Fourier Transform (FFT) retaining high-level features. Afterwards, each ECG is converted to a 3D compact representation through Inverse Dower's transform and fed through a specifically designed convolutional neural network. The reliability of the proposed method is validated and compared to standard 12-lead ECGs from PhysioNet Computing in Cardiology Challenge (2020). We obtained a competitive test categorical accuracy for the largest dataset (SVCG 90.6 - ECG 87.1 on PTB-XL) and comparable results for the remaining sources. Compared with the existing methods, we propose an efficient method for the classification of heart arrhythmias through vectorcardiography, avoiding the need of hand-crafted features also having a lower computational and timing effort.