Arrhythmia Detection Using Convolutional Neural Models

Abstract

Mostly all works dealing with ECG signal and Convolutional Network approach use 1D CNNs and must train them from scratch, usually applying a signal preprocessing, such as noise reduction, R-peak detection or heartbeat detection. Instead, our approach was focused on demonstrating that effective transfer learning from 2D CNNs can be done using a well-known CNN called AlexNet, that was trained using real images from ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012. From any temporal signal, it is possible to generate spectral images (spectrograms) than can be analysed by 2D CNN to do the task of extracting automatic features for the classification stage. In this work, the power spectrogram is generated from a randomly ECG segment, so no conditions of signal extraction are applied. After processing the spectrogram with the CNN, its outputs are used as relevant features to be discriminated by a Multi Layer Perceptron (MLP) which classifies them into arrhythmic or normal rhythm segments. The results obtained are in the 90% accuracy range, as good as the state of the art published with 1D CNNs, confirming that transfer learning is a good strategy to develop decision models in signal and image medical tasks.