Robust greedy deep dictionary learning for ECG arrhythmia classification

This work proposes a new deep learning method which we call robust deep dictionary learning RDDL. RDDL is suitable for learning representations from signals corrupted with sparse but large outliers such as artifacts and noise that are more heavy tailed than Gaussian distributions. Such outliers are common in biomedical signals e.g. EEG and ECG. RDDL learns multiple levels of non-linear dictionaries for representing the data. Instead of the standard Euclidean cost function that is usually employed in dictionary learning, we propose a robust l₁-norm cost function. In order to achieve sparse representation, an l₁-norm is imposed on the learned representation. The `depth' arises from the fact that multiple levels of dictionaries are learnt. The full formulation is solved in a greedy fashion, one layer at a time. To study the extent of usefulness of RDDL, we first benchmark it with two wellknown deep learning tools - the stacked denoising autoencoder and the deep belief network methods; experiments are carried out on benchmark deep learning datasets - MNIST, CIFAR-10 and SVHN. In all cases, our method yields the best results. Then the proposed method is used for learning representations of ECG data (containing arficacts) and for their classification using the MIT-BIH arrhythmia classification database. We compare it with traditional techniques as well as on deep learning tools. Our method yields the best results.