ECG compressed sensing based on classification in compressed space and specified dictionaries

An electrocardiographic signal (ECG) compressed sensing (CS) method, its reconstruction using specific dictionaries of cardiac pathologies and method evaluation testing using classical measures as well as by classification error of the reconstructed patterns based on the K-Nearest Neighbour classifier (KNN) are presented. For compressed sensing, a random matrix with standard normal distribution was used, followed by a classification of compressed signals in one of eight possible pathological classes. For each class a specific dictionary was created, and the signals were reconstructed using the Basis Pursuit algorithm according to the result of the classification.