Highly Nonlinear Approximations for Sparse Signal Representation
Sparsity based morphological differentiation of heartbeats
The electrocardiogram (ECG) is one of the most common primary tests to evaluate the health of the heart. Reliable automatic interpretation of ECG records is crucial to the goal of improving public health. The paper below presents a new methodology for morphological identification of heartbeats, which is placed outside the usual machine learning framework. The proposal considers the sparsity of the representation of a heartbeat as a parameter for morphologic identification. The approach involves greedy algorithms for selecting elements from redundant dictionaries, which should be previously learnt from examples of the classes to be identified.
by Laura Rebollo-Neira, Khalil Battikh, and Amadou Sidi Watt
This project is supported by EPSRC (EP/D062632/1)
