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Open AccessArticle

An Efficient Method to Learn Overcomplete Multi-Scale Dictionaries of ECG Signals

1
Department of Signal Theory and Communications, Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación, Universidad Politécnica de Madrid, C/Nikola Tesla s/n, 28031 Madrid, Spain
2
Department of Telematic and Electronic Engineering, Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación, Universidad Politécnica de Madrid, C/Nikola Tesla s/n, 28031 Madrid, Spain
3
Department of Electrical and Electronics Engineering, Shamoon College of Engineering, Ashdod 77245, Israel
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(12), 2569; https://doi.org/10.3390/app8122569
Received: 7 November 2018 / Revised: 21 November 2018 / Accepted: 22 November 2018 / Published: 11 December 2018
The electrocardiogram (ECG) was the first biomedical signal for which digital signal processing techniques were extensively applied. By its own nature, the ECG is typically a sparse signal, composed of regular activations (QRS complexes and other waveforms, such as the P and T waves) and periods of inactivity (corresponding to isoelectric intervals, such as the PQ or ST segments), plus noise and interferences. In this work, we describe an efficient method to construct an overcomplete and multi-scale dictionary for sparse ECG representation using waveforms recorded from real-world patients. Unlike most existing methods (which require multiple alternative iterations of the dictionary learning and sparse representation stages), the proposed approach learns the dictionary first, and then applies a fast sparse inference algorithm to model the signal using the constructed dictionary. As a result, our method is much more efficient from a computational point of view than other existing algorithms, thus becoming amenable to dealing with long recordings from multiple patients. Regarding the dictionary construction, we located first all the QRS complexes in the training database, then we computed a single average waveform per patient, and finally we selected the most representative waveforms (using a correlation-based approach) as the basic atoms that were resampled to construct the multi-scale dictionary. Simulations on real-world records from Physionet’s PTB database show the good performance of the proposed approach. View Full-Text
Keywords: electrocardiogram (ECG); Least Absolute Shrinkage and Selection Operator (LASSO); overcomplete multi-scale dictionary construction; signal representation; sparse inference electrocardiogram (ECG); Least Absolute Shrinkage and Selection Operator (LASSO); overcomplete multi-scale dictionary construction; signal representation; sparse inference
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Luengo, D.; Meltzer, D.; Trigano, T. An Efficient Method to Learn Overcomplete Multi-Scale Dictionaries of ECG Signals. Appl. Sci. 2018, 8, 2569.

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