Blind analysis of atrial fibrillation electrograms: A sparsity-aware formulation

Luengo, David; Monzon, Sandra; Trigano, Tom; Vía, Javier; Artés‐Rodríguez, Antonio

doi:10.3233/ica-140471

Cited by 12 publications

(10 citation statements)

References 38 publications

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“…, 160 ms. Note also that the global dictionary is simply obtained by performing N − M different time shifts of the resulting sub-dictionary [13].…”

Section: Construction Of the Multi-scale Dictionarymentioning

confidence: 99%

“…In electrocardiographic signal processing, many approaches have been proposed for the sparse representation of single-channel and multi-channel ECGs using different types of simple analytical waveforms: Gaussians [8][9][10], generalized Gaussians and Gabor dictionaries [11], several families of wavelets (e.g., the Mexican hat or the coiflet4) [12,13], etc. Although these approaches can lead to good practical results, the resulting models usually contain many spurious activations that must be removed to obtain physiologically interpretable signals, for instance by means of a post-processing stage [9,13] or through the minimization of a complex non-convex cost function [14]. Conversely, a customized dictionary, built from real-world signals, will provide a better performance in terms of the reconstruction error obtained for a given level of sparsity.…”

Section: Introductionmentioning

confidence: 99%

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

2018

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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.

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“…, 160 ms. Note also that the global dictionary is simply obtained by performing N − M different time shifts of the resulting sub-dictionary [13].…”

Section: Construction Of the Multi-scale Dictionarymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2018

Self Cite

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“…Blind Source Separation (BSS) is a non-parametric algorithm capable of recovering the original sources from a mixture of signals [8,123]. An explanation of the algorithm is presented in a recent article by Amezquita-Sanchez and Adeli [1].…”

Section: Blind Source Separationmentioning

confidence: 99%

Time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals

Perez-Ramirez¹,

Amezquita-Sanchez²,

Adeli³

et al. 2016

J. vibroeng.

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A major trust of modal parameters identification (MPI) research in recent years has been based on using artificial and natural vibrations sources because vibration measurements can reflect the true dynamic behavior of a structure while analytical prediction methods, such as finite element models, are less accurate due to the numerous structural idealizations and uncertainties involved in the simulations. This paper presents a state-of-the-art review of the time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals as well as the factors that affect the estimation accuracy. Further, the latest signal processing techniques proposed since 2012 are also reviewed. These algorithms are worth being researched for MPI of large real-life structures because they provide good time-frequency resolution and noise-immunity.

show abstract

“…In electrocardiographic signal processing, many approaches have been devised for the sparse representation of single-channel and multi-channel ECGs using different types of simple analytical waveforms: Gaussians [8][9][10], generalized Gaussians and Gabor dictionaries [11], several families of wavelets (like the mexican hat or the coiflet4) [12,13], etc. Although these approaches can lead to good practical results, they usually result in many spurious activations that must be removed in order to obtain physiologically interpretable signals, for instance by means of a post-processing stage [9,13] or through the minimization of a complex non-convex cost function [14]. However, a customized dictionary, built from real-world signals, will provide a better performance in terms of the reconstruction error obtained for a given level of sparsity.…”

Section: Introductionmentioning

confidence: 99%

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

Luengo¹,

Meltzer²,

Trigano³

2018

Preprint

Self Cite

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The electrocardiogram (ECG) was the first biomedical signal where digital signal processing techniques were extensively applied. By its own nature, the ECG is typically a sparse signal, composed of regular activations (the QRS complexes and other waveforms, like the P and T waves) and periods of inactivity (corresponding to isoelectric intervals, like 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 an efficient sparse inference algorithm to model the signal using the learnt dictionary. As a result, our method is much more efficient from a computational point of view than other existing methods, thus becoming amenable to deal with long recordings from multiple patients. Regarding the dictionary construction, we locate first all the QRS complexes in the training database, then we compute a single average waveform per patient, and finally we select the most representative waveforms (using a correlation-based approach) as the basic atoms that will be 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.

show abstract

Blind analysis of atrial fibrillation electrograms: A sparsity-aware formulation

Cited by 12 publications

References 38 publications

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

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

Time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals

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

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