Sparse ECG Representation with a Multi-Scale Dictionary Derived from Real-World Signals

Luengo, David; Meltzer, David; Trigano, Tom

doi:10.1109/tsp.2018.8441329

Cited by 5 publications

(9 citation statements)

References 9 publications

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“…Note the large correlation (corresponding to dark red points) among most waveforms. For this reason, in [22], we decided to extract a single waveform in order to construct the multi-scale and overcomplete dictionary. However, in Figure 6, we also notice that some waveforms exhibit low correlation values (as shown by blue points).…”

Section: Dictionary Constructionmentioning

confidence: 99%

“…Numerical simulations demonstrate that the proposed approach is able to obtain a very sparse representation without missing any QRS complex or introducing spurious activations. Note that a preliminary version of this paper, where a single waveform was used to construct the overcomplete and multi-scale dictionary, was published in [22]. From a theoretical point of view, the main extension with respect to [22] is the proposal of a precise and novel procedure to incorporate multiple waveforms in the construction of the dictionary.…”

Section: Introductionmentioning

confidence: 99%

“…Note that a preliminary version of this paper, where a single waveform was used to construct the overcomplete and multi-scale dictionary, was published in [22]. From a theoretical point of view, the main extension with respect to [22] is the proposal of a precise and novel procedure to incorporate multiple waveforms in the construction of the dictionary. Additional improvements, such as the simple and effective approach to remove the edge effects that appear after each resampling stage, have also been introduced in the pre-processing stages.…”

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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Section: Dictionary Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2018

Self Cite

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show abstract

“…Note the large correlation (corresponding to dark red points) among most waveforms. For this reason, in [22] we decided to extract a single waveform in order to construct the multi-scale and over-complete dictionary. However, in Figure 6 we also notice that some waveforms exhibit low correlation values (as shown by blue points).…”

Section: Dictionary Constructionmentioning

confidence: 99%

“…A preliminary version of this paper was published in [22]. With respect to that version, a much more detailed literature review has been performed, the pre-processing has been improved, multiple waveforms have been used to construct the dictionary, and many more numerical simulations have been performed.…”

mentioning

confidence: 99%

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

Luengo¹,

Meltzer²,

Trigano³

2018

Preprint

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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.

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Overcomplete Multi-scale Dictionaries for Efficient Representation of ECG Signals

Meltzer

Luengo

Trigano

2020

Lecture Notes in Computer Science

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The electrocardiogram (ECG) was the first biomedical signal subject of extensive digital signal processing techniques. Essentially, the ECG consists of a cyclic sequence of relevant activations embedded into inactivity time sequences combined with interferences and noise. By its nature, it can be subject of representation as a sparse signal. This work describes an efficient method to create overcomplete multi-scale dictionaries that can be used for sparse ECG representation. Whereas most of the proposed methods to date use fixed waveforms that somehow resemble actual ECG shapes, the main innovation in our approach is selecting ECG waveforms recorded from actual patients. A relevant result of our method is the ability to process long lasting recordings from multiple patients. Simulations on patient actual records from Physionet's PTB Diagnostic ECG Database confirm the good performance of the proposed approach.

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Sparse ECG Representation with a Multi-Scale Dictionary Derived from Real-World Signals

Cited by 5 publications

References 9 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

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

Overcomplete Multi-scale Dictionaries for Efficient Representation of ECG Signals

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