Dictionary learning for the sparse modelling of atrial fibrillation in ECG signals

Mailhé, Boris; Gribonval, Rémi; Bimbot, Frédéric; Lemay, Mathieu; Vesin, Jean‐Marc

doi:10.1109/icassp.2009.4959621

Cited by 26 publications

(16 citation statements)

References 7 publications

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“…2. Sparse representations have been proposed by [13] for ECG source separation problems, which motivated the utilization of the proposed approach for Arrhythmia detection: given an ECG signal that contains mostly normal heartbeats, the key idea is to decompose the signal into all possible -samples windows (on the order of 1 second duration) and train a dictionary that will provide a sparse representation for these windows. Note that due to the multiplicity and periodicity of normal heartbeats, their corresponding windows are highly repetitive, and constitute the majority among all windows.…”

Section: Arrhythmia Detection In Ecg Signalsmentioning

confidence: 99%

Sparse coding with anomaly detection

Adler¹,

Elad²,

Hel-Or³

et al. 2013

2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

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We consider the problem of simultaneous sparse coding and anomaly detection in a collection of data vectors. The majority of the data vectors are assumed to conform with a sparse representation model, whereas the anomaly is caused by an unknown subset of the data vectors -the outlierswhich significantly deviate from this model. The proposed approach utilizes the Alternating Direction Method of Multipliers (ADMM) to recover simultaneously the sparse representations and the outliers components for the entire collection. This approach provides a unified solution both for jointly sparse and independently sparse data vectors. We demonstrate the usefulness of the proposed approach for irregular heartbeats detection in Electrocardiogram (ECG) and specular reflectance removal from natural images.

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Section: Arrhythmia Detection In Ecg Signalsmentioning

confidence: 99%

Sparse coding with anomaly detection

Adler¹,

Elad²,

Hel-Or³

et al. 2013

2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

View full text Add to dashboard Cite

show abstract

“…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. Consequently, several on-line dictionary learning approaches have also been applied, both in the context of sparse inference and compressed sensing (CS), to ECG signals: the K-SVD algorithm in [15], the shift-invariant K-SVD in [16], and the method of optimal directions in [17]. Unfortunately, all these methods have a high computational cost (due to their need to iterate between the dictionary learning and sparse approximation stages) and lead to dictionaries whose atoms do not correspond to real-world signals (thus reducing the interpretability of the sparse model, as well as the ability to easily locate the relevant waveforms).…”

Section: Introductionmentioning

confidence: 99%

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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“…Inspired by their work, dictionary-learning methods have been proposed in biomedical signal such as ECG, EEG and EMG. While most of the existing works focus on ECG [13,14] signals, there are fewer studies reported using EEG signals. Moreover, event-related EEG signals used in Brain-Computer-Interface (BCI) tend to be the potential candidate signals.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Comparison of EEG Compressed Sensing using Gabor and K-SVD Dictionaries

Dao

Griffin

2018

2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)

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With the fast development of wearable healthcare systems, compressed sensing (CS) has been proposed to be applied in electroencephalogram (EEG) acquisition. For CS, it is desired to build the best-fit dictionary in order to achieve good reconstruction accuracy. While most of existing works focused on static dictionaries such as Gabor, Fourier and wavelets, the dynamic nature of EEG signals motivates us to study learned dictionaries, which are supposed to provide better reconstruction accuracy and lower computation cost. In this paper, we provide the quantitative performance comparison of EEG CS using two different types of dictionaries, i.e., the well-known Gabor dictionaries versus K-SVD learned dictionaries. The performance comparison utilizes the well-established database of scalp EEG from Physiobank, which allows researchers in this field to compare their work with ours. In addition, it also attempts to inspire the systematic study of dictionary learning in EEG CS.

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Dictionary learning for the sparse modelling of atrial fibrillation in ECG signals

Cited by 26 publications

References 7 publications

Sparse coding with anomaly detection

Sparse coding with anomaly detection

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

Quantitative Comparison of EEG Compressed Sensing using Gabor and K-SVD Dictionaries

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