High-rate compression of ECG signals by an accuracy-driven sparsity model relying on natural basis

Grossi, Giuliano; Lanzarotti, Raffaella; Lin, Jianyi

doi:10.1016/j.dsp.2015.06.006

Cited by 17 publications

(8 citation statements)

References 31 publications

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“…Sparsity techniques have been already applied to the compression of electrocardiogram (ECG) signals [31, 32]. To highlight the benefit of the dictionary learning approach in this task, we tested the two methods K-SVD and R-SVD recasting the compression as a problem of sparse approximation with a dictionary.…”

Section: Experiments On Natural Datamentioning

confidence: 99%

Orthogonal Procrustes Analysis for Dictionary Learning in Sparse Linear Representation

2017

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In the sparse representation model, the design of overcomplete dictionaries plays a key role for the effectiveness and applicability in different domains. Recent research has produced several dictionary learning approaches, being proven that dictionaries learnt by data examples significantly outperform structured ones, e.g. wavelet transforms. In this context, learning consists in adapting the dictionary atoms to a set of training signals in order to promote a sparse representation that minimizes the reconstruction error. Finding the best fitting dictionary remains a very difficult task, leaving the question still open. A well-established heuristic method for tackling this problem is an iterative alternating scheme, adopted for instance in the well-known K-SVD algorithm. Essentially, it consists in repeating two stages; the former promotes sparse coding of the training set and the latter adapts the dictionary to reduce the error. In this paper we present R-SVD, a new method that, while maintaining the alternating scheme, adopts the Orthogonal Procrustes analysis to update the dictionary atoms suitably arranged into groups. Comparative experiments on synthetic data prove the effectiveness of R-SVD with respect to well known dictionary learning algorithms such as K-SVD, ILS-DLA and the online method OSDL. Moreover, experiments on natural data such as ECG compression, EEG sparse representation, and image modeling confirm R-SVD’s robustness and wide applicability.

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Section: Experiments On Natural Datamentioning

confidence: 99%

Orthogonal Procrustes Analysis for Dictionary Learning in Sparse Linear Representation

2017

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“…The first are aimed at uniformly enhancing the sparseness level by shrinking effects, while the latter to project back into the feasible space of solutions. A motivated reason to use k - LiMapS is that we have already demonstrated in past works its ability to find low-rank approximate solutions in tasks such as biomedical signal compression [35] and FR problems with very few training samples [36,37], and FR in presence of partial occlusions [38]. Here we show how to apply it to the SSPP problem which is one of the most challenging task in the realm of face analysis, as highlighted at the beginning of this paper.…”

Section: Methodsmentioning

confidence: 99%

Robust Single-Sample Face Recognition by Sparsity-Driven Sub-Dictionary Learning Using Deep Features

Cuculo

D’Amelio

Grossi

et al. 2019

Sensors

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Face recognition using a single reference image per subject is challenging, above all when referring to a large gallery of subjects. Furthermore, the problem hardness seriously increases when the images are acquired in unconstrained conditions. In this paper we address the challenging Single Sample Per Person (SSPP) problem considering large datasets of images acquired in the wild, thus possibly featuring illumination, pose, face expression, partial occlusions, and low-resolution hurdles. The proposed technique alternates a sparse dictionary learning technique based on the method of optimal direction and the iterative sans-serifℓ0-norm minimization algorithm called k-LiMapS. It works on robust deep-learned features, provided that the image variability is extended by standard augmentation techniques. Experiments show the effectiveness of our method against the hardness introduced above: first, we report extensive experiments on the unconstrained LFW dataset when referring to large galleries up to 1680 subjects; second, we present experiments on very low-resolution test images up to 8×8 pixels; third, tests on the AR dataset are analyzed against specific disguises such as partial occlusions, facial expressions, and illumination problems. In all the three scenarios our method outperforms the state-of-the-art approaches adopting similar configurations.

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“…Procedurally, for EDA we perform a standard continuous decomposition analysis aiming at unbiased scores of phasic and tonic activity, thus retaining only the phasic data. In regards to the HRV signal, once denoised the ECG signal, we achieve the beat-to-beat fluctuations as RR-interval time series from ECG using standard techniques [42]. After preprocessing stage, including signal segmentation which divides the signal into 4-sec overlapped windows, we select empirically a suitable level of Daubechies 3 (db3), following the rule…”

Section: Methodsmentioning

confidence: 99%

Deep Construction of an Affective Latent Space via Multimodal Enactment

Boccignone

Conte

Cuculo

et al. 2018

IEEE Trans. Cogn. Dev. Syst.

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We draw on a simulationist approach to the analysis of facially displayed emotions-e.g., in the course of a face-to-face interaction between an expresser and an observer. At the heart of such perspective lies the enactment of the perceived emotion in the observer. We propose a novel probabilistic framework based on a deep latent representation of a continuous affect space, which can be exploited for both the estimation and the enactment of affective states in a multimodal space (visible facial expressions and physiological signals). The rationale behind the approach lies in the large body of evidence from affective neuroscience showing that when we observe emotional facial expressions, we react with congruent facial mimicry. Further, in more complex situations, affect understanding is likely to rely on a comprehensive representation grounding the reconstruction of the state of the body associated with the displayed emotion. We show that our approach can address such problems in a unified and principled perspective, thus avoiding ad hoc heuristics while minimising learning efforts.

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High-rate compression of ECG signals by an accuracy-driven sparsity model relying on natural basis

Cited by 17 publications

References 31 publications

Orthogonal Procrustes Analysis for Dictionary Learning in Sparse Linear Representation

Orthogonal Procrustes Analysis for Dictionary Learning in Sparse Linear Representation

Robust Single-Sample Face Recognition by Sparsity-Driven Sub-Dictionary Learning Using Deep Features

Deep Construction of an Affective Latent Space via Multimodal Enactment

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