Unsupervised deep neuron-per-neuron hashing

Chafik, Sanaa; Yacoubi, Mounîm El; Daoudi, Imane; Ouardi, Hamid El

doi:10.1007/s10489-018-1353-5

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…A Convolutional Neural Network (CNN) AutoEncoder model was used to automatically extract features from DVP waves (Chafik et al, 2019). CNNs are widely employed in biomedical signal processing (Alaskar, 2018) since they are capable of extracting features by exploiting the convolution operation between the input Medoids (dark green) and DVPs (light green) for all the proposed approaches.…”

Section: Convolutional Neural Network Autoencodermentioning

confidence: 99%

Advances in Basic and Applied Research in Photoplethysmography

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Section: Convolutional Neural Network Autoencodermentioning

confidence: 99%

Advances in Basic and Applied Research in Photoplethysmography

2024

Frontiers Research Topics

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Section: Convolutional Neural Network Autoencodermentioning

confidence: 99%

Clustered photoplethysmogram pulse wave shapes and their associations with clinical data

Zanelli,

Eveilleau,

Charlton

et al. 2023

Front. Physiol.

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Photopletysmography (PPG) is a non-invasive and well known technology that enables the recording of the digital volume pulse (DVP). Although PPG is largely employed in research, several aspects remain unknown. One of these is represented by the lack of information about how many waveform classes best express the variability in shape. In the literature, it is common to classify DVPs into four classes based on the dicrotic notch position. However, when working with real data, labelling waveforms with one of these four classes is no longer straightforward and may be challenging. The correct identification of the DVP shape could enhance the precision and the reliability of the extracted bio markers. In this work we proposed unsupervised machine learning and deep learning approaches to overcome the data labelling limitations. Concretely we performed a K-medoids based clustering that takes as input 1) DVP handcrafted features, 2) similarity matrix computed with the Derivative Dynamic Time Warping and 3) DVP features extracted from a CNN AutoEncoder. All the cited methods have been tested first by imposing four medoids representative of the Dawber classes, and after by automatically searching four clusters. We then searched the optimal number of clusters for each method using silhouette score, the prediction strength and inertia. To validate the proposed approaches we analyse the dissimilarities in the clinical data related to obtained clusters.

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Learning hash index based on a shallow autoencoder

Lin

Huang

2022

Appl Intell

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Unsupervised deep neuron-per-neuron hashing

Cited by 4 publications

References 40 publications

Advances in Basic and Applied Research in Photoplethysmography

Advances in Basic and Applied Research in Photoplethysmography

Clustered photoplethysmogram pulse wave shapes and their associations with clinical data

Learning hash index based on a shallow autoencoder

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