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Citations

4

Altmetrics

Article has an altmetric score of 14

Grant support

The authors acknowledge the Grants TED2021-129774B-C21, TED2021-129774B-C22, and PLEC2022-009235, funded by the Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033) and by the European Union "NextGenerationEU"/PRTR: the first one to A.B-N and N.G, and the next two to E.L-P. S.L.C acknowledges the Grant PID2023-147790OB-I00 funded by MCIU/AEI/10.13039/50110001103 3/FEDER, UE. The authors also acknowledge the Grant PEJ-2019-TL/BMD-12831 from Comunidad de Madrid to E.L-P and to M.V-O, and a Juan de la Cierva Incorporacion Grant (IJCI-2016-27698) to M.V-O. The CNIC is supported by the Instituto de Salud Carlos III (ISCIII) , the Ministerio de Ciencia, Innovacion y Universidades (MICIU) , and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (Grant CEX2020-001041-S funded by MICIU/AEI/10.13039/501100011033) .

Analysis of institutional authors

Bell-Navas, AndresCorresponding AuthorGaricano-Mena, JesusAuthorLe Clainche, SoledadAuthor

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December 24, 2024
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Automatic Cardiac Pathology Recognition in Echocardiography Images using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets

Publicated to:Expert Systems With Applications. 264 125849- - 2025-03-10 264(), DOI: 10.1016/j.eswa.2024.125849

Authors: Bell-Navas, A; Groun, N; Villalba-Orero, M; Lara-Pezzi, E; Garicano-Mena, J; Le Clainche, S

Affiliations

Ctr Computat Simulat CCS, Boadilla Del Monte 28660, Spain - Author
Ctr Nacl Invest Cardiovasc CNIC, C Melchor Fernandez Almagro 3, Madrid 28029, Spain - Author
Univ Complutense Madrid, Fac Vet, Dept Med & Cirugia Anim, Ave Puerta Hierro, Madrid 28040, Spain - Author
Univ Politecn Madrid, ETSI Aeronaut & Espacio, Pl Cardenal Cisneros 3, Madrid 28040, Spain - Author
Univ Politecn Madrid, ETSI Telecomunicac, Ave Complutense 30, Madrid 28040, Spain - Author
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Abstract

Heart diseases are the main international cause of human defunction. According to the WHO, nearly 18 million people decease each year because of heart diseases. Also considering the increase of medical data, much pressure is put on the health industry to develop systems for early and accurate heart disease recognition. In this work, an automatic cardiac pathology recognition system based on a novel deep learning framework is proposed, which analyses in real-time echocardiography video sequences. The system works in two stages. The first one transforms the data included in a database of echocardiography sequences into a machine learning- compatible collection of annotated images which can be used in the training phase of any kind of machine learning-based framework, including deep learning. This includes the use of the Higher Order Dynamic Mode Decomposition (HODMD) algorithm, for the first time to the authors' knowledge, for both data augmentation and feature extraction in the medical field. The second stage is focused on building and training a Vision Transformer (ViT), barely explored in the related literature. The ViT is adapted for an effective training from scratch, even with small datasets. The designed neural network analyses images from an echocardiography sequence to predict the heart state. The results obtained show the efficacy of the HODMD algorithm and the superiority of the proposed system, even outperforming pretrained Convolutional Neural Networks (CNNs), which are so far the method of choice in the literature.

Keywords

Cardiac pathology recognitionDeep learningEchocardiography imagingHigher order dynamic mode decompositionVision transformerVision transformers

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Expert Systems With Applications due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 24/197, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Artificial Intelligence.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-07-07:

  • Scopus: 1

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-07:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 10.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 10 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 14.
  • The number of mentions in news outlets: 2 (Altmetric).

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (BELL NAVAS, ANDRES) and Last Author (LE CLAINCHE MARTINEZ, SOLEDAD).

the author responsible for correspondence tasks has been BELL NAVAS, ANDRES.