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Visual Explanations of Deep Learning Architectures in Predicting Cyclic Alternating Patterns Using Wavelet Transforms

2023Journal article Open access
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dc.contributor.authorGupta, Ankit
dc.contributor.authorMendonça, Fabio
dc.contributor.authorMostafa, Sheikh Shanawaz
dc.contributor.authorRavelo-García, Antonio G.
dc.contributor.authorDias, Fernando Morgado
dc.date.accessioned2024-02-16T15:25:21Z
dc.date.available2024-02-16T15:25:21Z
dc.date.issued2023
dc.description.abstract: Cyclic Alternating Pattern (CAP) is a sleep instability marker defined based on the ampli tude and frequency of the electroencephalogram signal. Because of the time and intensive process of labeling the data, different machine learning and automatic approaches are proposed. However, due to the low accuracy of the traditional approach and the black box approach of the machine learning approach, the proposed systems remain untrusted by the physician. This study contributes to accurately estimating CAP in a Frequency-Time domain by A-phase and its subtypes prediction by transforming the monopolar deviated electroencephalogram signals into corresponding scalograms. Subsequently, various computer vision classifiers were tested for the A-phase using scalogram images. It was found that MobileNetV2 outperformed all other tested classifiers by achieving the average accuracy, sensitivity, and specificity values of 0.80, 0.75, and 0.81, respectively. The MobileNetV2 trained model was further fine-tuned for A-phase subtypes prediction. To further verify the visual ability of the trained models, Gradcam++ was employed to identify the targeted regions by the trained network. It was verified that the areas identified by the model match the regions focused on by the sleep experts for A-phase predictions, thereby proving its clinical viability and robustness. This motivates the development of novel deep learning based methods for CAP patterns predictions.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationGupta , A.; Mendonça, F.; Mostafa, S.S.; Ravelo-García, A.G.; Morgado-Dias, F. Visual Explanations of Deep Learning Architectures in Predicting Cyclic Alternating Patterns Using Wavelet Transforms. Electronics 2023, 12, 2954. https:// doi.org/10.3390/electronics12132954pt_PT
dc.identifier.doi10.3390/electronics12132954pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/5560
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationLaboratory of Robotics and Engineering Systems
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectContinuous wavelet transformpt_PT
dc.subjectCyclic alternating patternspt_PT
dc.subjectDeep learningpt_PT
dc.subjectElectroencephalogrampt_PT
dc.subjectSignal processingpt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleVisual Explanations of Deep Learning Architectures in Predicting Cyclic Alternating Patterns Using Wavelet Transformspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleLaboratory of Robotics and Engineering Systems
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50009%2F2020/PT
oaire.citation.issue13pt_PT
oaire.citation.startPage2954pt_PT
oaire.citation.titleElectronicspt_PT
oaire.citation.volume12pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameGupta
person.familyNameSilva Mendonça
person.familyNameMostafa
person.familyNameRavelo-García
person.familyNameMorgado-Dias
person.givenNameAnkit
person.givenNameFábio Rúben
person.givenNameSheikh Shanawaz
person.givenNameAntonio G.
person.givenNameFernando
person.identifier34497
person.identifier.ciencia-id9111-97C0-B28A
person.identifier.ciencia-id7F1E-8AE9-3098
person.identifier.ciencia-idEE14-BEB3-F82B
person.identifier.ciencia-id7B14-DF07-AA6D
person.identifier.orcid0000-0002-2310-908X
person.identifier.orcid0000-0002-5107-3248
person.identifier.orcid0000-0002-7677-0971
person.identifier.orcid0000-0002-8512-965X
person.identifier.orcid0000-0001-7334-3993
person.identifier.ridN-9228-2015
person.identifier.scopus-author-id57197874356
person.identifier.scopus-author-id55489640900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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