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dc.contributor.authorMohebbian, Mohammad Reza
dc.contributor.authorVedaei, Seyed Shahim
dc.contributor.authorWahid, Khan A.
dc.contributor.authorDinh, Anh
dc.contributor.authorMarateb, Hamid Reza
dc.contributor.authorTavakolian, Kouhyar
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2023-06-07T08:55:49Z
dc.date.available2024-02-04T01:29:33Z
dc.date.issued2022-02
dc.identifier.citationMohebbian, M. [et al.]. Fetal ECG extraction from maternal ECG using attention-based cycleGAN. "IEEE journal of biomedical and health informatics", Febrer 2022, vol. 26, núm. 2, p. 515-526.
dc.identifier.issn2168-2208
dc.identifier.otherhttps://pubmed.ncbi.nlm.nih.gov/34516382/
dc.identifier.urihttp://hdl.handle.net/2117/388306
dc.description© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.description.abstractA non-invasive fetal electrocardiogram (FECG) is used to monitor the electrical pulse of the fetal heart. Decomposing the FECG signal from the maternal ECG (MECG) is a blind source separation problem, which is hard due to the low amplitude of the FECG, the overlap of R waves, and the potential exposure to noise from different sources. Traditional decomposition techniques, such as adaptive filters, require tuning, alignment, or pre-configuration, such as modeling the noise or desired signal to map the MECG to the FECG. The high correlation between maternal and fetal ECG fragments decreases the performance of convolution layers. Therefore, the masking region of interest based on the attention mechanism was performed to improve the signal generators' precision. The sine activation function was also used to retain more details when converting two signal domains. Three available datasets from the Physionet, including the A&D FECG, NI-FECG, and NI-FECG challenge, and one synthetic dataset using FECGSYN toolbox, were used to evaluate the performance. The proposed method could map an abdominal MECG to a scalp FECG with an average of 98% R-Square [CI 95%: 97%, 99%] as the goodness of fit on the A&D FECG dataset. Moreover, it achieved 99.7% F1-score [CI 95%: 97.8-99.9], 99.6% F1-score [CI 95%: 98.2%, 99.9%] and 99.3% F1-score [CI 95%: 95.3%, 99.9%] for fetal QRS detection on the A&D FECG, NI-FECG and NI-FECG challenge datasets, respectively. Also, the distortion was in the “very good” and “good” ranges. These results are comparable to the state-of-the-art results; thus, the proposed algorithm has the potential to be used for high-performance signal-to-signal conversion.
dc.format.extent12 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal
dc.subjectÀrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica
dc.subject.lcshFetal monitoring
dc.subject.lcshElectrocardiography
dc.subject.otherFetal ECG
dc.subject.otherCycleGAN
dc.subject.otherBlind source separation
dc.subject.otherAttention layer
dc.titleFetal ECG extraction from maternal ECG using attention-based cycleGAN
dc.typeArticle
dc.subject.lemacMonitoratge fetal
dc.subject.lemacElectrocardiografia
dc.contributor.groupUniversitat Politècnica de Catalunya. BIOART - BIOsignal Analysis for Rehabilitation and Therapy
dc.identifier.doi10.1109/JBHI.2021.3111873
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9536378
dc.rights.accessOpen Access
local.identifier.drac36593516
dc.description.versionPostprint (author's final draft)
local.citation.authorMohebbian, M.; Vedaei, S.; Wahid, K.; Dinh, A.; Marateb, H.; Tavakolian, K.
local.citation.publicationNameIEEE journal of biomedical and health informatics
local.citation.volume26
local.citation.number2
local.citation.startingPage515
local.citation.endingPage526


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