{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:26:31Z","timestamp":1783527991194,"version":"3.55.0"},"reference-count":37,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T00:00:00Z","timestamp":1714953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61871005"],"award-info":[{"award-number":["61871005"]}]},{"name":"National Natural Science Foundation of China","award":["11804013"],"award-info":[{"award-number":["11804013"]}]},{"name":"National Natural Science Foundation of China","award":["61801312"],"award-info":[{"award-number":["61801312"]}]},{"name":"National Natural Science Foundation of China","award":["4222001"],"award-info":[{"award-number":["4222001"]}]},{"name":"National Natural Science Foundation of China","award":["4184081"],"award-info":[{"award-number":["4184081"]}]},{"name":"Beijing Natural Science Foundation","award":["61871005"],"award-info":[{"award-number":["61871005"]}]},{"name":"Beijing Natural Science Foundation","award":["11804013"],"award-info":[{"award-number":["11804013"]}]},{"name":"Beijing Natural Science Foundation","award":["61801312"],"award-info":[{"award-number":["61801312"]}]},{"name":"Beijing Natural Science Foundation","award":["4222001"],"award-info":[{"award-number":["4222001"]}]},{"name":"Beijing Natural Science Foundation","award":["4184081"],"award-info":[{"award-number":["4184081"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The fetal electrocardiogram (FECG) records changes in the graph of fetal cardiac action potential during conduction, reflecting the developmental status of the fetus in utero and its physiological cardiac activity. Morphological alterations in the FECG can indicate intrauterine hypoxia, fetal distress, and neonatal asphyxia early on, enhancing maternal and fetal safety through prompt clinical intervention, thereby reducing neonatal morbidity and mortality. To reconstruct FECG signals with clear morphological information, this paper proposes a novel deep learning model, CBLS-CycleGAN. The model\u2019s generator combines spatial features extracted by the CNN with temporal features extracted by the BiLSTM network, thus ensuring that the reconstructed signals possess combined features with spatial and temporal dependencies. The model\u2019s discriminator utilizes PatchGAN, employing small segments of the signal as discriminative inputs to concentrate the training process on capturing signal details. Evaluating the model using two real FECG signal databases, namely \u201cAbdominal and Direct Fetal ECG Database\u201d and \u201cFetal Electrocardiograms, Direct and Abdominal with Reference Heartbeat Annotations\u201d, resulted in a mean MSE and MAE of 0.019 and 0.006, respectively. It detects the FQRS compound wave with a sensitivity, positive predictive value, and F1 of 99.51%, 99.57%, and 99.54%, respectively. This paper\u2019s model effectively preserves the morphological information of FECG signals, capturing not only the FQRS compound wave but also the fetal P-wave, T-wave, P-R interval, and ST segment information, providing clinicians with crucial diagnostic insights and a scientific foundation for developing rational treatment protocols.<\/jats:p>","DOI":"10.3390\/s24092948","type":"journal-article","created":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T14:26:11Z","timestamp":1715005571000},"page":"2948","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Enhancing Fetal Electrocardiogram Signal Extraction Accuracy through a CycleGAN Utilizing Combined CNN\u2013BiLSTM Architecture"],"prefix":"10.3390","volume":"24","author":[{"given":"Yuyao","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuicai","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e017704","DOI":"10.1161\/JAHA.120.017704","article-title":"Survival in children with congenital heart disease: Have we reached a peak at 97%?","volume":"9","author":"Mandalenakis","year":"2020","journal-title":"J. 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