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A noninvasive fetal electrocardiogram (fECG) provides additional clinical information for fetal heart monitoring. To date, the analysis of noninvasive fECG is challenging due to the cancellation of maternal QRS-complexes, despite significant advances in electrocardiography. Fetal QRS-complex is highly considered to measure fetal heart rate to detect some fetal abnormalities such as arrhythmia. In this study, we proposed a deep learning (DL) framework that stacked a convolutional layer and bidirectional long short-term memory for fetal QRS-complexes classification. The fECG signals are first preprocessed using discrete wavelet transform (DWT) to remove the noise or inferences. The following step beats and QRS-complex segmentation. The last step is fetal QRS-complex classification based on DL. In the experiment of Physionet\/Computing in Cardiology Challenge 2013, this study achieved 100% accuracy, sensitivity, specificity, precision, and F1-score. A stacked DL model demonstrates an effective tool for fetal QRS-complex classification and contributes to clinical applications for long-term maternal and fetal monitoring.<\/jats:p>","DOI":"10.1007\/s44196-023-00339-x","type":"journal-article","created":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T10:03:07Z","timestamp":1695722587000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Accurate Fetal QRS-Complex Classification from Abdominal Electrocardiogram Using Deep Learning"],"prefix":"10.1007","volume":"16","author":[{"given":"Annisa","family":"Darmawahyuni","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bambang","family":"Tutuko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8024-2952","authenticated-orcid":false,"given":"Siti","family":"Nurmaini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Naufal","family":"Rachmatullah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Ardiansyah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Firdaus","family":"Firdaus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ade Iriani","family":"Sapitri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anggun","family":"Islami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,9,26]]},"reference":[{"issue":"22","key":"339_CR1","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.120.017704","volume":"9","author":"Z Mandalenakis","year":"2020","unstructured":"Mandalenakis, Z., et al.: Survival in children with congenital heart disease: have we reached a peak at 97\\%? 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