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However, it is arduous to manually assess the leads, as a variety of signal morphological variations in each lead have potential defects in recording, noise, or irregular heart rhythm\/beat.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Method<\/jats:title>\n                <jats:p>A computer-aided deep-learning algorithm is considered a state-of-the-art delineation model to classify ECG waveform and boundary in terms of the P-wave, QRS-complex, and T-wave and indicated the satisfactory result. This study implemented convolution layers as a part of convolutional neural networks for automated feature extraction and bidirectional long short-term memory as a classifier. For beat segmentation, we have experimented beat-based and patient-based approach.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The empirical results using both beat segmentation approaches, with a total of 14,588 beats were showed that our proposed model performed excellently well. All performance metrics above 95% and 93%, for beat-based and patient-based segmentation, respectively.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>This is a significant step towards the clinical pertinency of automated 12-lead ECG delineation using deep learning.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-023-02233-0","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T08:02:02Z","timestamp":1690531322000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Improved delineation model of a standard 12-lead electrocardiogram based on a deep learning algorithm"],"prefix":"10.1186","volume":"23","author":[{"given":"Annisa","family":"Darmawahyuni","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"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":"Prazna Paramitha","family":"Avi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel Benedict Putra","family":"Teguh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ade Iriani","family":"Sapitri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bambang","family":"Tutuko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Firdaus","family":"Firdaus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,28]]},"reference":[{"key":"2233_CR1","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1016\/j.measurement.2018.05.033","volume":"125","author":"M Hammad","year":"2018","unstructured":"Hammad M, Maher A, Wang K, Jiang F, Amrani M. 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