{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:37:05Z","timestamp":1774629425515,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T00:00:00Z","timestamp":1744934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education, Culture, Research, and Technology, Indonesia","award":["142\/E5\/PG.02.00.PT\/2022"],"award-info":[{"award-number":["142\/E5\/PG.02.00.PT\/2022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Accurate delineation of ECG signals is critical for effective cardiovascular diagnosis and treatment. However, previous studies indicate that models developed for specific datasets and environments perform poorly when used with varying ECG signal morphology characteristics. This paper presents a novel approach to ECG signal delineation using a multi-layer filter (MLF) combined with a bidirectional long short-term memory (BiLSTM) model, namely iCOR. The proposed iCOR architecture enhances noise removal and feature extraction, resulting in improved classification of the P-QRS-T-wave morphology with a simpler model. Our method is evaluated on a combination of two standard ECG databases, the Lobachevsky University Electrocardiography Database (LUDB) and QT Database (QTDB). It can be observed that the classification performance for unseen sets of LUDB datasets yields above 90.4% and 98% accuracy, for record-based and beat-based approaches, respectively. Beat-based approaches outperformed the record-based approach in overall performance metric results. Similar results were shown in an unseen set of the QTDB, in which beat-based approaches performed with accuracy above 97%. These results highlight the robustness and efficacy of the iCOR model across diverse ECG signal datasets. The proposed approach offers a significant advancement in ECG signal analysis, paving the way for more reliable and precise cardiac health monitoring.<\/jats:p>","DOI":"10.3390\/a18040236","type":"journal-article","created":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T06:23:56Z","timestamp":1744957436000},"page":"236","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["iCOR: End-to-End Electrocardiography Morphology Classification Combining Multi-Layer Filter and BiLSTM"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8024-2952","authenticated-orcid":false,"given":"Siti","family":"Nurmaini","sequence":"first","affiliation":[{"name":"Intelligent System Research Group, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wisnu","family":"Jatmiko","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, University of Indonesia, Depok 16424, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6997-5875","authenticated-orcid":false,"given":"Satria","family":"Mandala","sequence":"additional","affiliation":[{"name":"Human Centric (HUMIC) Engineering, School of Computing, Telkom University, Bandung 40257, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bambang","family":"Tutuko","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erwin","family":"Erwin","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander Edo","family":"Tondas","sequence":"additional","affiliation":[{"name":"Department of Cardiology & Vascular Medicine, Dr. Mohammad Hoesin Hospital, Palembang 30126, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0229-5717","authenticated-orcid":false,"given":"Annisa","family":"Darmawahyuni","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2791-3486","authenticated-orcid":false,"given":"Firdaus","family":"Firdaus","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad Naufal","family":"Rachmatullah","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ade Iriani","family":"Sapitri","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anggun","family":"Islami","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akhiar Wista","family":"Arum","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad Ikhwan","family":"Perwira","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s42444-022-00075-x","article-title":"Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis","volume":"23","author":"Chung","year":"2022","journal-title":"Int. J. Arrhythmia"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mvuh, F.L., Ebode Ko\u2019a, C.O.V., and Bodo, B. (2024). Multichannel high noise level ECG denoising based on adversarial deep learning. Sci. Rep., 14.","DOI":"10.1038\/s41598-023-50334-7"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"60806","DOI":"10.1109\/ACCESS.2019.2912036","article-title":"Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders","volume":"7","author":"Chiang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hou, Y., Liu, R., Shu, M., and Chen, C. (2023). An ECG denoising method based on adversarial denoising convolutional neural network. Biomed. Signal Process. Control, 84.","DOI":"10.1016\/j.bspc.2023.104964"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2382","DOI":"10.17762\/turcomat.v12i2.2033","article-title":"ECG denoising using artificial neural networks and complete ensemble empirical mode decomposition","volume":"12","author":"Birok","year":"2021","journal-title":"Turkish J. Comput. Math. Educ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7536","DOI":"10.48084\/etasr.4302","article-title":"Denoising the ECG signal using ensemble empirical mode decomposition","volume":"11","author":"Mohguen","year":"2021","journal-title":"Eng. Technol. Appl. Sci. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.isatra.2020.12.029","article-title":"Stationary wavelet transform based ECG signal denoising method","volume":"114","author":"Kumar","year":"2021","journal-title":"ISA Trans."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"23595","DOI":"10.1109\/ACCESS.2021.3056459","article-title":"ECG baseline estimation and denoising with group sparse regularization","volume":"9","author":"Shi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.neucom.2019.03.083","article-title":"Adversarial de-noising of electrocardiogram","volume":"349","author":"Wang","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1109\/TCBB.2020.2976981","article-title":"A new ECG denoising framework using generative adversarial network","volume":"18","author":"Singh","year":"2020","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Romero, F.P., Pi\u00f1ol, D.C., and V\u00e1zquez-Seisdedos, C.R. (2021). DeepFilter: An ECG baseline wander removal filter using deep learning techniques. Biomed. Signal Process. Control, 70.","DOI":"10.1016\/j.bspc.2021.102992"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lin, H., Liu, R., and Liu, Z. (2023). ECG signal denoising method based on disentangled autoencoder. Electronics, 12.","DOI":"10.3390\/electronics12071606"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chorney, W., Wang, H., He, L., Lee, S., and Fan, L.-W. (2023). Convolutional block attention autoencoder for denoising electrocardiograms. Biomed. Signal Process. Control, 86.","DOI":"10.1016\/j.bspc.2023.105242"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1007\/s00034-014-9864-8","article-title":"Effect of multiscale PCA de-noising in ECG beat classification for diagnosis of cardiovascular diseases","volume":"34","author":"Alickovic","year":"2015","journal-title":"Circuits Syst. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5751","DOI":"10.1016\/j.eswa.2010.02.033","article-title":"A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification","volume":"37","author":"Khorrami","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"9023478","DOI":"10.1155\/2022\/9023478","article-title":"A machine learning approach for the detection of QRS complexes in electrocardiogram (ECG) using discrete wavelet transform (DWT) algorithm","volume":"2022","author":"Rizwan","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1080\/24699322.2018.1560088","article-title":"A new modified wavelet-based ECG denoising","volume":"24","author":"Wang","year":"2019","journal-title":"Comput. Assist. Surg."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1515\/cdbme-2021-2143","article-title":"Use of a trained denoising autoencoder to estimate the noise level in the ECG","volume":"7","author":"Samann","year":"2021","journal-title":"Curr. Dir. Biomed. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jimenez-Perez, G., Alcaine, A., and Camara, O. (2021). Delineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks. Sci. Rep., 11.","DOI":"10.1038\/s41598-020-79512-7"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"92600","DOI":"10.1109\/ACCESS.2021.3092631","article-title":"Beat-to-Beat Electrocardiogram Waveform Classification Based on a Stacked Convolutional and Bidirectional Long Short-Term Memory","volume":"9","author":"Nurmaini","year":"2021","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tutuko, B., Darmawahyuni, A., Nurmaini, S., Tondas, A.E., Naufal Rachmatullah, M., Teguh, S.B.P., Firdaus, F., Sapitri, A.I., and Passarella, R. (2022). DAE-ConvBiLSTM: End-to-end learning single-lead electrocardiogram signal for heart abnormalities detection. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0277932"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Nurmaini, S., Darmawahyuni, A., Rachmatullah, M.N., Firdaus, F., Sapitri, A.I., Tutuko, B., Tondas, A.E., Putra, M.H.P., and Islami, A. (2023). Robust electrocardiogram delineation model for automatic morphological abnormality interpretation. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-40965-1"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/j.measurement.2011.10.025","article-title":"Delineation of ECG characteristic features using multiresolution wavelet analysis method","volume":"45","author":"Banerjee","year":"2012","journal-title":"Measurement"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2825","DOI":"10.1109\/JBHI.2020.2973982","article-title":"An automatic R and T peak detection method based on the combination of hierarchical clustering and discrete wavelet transform","volume":"24","author":"Chen","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"113911","DOI":"10.1016\/j.eswa.2020.113911","article-title":"DENS-ECG: A deep learning approach for ECG signal delineation","volume":"165","author":"Peimankar","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Londhe, A.N., and Atulkar, M. (2021). Semantic segmentation of ECG waves using hybrid channel-mix convolutional and bidirectional LSTM. Biomed. Signal Process. Control, 63.","DOI":"10.1016\/j.bspc.2020.102162"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, D., Qiu, L., Zhu, W., Dong, Y., Zhang, H., Chen, Y., and Wang, L. (2023). Inter-patient ECG characteristic wave detection based on convolutional neural network combined with transformer. Biomed. Signal Process. Control, 81.","DOI":"10.1016\/j.bspc.2022.104436"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jimenez-Perez, G., Alcaine, A., and Camara, O. (2019, January 8\u201311). U-Net Architecture for the Automatic Detection and Delineation of the Electrocardiogram. Proceedings of the 2019 Computing in Cardiology (CinC), Singapore.","DOI":"10.22489\/CinC.2019.284"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.future.2020.02.068","article-title":"A knowledge-based deep learning method for ECG signal delineation","volume":"109","author":"Wang","year":"2020","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liang, X., Li, L., Liu, Y., Chen, D., Wang, X., Hu, S., Wang, J., Zhang, H., Sun, C., and Liu, C. (2022). ECG_SegNet: An ECG delineation model based on the encoder-decoder structure. Comput. Biol. Med., 145.","DOI":"10.1016\/j.compbiomed.2022.105445"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_32","unstructured":"Laguna, P., Mark, R.G., Goldberg, A., and Moody, G.B. (1997, January 7\u201310). A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. Proceedings of the Computers in Cardiology, Lund, Sweden."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"186181","DOI":"10.1109\/ACCESS.2020.3029211","article-title":"Ludb: A new open-access validation tool for electrocardiogram delineation algorithms","volume":"8","author":"Kalyakulina","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Dhas, D.E., and Suchetha, M. (2022). Dual phase dependent RLS filtering approach for baseline wander removal in ECG signal acquisition. Biomed. Signal Process. Control, 77.","DOI":"10.1016\/j.bspc.2022.103767"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Halvaei, H., S\u00f6rnmo, L., and Stridh, M. (2021). Signal quality assessment of a novel ECG electrode for motion artifact reduction. Sensors, 21.","DOI":"10.3390\/s21165548"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"109104","DOI":"10.1016\/j.jneumeth.2021.109104","article-title":"A hybrid method for muscle artifact removal from EEG signals","volume":"353","author":"Chen","year":"2021","journal-title":"J. Neurosci. Methods"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"97760","DOI":"10.1109\/ACCESS.2021.3095248","article-title":"A comprehensive survey on ECG signals as new biometric modality for human authentication: Recent advances and future challenges","volume":"9","author":"Uwaechia","year":"2021","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1007\/s44150-021-00015-8","article-title":"Error metrics and performance fitness indicators for artificial intelligence and machine learning in engineering and sciences","volume":"3","author":"Naser","year":"2023","journal-title":"Archit. Struct. Constr."},{"key":"ref_39","unstructured":"Liang, J. (2022). Confusion matrix: Machine learning. POGIL Act. Clgh., 3, Available online: https:\/\/pac.pogil.org\/index.php\/pac\/article\/view\/304."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2143","DOI":"10.1109\/TBME.2024.3363077","article-title":"ECGVEDNET: A Variational Encoder-Decoder Network for ECG Delineation in Morphology Variant ECGs","volume":"71","author":"Chen","year":"2024","journal-title":"IEEE Trans. Biomed. Eng."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/4\/236\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:17:19Z","timestamp":1760030239000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/4\/236"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,18]]},"references-count":40,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["a18040236"],"URL":"https:\/\/doi.org\/10.3390\/a18040236","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,18]]}}}