## 4 Conclusions  
In this study, we proposed a novel approach for ECG signal delineation using an MLF combined with a BiLSTM model, namely iCOR. Our method addresses the limitations of previous models, which often performed sub-optimally on diverse datasets. Our approach achieves a simpler yet effective delineation model by utilizing the MLF for simultaneous noise removal and feature extraction and integrating these features into the BiLSTM for classification. The evaluation of combined LUDB and QTDB datasets demonstrates the robustness of our model, achieving high F1 scores. 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 outper  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Authors**} & \multicolumn{2}{c}{**Method**} & \multicolumn{3}{c}{**Accuracy (\%)**} \\ \cline{2-7}  & **ECG Denoising** & **ECG Delineation** & **P-Wave** & **QRS-Complex** & **T-Wave** \\ \hline [PERSON] et al. [21] & DAE & BiLSTM & \begin{tabular}{c} QTDB (normal \\ sinus records only) \\ \end{tabular} & 99.81 & 99.86 & 99.35 \\ \hline [PERSON] et al. [40] & \
\begin{tabular}{c} Regularized latent \\ space (encoder- \\ decoder) \\ \end{tabular} & ECGVEDNET & ICDIRS & - & 86.28 & 89.94 \\ \hline \multirow{2}{*}{Present study} & \multirow{2}{*}{iCOR} & \multicolumn{3}{c}{LUDB 98.8} & 98.8 & 98 \\ \cline{3-6}  & &
\begin{tabular}{c} QTDB (normal and \\ abnormal records) \\ \end{tabular} & 98.7 & 98.1 & 97 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The benchmarking studies of ECG denoising and delineation process with DL approach.  
formed 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 indicate that the proposed model can effectively delineate ECG signals from various sources, making it a versatile tool for clinical applications. The performance results of iCOR across different datasets underscore its potential for reliable and precise cardiac health monitoring. Future work will focus on further refining the model and exploring its application to other biomedical signal-processing tasks.  
[PERSON] wrote the manuscript, formal analysis, funding acquisition, and methodology. [PERSON] and S.M.: formal analysis. [PERSON] and E.E.: formal analysis and resources. [PERSON]: medical supervisor. A.D.: wrote the manuscript, formal analysis, and methodology. F.F, M.N.R. and M.I.P.: designed computer programs and curated the data. A.I.S., A.I. and A.W.A.: resources and prepared figures. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Collaboration Research Strategy (Katalis) Grants from the Ministry of Education, Culture, Research, and Technology, Indonesia, No. 142/E5/PG.02.00.PT/2022.  
The datasets generated and/or analyzed during the current study are available in the PhysioNet: MIT-BIH Noise Stress Test Database ([[https://physionet.org/content/nstdb/1.0.0/](https://physionet.org/content/nstdb/1.0.0/)]([https://physionet.org/content/nstdb/1.0.0/](https://physionet.org/content/nstdb/1.0.0/)), accessed on 28 February 2025), Lobachevsky University Electrocardiography Database ([[https://physionet.org/content/ludb/1.0.1/](https://physionet.org/content/ludb/1.0.1/)]([https://physionet.org/content/ludb/1.0.1/](https://physionet.org/content/ludb/1.0.1/)), accessed on 28 February 2025), QT Database ([[https://physionet.org/content/qtdb/1.0.0/](https://physionet.org/content/qtdb/1.0.0/)]([https://physionet.org/content/qtdb/1.0.0/](https://physionet.org/content/qtdb/1.0.0/)), accessed on 28 February 2025).  
We thank the Intelligent System Research Group (ISysRG), Faculty of Computer Science, Universitas Sriwijaya, Indonesia, for supporting the infrastructure.  
The authors have no conflicts of interest.