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. © 2025 by the authors.