## 3 Results and Discussion
### ECG Delineation  
Based on the ECG denoising process using the best multi-layer filter model, a noise-free ECG signal can be produced. Following this, the ECG delineation process is carried out to separate the P-QRS-T-wave morphology through a classification scheme. The proposed parameters for the ECG delineation model are batch size of 16, learning rate of 0.0001, 100 epochs, an adaptive moment estimation (ADAM) optimizer, and a sparse categorical cross-entropy loss function. From the best model of ECG denoising in the FE amplitude-isoelectric centered method, transfer learning is performed where part of the first hidden layer of the model is frozen, while part of the last hidden layer is set to trainable. In the output layer, BiLSTM followed by dense layers with a Softmax activation function forms a delineation model. The performance results of ECG delineation with MLF-BiLSTM in record-based and beat-based approaches are visualized in Figure 7. Figure 7 shows the classification performance for unseen 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, while beat-based approaches performed well. As seen in Figure 7, the F1 score helps to understand the trade-off between precision and recall, making it a valuable metric. The delineation model with iCOR demonstrates effective signal delineation on both LUDB and QTDB datasets. It achieves an F1 score of above 85.3% for the P-wave class, 87.7% for the QRS-complex class, and 89% for the  
Figure 5: ECG signal denoising result with amplitude centered method. T-wave class with the beat-based approach. These results indicate that the model is robust and performs well across various sources of ECG signal data. Despite the differences in ECG signal recording characteristics between the QTDB and LUDB in terms of frequency, recording length, equipment used, and recorded leads, the proposed method effectively classifies the morphology of ECG signals. For a more comprehensive analysis, this study detected atrial fibrillation (AF) and atrial flutter (AFL) with the proposed iCOR model. Figure 8 presents the sample results of ECG delineation between ground truth and iCOR prediction in AF and AFL records, respectively. The results showed there is no significant gap between ground truth and iCOR prediction for P-QRS-T-wave classification. The characteristics of both are no visible P-waves, while in AF, the atria beat irregularly, and in AFL, the atria beat regularly. We have tested 14 AF records (8, 38, 44, 51, 83, 88, 93, 95, 96, 101, 110, 112, 129, and 173) and 3 AFL records (35, 52, and 103). The experimental results of both records are presented in Tables 4 and 5 for AF and AFL, respectively. The experiment of 14 AF records with iCOR showed that the sensitivity, precision, and F1 score of P-waves are zero (0). P-waves will not be seen on the ECG in patients with AF. consistent, regular atrial activity in both atria. AF and AFL showed absent P-waves in ECG signals. Table 5 presents absent P-waves in AFL records (35, 52, and 103). From the results of Tables 4 and 5, our proposed iCOR has successfully detected the characteristic of AF and AFL with absent P-wave morphology in ECG signals. Figure 7: The performance results of ECG delineation with iCOR in an unseen set.