## 2 Materials and Methods
### iCOR Proposed Model  
This study proposes a multi-layer filter (MLF) architecture used for noise removal and delineation processes on ECG signals. In the noise removal process, the MLF model with 1D CNN on the output layer applies the regression principle. Meanwhile, for the ECG delineation process, the MLF model with BiLSTM on the output layer applies the classification principle. This end-to-end learning process is called iCOR. The name of iCOR comprises intelligent and COR. COR comes from the Latin word for \"heart\". COR is often used in medical terminology related to cardiology. Therefore, this study was developed the intelligent system for AI-powered systems for analyzing the heart using ECG signals. iCOR for ECG signals is presented in Figure 3. Figure 3 shows a two-step process involving noise removal and delineation that is described below:  
The denoising process employs an MLF to reduce noise. The MLF model was adopted using principles from previous research [11]. In [11], they propose a novel algorithm for baseline wander noise filtering using deep learning techniques. From [11], the best-performing model identified is the deep filter model called Multibranch Linear and Non-Linear Dilated (MBLANLD), which achieved the highest performance in denoising. With the adopted MBLANLD, in this study, an MLF is simultaneously used for noise removal and feature extraction from the ECG signal. The ECG signals, with interference from some noise, undergo processing through several layers of filter modules, each with varying numbers of filters and dilation rates to control the sensitivity and scale of the filtering. Figure 2: The sample of QTDB for _.pu0_ and LUDB for lead II with a frequency 360 Hz. The ECG noise removal includes an output layer with a 1D CNN for a regressor. In the denoising model that is presented in Figure 3, the output layer consists of a 1D CNN with 9 kernels, utilizing a linear activation function. The first two modules, (b) and (c), consist of a total of 64 convolutional filters and follow the internal structure. In the second multipath module (c), convolutional operations were applied with a dilation rate of (r = 3), enabling dilated convolutions. The same dilation rate was also used in the fourth and sixth modules. As the network progresses, the number of extracted features gradually decreases. The first two modules contain 64 features, followed by 32 features in the third and fourth modules. Finally, the fifth and sixth modules each extract 16 features. The final stage consists of a single convolutional filter with a kernel size of 9, which shapes the output signal. The process of delineation involves the categorization of modules into two main groups: frozen-weight, also known as problem-independent, and trainable-weight, which are problem-dependent. Within the frozen-weight modules, the weights are pre-trained and fixed, facilitating the processing of clean input data by passing them through various filtering stages to establish a baseline for the denoising procedure. Concurrently, the noisy input data undergo processing by the trainable-weight modules, where the weights are adaptable throughout the training phase. These modules work to enhance the data, progressively converting the noisy input into a more refined version. The ultimate result is a visualization of the delineated data that accentuates specific characteristics, thus concluding the dual-stage process of purification and feature extraction. This design effectively integrates both problem-independent and problem-dependent modules to offer broad applicability across diverse datasets while still maintaining precision for particular objectives. The model's outcome will deliver an ECG signal that has undergone denoising and has been categorized into four morphological classes: P-waves, QRS-complexes, T-waves, and isoelectric lines. To further refine the classification process, a combination of BiLSTM and dense layers is employed.