iCOR: End-to-End Electrocardiography Morphology Classification Combining Multi-Layer Filter and BiLSTM  
[PERSON]  
1 Intelligent System Research Group, Universities Stiviiya, Palembang 30139, Indonesia; [EMAIL_ADDRESS] (B.T. ); [EMAIL_ADDRESS] (F.F. ); [EMAIL_ADDRESS] (M.N.R. ); [EMAIL_ADDRESS] (A.I.S. ); [EMAIL_ADDRESS] (A.I. ); [EMAIL_ADDRESS] (A.W.A. ); [EMAIL_ADDRESS] (M.I.P.) 2 Faculty of Computer Science, University of Indonesia, Depok 16424, Indonesia; [EMAIL_ADDRESS]  
3 Human Centre (HUNIC) Engineering School of Computing, Telkom University, Bandung 40257, Indonesia; [EMAIL_ADDRESS]  
4 Department of Computer Engineering, Faculty of Computer Science, Universitats Stiwiwiaya, Palembang 30139, Indonesia; [EMAIL_ADDRESS]  
5 Department of Cardiology & Vascular Medicine, Dr. Mohammad Hoosin Hospital, Palembang 30126, Indonesia; [EMAIL_ADDRESS]  
6 Correspondence: [PERSON]IL_ADDRESS] (S.N. ); [EMAIL_ADDRESS] (A. Several methods have been proposed to remove noise from ECG signals, and they can be grouped into [4] model-based methods [5; 6; 7; 8] and learning-based methods [3; 9; 10; 11]. Deep-learning-based (DL) methods have recently achieved incredible success in various fields, including in ECG signal denoising, which is crucial for accurate diagnosis. Several DL methods have been proposed such as the adversarial DL approach [2]. This method involves training a deep neural network to learn a sequence of non-linear transformations that can take noisy ECG signals as input and produce clean signals as output. Another method is based on disentangled autoencoders [12]. This approach involves introducing a disentangled mechanism to separate the signal features from the noise features. The disentangled autoencoder network (DANet) uses attention to shunt latent variables, de-constructing the potential spatial separation between noise and the original ECG data. Supervised learning-based methods like the denoising convolutional autoencoder (FCN-DAE) [13], traditional methods like principal component analysis (PCA) [14], and discrete wavelet transformation (DWT) [15] are also used. However, previous researchers primarily focused only on removing ECG noise and analyzing the results [16; 17; 18]. This study, however, goes further by delineating the denoised ECG signal to identify abnormalities in the ECG wave morphology through P-QRS-T-wave delineation [19; 20; 21; 22]. ECG analysis involves understanding how to extract the morphology of ECG waves and segments (delineation). ECG signal delineation is a critical step that involves accurately identifying key feature points such as the onset and offset of P-waves, QRS-complexes, and T-waves [19; 20; 21; 22]. However, accurately identifying the start, peak, and endpoint of the three primary ECG waveforms can be quite challenging. Moreover, diagnosing conditions through ECG signal analysis is a complex and time-consuming process that requires extensive training and specialized expertise. ECG delineation methods are broadly classified into two categories: digital signal processing [23; 24] and intelligent processing [25; 26; 27; 28]. Extensive research has been conducted on both approaches [23; 24; 25; 26; 27; 28]. However, certain challenges persist, including the insufficient examination of noise effects in ECG signal delineation methods [20]. Numerous studies have proposed various ECG delineation models, demonstrating promising results [19; 20; 21; 22; 29; 30]. However, accurately detecting and distinguishing between the P-, QRS-, and T-waves, handling noise and artifacts in the signal, and managing variations in wave morphology across different individuals and conditions remain challenges [11]. In addition, most previous studies only delineated normal signals as a baseline for interpreting abnormal morphology [21].