###### Abstract  
Heart diseases such as cardiovascular and myocardial infarction are the foremost reasons of death in the world. The timely, accurate, and effective prediction of heart diseases is crucial for saving lives. Electrocardiography (ECG) is a primary non-invasive method to identify cardiac abnormalities. However, manual interpretation of ECG recordings for heart disease diagnosis is a time-consuming and inaccurate process. For the accurate and efficient detection of heart diseases from the 12-lead ECG dataset, we have proposed a hybrid residual/inception-based deeper model (HRIDM). In this study, we have utilized ECG datasets from various sources, which are multi-institutional large ECG datasets. The proposed model is trained on 12-lead ECG data from over 10,000 patients. We have compared the proposed model with several state-of-the-art (SOTA) models, such as LeNet-5, AlexNet, VGG-16, ResNet-50, Inception, and LSTM, on the same training and test datasets. To show the effectiveness of the computational efficiency of the proposed model, we have only trained over 20 epochs without GPU support and we achieved an accuracy of 50.87% on the test dataset for 27 categories of heart abnormalities. We found that our proposed model outperformed the previous studies which participated in the official PhysioNet/CinC Challenge 2020 and achieved fourth place as compared with the 41 official ranking teams. The result of this study indicates that the proposed model is an implying new method for predicting heart diseases using 12-lead ECGs.  
cardiac abnormality detection; 12-lead ECG; deep learning; large dataset +
Footnote †: journal: _arxiv.org_  
employed to classify heart diseases based on raw ECG data, reflecting the growing interest in the automated detection of abnormal behavior in ECG signals. Recently, DL has emerged as a valuable tool in biomedical and healthcare systems by learning intricate patterns and features from signal data [11; 12; 13]. In DL, various deep neural networks such as recurrent neural networks (RNNs) [14], deep residual networks (ResNets) [15], transformers, and attention networks have been developed to classify the diseases. ResNet is one of the popular networks and it handles the complexity of large datasets efficiently with its unique deep layer-based architecture.  
Despite of the many advancements in the field of machine learning (ML) and DL for ECG signal analysis, the existing solutions are limited in their ability to accurately detect cardiac abnormalities, especially in many categories. These limitations highlight the need for more robust and generalized DL models that can effectively handle large and diverse datasets. The challenge lies in developing models that not only improve detection accuracy but also enhance computational efficiency and generalizability. In order to address the shortcomings of other existing methods, this work presents the HRIDM (Hybrid Residual/Inception-based Deeper Model), a unique deep learning model. This work aims to enhance the detection efficiency of heart disease using a large 12-lead ECG dataset through DL neural networks. Specifically, this study aims to:  
* Develop a novel deep learning model for ECG abnormality detection, which could outperform the prior state-of-the-art (SOTA) models, considerably enhancing performance;
* Utilize the annotated dataset provided by the PhysioNet/CinC Challenge 2020 to accurately classify the 27 different cardiac abnormalities, demonstrating the model's capability to handle complex and large-scale datasets effectively;
* Train the proposed model on a large 12-lead ECG dataset from over 10,000 patients, to improve its generalizability and robustness;
* Illustrate the computational efficiency of the proposed model by achieving high accuracy with limited resources, such as minimal training epochs and no GPU support;
* Benchmark the HRIDM against several SOTA models, including Inception, LeNet-5, AlexNet, VGG-16, ResNet-50, and LSTM, to validate its performance.