# Research on ECG Signal Classification Based on Hybrid Residual Network
## 4 Conclusions  
This paper proposes a Hybrid Residual Network (Hybrid ResNet) that integrates standard convolution, depthwise separable convolution, and residual connections for the categorization of ECG signals. The significance of key modules, including data preprocessing, residual connections, and depthwise separable convolution, in improving model performance was systematically evaluated through ablation experiments and comparisons with state-of-the-art methods. The experimental results showed that the preprocessing methods employed in this paper effectively eliminated baseline drift and high-frequency noise from the ECG signals, hence yielding cleaner input data for the model. Residual connections augmented the training stability of the deep network and improved its feature extraction capabilities. Depthwise separable convolution markedly lowered the computing complexity while preserving efficient feature extraction. The model achieved a classification accuracy of 99.09% on the MIT-BIH Arrhythmia Database, surpassing traditional models and other state-of-the-art methods, while also exhibiting remarkable computing economy.  
This work has several limitations that warrant further attention. Firstly, while the current model achieves high accuracy, it may not fully exploit the potential of advanced preprocessing techniques. Future work should explore more sophisticated preprocessing methods to enhance signal quality and computational efficiency, ensuring the model's robustness in diverse clinical scenarios. Additionally, subsequent endeavors will focus on refining the model's complexity to make it more suitable for low-power devices, thereby broadening the applicability of ECG monitoring technologies in resource-constrained environments. Furthermore, we intend to investigate alternative deep learning architectures to better handle the intricate characteristics of ECG signals. These improvements aim to provide more reliable technical support for the early diagnosis and prevention of cardiovascular diseases.  
Conceptualization, T.Q. and H.Z. ([PERSON]); methodology, T.Q.; software, T.Q.; validation, T.Q., H.Z. ([PERSON]) and H.Z. ([PERSON]).; formal analysis, T.Q.; investigation, T.Q.; data curation, T.Q.; writing--original draft preparation, T.Q.; writing--review and editing, T.Q. and X.L.; visualization, T.Q.; supervision, H.Z. ([PERSON]) and X.L.; project administration, C.S.; funding acquisition, C.S. All authors have read and agreed to the published version of the manuscript. This work was supported, in part, by the Innovative Research Group Project of the National Natural Science Foundation of China (51821003), the Excellent Youth foundation of Shanxi Province (202103021222011), the Key research and development project of Shanxi Province (202202020101002), the fundamental research program of Shanxi Province (202303021211150), the Aviation Science Foundation (20222020U0002), the Shanxi province key laboratory of quantum sensing and precision measurement (201905D121001), the 173 Foundation (2021 XXXX0668), ZBZJDJCJKT (51405 XX01), the Science Foundation of China (202400080U0001), and the Open foundation for State Key Laboratory of Dynamic Measurement Technology.  
Not applicable.  
The original data presented in the study are openly available at [[https://physionet.org/content/mitdb/1.0.0/](https://physionet.org/content/mitdb/1.0.0/)]([https://physionet.org/content/mitdb/1.0.0/](https://physionet.org/content/mitdb/1.0.0/)) (accessed on 20 May 2024).  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.