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11 September 2024 Development of a signal-features-based nomogram model for distinguish abnormal electrocardiogram signals
Yuhan Zhou, Zhengguang Xiao, Xinran Zhang, Qikai Ji, Yang Liu, Yutong Xie, Qi Sun, Yuhao Jin, Miao Yu, Linrong Yuan, He Ren, Liang Zhou, Jiahao He, Ping Li
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Proceedings Volume 13270, International Conference on Future of Medicine and Biological Information Engineering (MBIE 2024); 132700U (2024) https://doi.org/10.1117/12.3048450
Event: 2024 International Conference on Future of Medicine and Biological Information Engineering (MBIE 2024), 2024, Shenyang, China
Abstract
Background: Cardiovascular disease is one of the leading causes of death worldwide. Electrocardiogram(ECG) signals play a crucial role in diagnosing various heart conditions, including arrhythmias and myocardial infarctions. There is a need for a reliable and efficient method to quickly identify abnormal heartbeats to aid early diagnosis and treatment. Methods: The study utilized the MIT-BIH arrhythmia database, which includes 48 groups of two-lead ECG signals. High-dimensional features were extracted from the ECG signals using the ts fresh package in Python. Feature selection was performed using variance analysis, Spearman correlation, mRMR, and LASSO methods. Logistic regression models were then constructed to predict abnormal heartbeats. Results: The final model included 10 key features and demonstrated high diagnostic performance. The AUC was 0.958in the training set and 0.947 in the test set, with specificities of 0.930 and 0.851, and sensitivities of 0.881 and0.892, respectively. The model outperformed traditional methods and deep learning models such as CNN and VGG in identifying abnormal beats. Conclusions: This study presents a robust and effective nomogram model for distinguishing abnormal ECG signals, highlighting its significant clinical application potential. Future research will focus on expanding sample sizes and incorporating additional methods for feature calculation to further enhance model generalizability
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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Yuhan Zhou, Zhengguang Xiao, Xinran Zhang, Qikai Ji, Yang Liu, Yutong Xie, Qi Sun, Yuhao Jin, Miao Yu, Linrong Yuan, He Ren, Liang Zhou, Jiahao He, and Ping Li "Development of a signal-features-based nomogram model for distinguish abnormal electrocardiogram signals", Proc. SPIE 13270, International Conference on Future of Medicine and Biological Information Engineering (MBIE 2024), 132700U (11 September 2024); https://doi.org/10.1117/12.3048450
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KEYWORDS
Education and training
Electrocardiography
Data modeling
Performance modeling
Feature extraction
Reflection
Heart
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