KEYWORDS: Electrocardiography, Education and training, Performance modeling, Data modeling, Feature extraction, Reflection, Heart, Signal processing, Signal detection, Diagnostics
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
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