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For people with severe heart disease, real-time ECG monitoring is an important means of detecting arrhythmia. The traditional ECG signal diagnosis method is for doctors to provide accurate judgment results by observing the electrocardiogram and combining theory and clinical practice. For some patients with severe cardiovascular diseases, real-time ECG monitoring is an important means of detecting arrhythmias, which requires correct judgment of arrhythmias in a short period of time. However, at present, the judgment of electrocardiogram is mainly carried out manually, and there are problems of real-time judgment and low efficiency. Therefore, in response to the above problems, the application of machine learning algorithms in the identification of arrhythmia symptoms and risk levels is proposed. First, feature extraction was performed on the power spectral density of each data, and five criteria were extracted: low-frequency power (LF), high-frequency power (HF), the ratio of low-frequency to high-frequency power (LF/HF), average value, and standard deviation., using the random forest model to classify the electrocardiogram of normal heartbeat and arrhythmia. The second point is to classify arrhythmias, use the obtained multiple feature values to determine the optimal number of clusters for the arrhythmia data using the elbow rule, and then perform cluster analysis with the K-Means algorithm to match possible situations. Statistics of all categories are made, and models with good classification effects are selected through cluster summary diagrams, data set cluster annotations, cluster scatter plots, silhouette coefficients, DBI, CH and other criteria. The physiological significance of its classification criteria is analyzed, and the judgment criteria for each arrhythmia type are given. Finally, the risk level of each arrhythmia condition is analyzed, and the given data are sorted according to the level of risk based on the K-Means algorithm to obtain visual and reasonable analysis results for application in clinical monitoring equipment.

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Application of Machine Learning Algorithms in Determining Arrhythmia Symptoms and Risk Levels

  • 会议论文

  • 发表日期：

    2025年

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  • Ziwen Wang,
  • Yujia Wang,
  • Mingjie Zheng

摘要

For people with severe heart disease, real-time ECG monitoring is an important means of detecting arrhythmia. The traditional ECG signal diagnosis method is for doctors to provide accurate judgment results by observing the electrocardiogram and combining theory and clinical practice. For some patients with severe cardiovascular diseases, real-time ECG monitoring is an important means of detecting arrhythmias, which requires correct judgment of arrhythmias in a short period of time. However, at present, the judgment of electrocardiogram is mainly carried out manually, and there are problems of real-time judgment and low efficiency. Therefore, in response to the above problems, the application of machine learning algorithms in the identification of arrhythmia symptoms and risk levels is proposed. First, feature extraction was performed on the power spectral density of each data, and five criteria were extracted: low-frequency power (LF), high-frequency power (HF), the ratio of low-frequency to high-frequency power (LF/HF), average value, and standard deviation., using the random forest model to classify the electrocardiogram of normal heartbeat and arrhythmia. The second point is to classify arrhythmias, use the obtained multiple feature values to determine the optimal number of clusters for the arrhythmia data using the elbow rule, and then perform cluster analysis with the K-Means algorithm to match possible situations. Statistics of all categories are made, and models with good classification effects are selected through cluster summary diagrams, data set cluster annotations, cluster scatter plots, silhouette coefficients, DBI, CH and other criteria. The physiological significance of its classification criteria is analyzed, and the judgment criteria for each arrhythmia type are given. Finally, the risk level of each arrhythmia condition is analyzed, and the given data are sorted according to the level of risk based on the K-Means algorithm to obtain visual and reasonable analysis results for application in clinical monitoring equipment.