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[Automatic Identification and Classification Diagnosis of Atrial Ventricular Hypertrophy Electrocardiogram Based on
Yanni Tong1, Ruiqing Zhang1, Yang Shen1
1Department of Biomedical Engineering, China Medical University, Shenyang, 110122. ##Email#.
Objective:
Identifying Atrial Ventricular Hypertrophy Electrocardiogram (AVH ECG)and diagnosing the classification of theirs automatically.
Methods:
The ECG data used in this experiment was collected from the First Affiliated Hospital of China Medical University. CNN are combined with conventional methods and a 10 layers of one dimensional CNN are created in this experiment to extract the features of ECG signals automatically and achieve the function of classifying. ROC, sensitivity and F1-score are used here to evaluate the effects of the model.
Results:
In the experiment of identifying AVH ECG, the AUC of test dataset is 0.991, while in the experiment of classifying AVH ECG, the maximal F1-score can reach 0.992.
Conclusions:
The CNN model created in this experiment can achieve the auxiliary diagnosis of AVH ECG.
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