Automatic Identification and Classification Diagnosis of Atrial Ventricular Hypertrophy Electrocardiogram Based on Convolutional Neural Network.
10.3969/j.issn.1671-7104.2020.01.004
- Author:
Yanni TONG
1
;
Ruiqing ZHANG
1
;
Yang SHEN
1
;
Hua JIANG
2
;
Shijie CHANG
1
;
Xianzheng SHA
1
Author Information
1. Department of Biomedical Engineering, China Medical University, Shenyang, 110122. ##Email#.
2. Department of Cardiovascular, the First Affiliated Hospital of China Medical University, Shenyang, 110054. ##Email#.
- Publication Type:Journal Article
- Keywords:
CNN;
atrial ventricular hypertrophy;
auxiliary diagnosis
- MeSH:
China;
Electrocardiography;
Heart Atria/pathology*;
Humans;
Hypertrophy;
Neural Networks, Computer
- From:
Chinese Journal of Medical Instrumentation
2020;44(1):20-23
- CountryChina
- Language:Chinese
-
Abstract:
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.
