발간년도 : [2022]
| 논문정보 |
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| 논문명(한글) |
[Vol.17, No.1] A Study of ECG Classification Algorithm Using Deep Learning |
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| 논문투고자 |
Se Yul Lee |
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| 논문내용 |
The electrocardiogram(ECG) is a algorithm of recording the change in potential value for the activity of the heart by time and has been used as a testbed algorithm to analyze the normal/abnormal state of the heart. The ECG analysis algorithm uses a frequency band filter but recently deep learning and machine learning using artificial intelligence technology. In this paper, we used of ECG data, a band filter between 0.67Hz and 50Hz was used by fast Fourier Transform and the ECG data for four seconds were reconstructed into data for three seconds before and one second after the beat to be classified as one. As the learning algorithm of artificial intelligence, deep learning, convolution neural network operation was used. To prevent overfitting of the learning model, we developed a model that classifies ECG by repeating dropout. for training data, 70% were trained using 44 data set excluding pacemaker among 48 record set of MIT-BIH arrhythmia data set. As a result of the testbed, the average accuracy of the convolution neural network model was 99.8%, whereas the average accuracy of all classification was 99.6% as a result of the evaluation data, indication the it showed similar compared to the performance of the conventional ECG classification. In this paper, it is a classification for heart beat. but it is possible to contribute to the study on classification of atrial fibrillation as classification of ECG rhythms by learning models. |
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