J Korean Med Sci. 2019 Feb 15;34(7):e64. doi: 10.3346/jkms.2019.34.e64. eCollection 2019 Feb 25.
Automatic Prediction of Atrial Fibrillation Based on Convolutional Neural Network Using a Short-term Normal Electrocardiogram Signal.
Erdenebayar U1, Kim H2, Park JU1, Kang D1,3, Lee KJ1.
Author information
1Department of Biomedical Engineering, Yonsei University College of Health Science, Wonju, Korea.2CHANGEUI TECH Co., Ltd., Wonju, Korea.3MEDIANA Co., Ltd., Wonju, Korea.
Abstract
BACKGROUND:
In this study, we propose a method for automatically predicting atrial fibrillation (AF) based on convolutional neural network (CNN) using a short-term normal electrocardiogram (ECG) signal.
METHODS:
We designed a CNN model and optimized it by dropout and normalization. One-dimensional convolution, max-pooling, and fully-connected multiple perceptron were used to analyze the short-term normal ECG. The ECG signal was preprocessed and segmented to train and evaluate the proposed CNN model. The training and test sets consisted of the two AF and one normal dataset from the MIT-BIH database.
RESULTS:
The proposed CNN model for the automatic prediction of AF achieved a high performance with a sensitivity of 98.6%, a specificity of 98.7%, and an accuracy of 98.7%.
CONCLUSION:
The results show the possibility of automatically predicting AF based on the CNN model using a short-term normal ECG signal. The proposed CNN model for the automatic prediction of AF can be a helpful tool for the early diagnosis of AF in healthcare fields.
KEYWORDS:
Atrial Fibrillation; Convolutional Neural Network; Deep Learning; Electrocardiogram
PMID: 30804732 PMCID: PMC6384436 DOI: 10.3346/jkms.2019.34.e64
오랜만에 보는 국내 논문..
의대에서 쓴 논문은 아닌듯.
메디아나. 의료기 회사.
심전도 사진. CNN. abnormal ECG detection
흔한 주제. 몇 년전 초창기 유행하던 주제..
이제서야 이런 논문을 내면, 너무 늦은 듯...
