TY - JOUR
T1 - Atrial Fibrillation Beat Identification Using the Combination of Modified Frequency Slice Wavelet Transform and Convolution Neural Networks
AU - Xu, Xiaoyan
AU - Wei, Shoushui
AU - Ma, Caiyun
AU - Luo, Kan
AU - Zhang, Li
AU - Liu, Chengyu
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Atrial fibrillation (AF) is a serious cardiovascular disease with the phenomenon of beating irregularly. It is the major cause of variety of heart diseases, such as myocardial infarction. Automatic AF beat detection is still a challenging task which needs further exploration. A new framework, which combines modified frequency slice wavelet transform (MFSWT) and convolutional neural networks (CNNs), was proposed for automatic AF beat identification. MFSWT was used to transform 1-s electrocardiogram (ECG) segments to time-frequency images, then the images were fed into a 12-layer CNN for feature extraction and AF/non-AF beat classification. The results on the MIT-BIH Atrial Fibrillation database showed that a mean accuracy (Acc) of 81.07% from 5-fold cross validation is achieved for the test data. The corresponding sensitivity (Se), specificity (Sp) and the area under ROC curve (AUC) results are 74.96%, 86.41% and 0.88. When excluding an extreme poor signal quality ECG recording in the test data, a mean Acc of 84.85% is achieved, with the corresponding Se, Sp and AUC values of 79.05%, 89.99% and 0.92. This study indicates that it is possible to accurately identify AF or non-AF ECGs from a short-term signal episode.
AB - Atrial fibrillation (AF) is a serious cardiovascular disease with the phenomenon of beating irregularly. It is the major cause of variety of heart diseases, such as myocardial infarction. Automatic AF beat detection is still a challenging task which needs further exploration. A new framework, which combines modified frequency slice wavelet transform (MFSWT) and convolutional neural networks (CNNs), was proposed for automatic AF beat identification. MFSWT was used to transform 1-s electrocardiogram (ECG) segments to time-frequency images, then the images were fed into a 12-layer CNN for feature extraction and AF/non-AF beat classification. The results on the MIT-BIH Atrial Fibrillation database showed that a mean accuracy (Acc) of 81.07% from 5-fold cross validation is achieved for the test data. The corresponding sensitivity (Se), specificity (Sp) and the area under ROC curve (AUC) results are 74.96%, 86.41% and 0.88. When excluding an extreme poor signal quality ECG recording in the test data, a mean Acc of 84.85% is achieved, with the corresponding Se, Sp and AUC values of 79.05%, 89.99% and 0.92. This study indicates that it is possible to accurately identify AF or non-AF ECGs from a short-term signal episode.
KW - Atrial fibrillation
KW - Electrocardiogram
KW - Convolutional neural networks
KW - Modified frequency slice wavelet transform
KW - Time-frequency analysis
U2 - 10.1155/2018/2102918
DO - 10.1155/2018/2102918
M3 - Article
SN - 2040-2295
VL - 2018
JO - Journal of Healthcare Engineering
JF - Journal of Healthcare Engineering
M1 - 2102918
ER -