TY - GEN
T1 - A Signal Quality Assessment Method for Electrocardiography Acquired by Mobile Device
AU - Zhang, Junjie
AU - Wang, Liping
AU - Zhang, Wenjie
AU - Yao, Junjie
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/21
Y1 - 2019/1/21
N2 - Electrocardiography (ECG) is a significant tool for detecting cardiovascular diseases. The remote ECG monitoring system by mobile device can gather data anywhere, at any time, which broaden the scope of diagnosis service. However, in clinical, the crucial obstacle involved in the remote system is to identify whether the ECG collected by inexperienced person is usable for diagnostic interpretation. In this study, we address the quality assessment problem of clinical ECG and provide an effective 7-layer Long Short-Term Memory neural network, named LSTM-ECG. According to medical knowledge, we devise a comprehensive feature set which covers the spectral distribution, signal complexity, horizontal and vertical variation of waves, and so on. Meanwhile, we design two LSTM layers in LSTM-ECG to automatically learn the related features. A merge layer is utilized to accomplish feature fusion between domain feature set and LSTM layer feature set and a dropout layer is introduced to prevent overfitting. In order to test the effectiveness of LSTM-ECG, four classifiers are implemented for contrast. Two datasets include large scale clinical data are used in experiments. Comprehensive experiments show that LSTM-ECG is better than the prior state-of-art method and effective in clinical data.
AB - Electrocardiography (ECG) is a significant tool for detecting cardiovascular diseases. The remote ECG monitoring system by mobile device can gather data anywhere, at any time, which broaden the scope of diagnosis service. However, in clinical, the crucial obstacle involved in the remote system is to identify whether the ECG collected by inexperienced person is usable for diagnostic interpretation. In this study, we address the quality assessment problem of clinical ECG and provide an effective 7-layer Long Short-Term Memory neural network, named LSTM-ECG. According to medical knowledge, we devise a comprehensive feature set which covers the spectral distribution, signal complexity, horizontal and vertical variation of waves, and so on. Meanwhile, we design two LSTM layers in LSTM-ECG to automatically learn the related features. A merge layer is utilized to accomplish feature fusion between domain feature set and LSTM layer feature set and a dropout layer is introduced to prevent overfitting. In order to test the effectiveness of LSTM-ECG, four classifiers are implemented for contrast. Two datasets include large scale clinical data are used in experiments. Comprehensive experiments show that LSTM-ECG is better than the prior state-of-art method and effective in clinical data.
KW - Deep learning method
KW - Electrocardiogram
KW - Feature fusion
KW - LSTM
KW - Quality assessment
UR - https://www.scopus.com/pages/publications/85062511027
U2 - 10.1109/BIBM.2018.8621160
DO - 10.1109/BIBM.2018.8621160
M3 - 会议稿件
AN - SCOPUS:85062511027
T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
SP - 2826
EP - 2828
BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
A2 - Schmidt, Harald
A2 - Griol, David
A2 - Wang, Haiying
A2 - Baumbach, Jan
A2 - Zheng, Huiru
A2 - Callejas, Zoraida
A2 - Hu, Xiaohua
A2 - Dickerson, Julie
A2 - Zhang, Le
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Y2 - 3 December 2018 through 6 December 2018
ER -
