- Volume 41 Issue 2
DOI QR Code
Automatic Parameter Acquisition of 12 leads ECG Using Continuous Data Processing Deep Neural Network
연속적 데이터 처리 심층신경망을 이용한 12 lead 심전도 파라미터의 자동 획득
- Kim, Ji Woon (Department of Mechanical & Biomedical Engineering, Kangwon National University) ;
- Park, Sung Min (Department of Thoracic & Cardiovascular Surgery, School of Medicine, Kangwon National University) ;
- Choi, Seong Wook (Department of Mechanical & Biomedical Engineering, Kangwon National University)
- Received : 2020.02.03
- Accepted : 2020.04.14
- Published : 2020.04.30
The deep neural networks (DNN) that can replicate the behavior of the human expert who recognizes the characteristics of ECG waveform have been developed and studied to analyze ECG. However, although the existing DNNs can not provide the explanations for their decisions, those trials have attempted to determine whether patients have certain diseases or not and those decisions could not be accepted because of the absence of relating theoretical basis. In addition, these DNNs required a lot of training data to obtain sufficient accuracy in spite of the difficulty in the acquisition of relating clinical data. In this study, a small-sized continuous data processing DNN (C-DNN) was suggested to determine the simple characteristics of ECG wave that were not required additional explanations about its decisions and the C-DNN can be easily trained with small training data. Although it can analyze small input data that was selected in narrow region on whole ECG, it can continuously scan all ECG data and find important points such as start and end points of P, QRS and T waves within a short time. The star and end points of ECG waves determined by the C-DNNs were compared with the results performed by human experts to estimate the accuracies of the C-DNNs. The C-DNN has 150 inputs, 51 outputs, two hidden layers and one output layer. To find the start and end points, two C-DNNs were trained through deep learning technology and applied to a parameter acquisition algorithms. 12 lead ECG data measured in four patients and obtained through PhysioNet was processed to make training data by human experts. The accuracy of the C-DNNs were evaluated with extra data that were not used at deep learning by comparing the results between C-DNNs and human experts. The averages of the time differences between the C-DNNs and experts were 0.1 msec and 13.5 msec respectively and those standard deviations were 17.6 msec and 15.7 msec. The final step combining the results of C-DNN through the waveforms of 12 leads was successfully determined all 33 waves without error that the time differences of human experts decision were over 20 msec. The reliable decision of the ECG wave's start and end points benefits the acquisition of accurate ECG parameters such as the wave lengths, amplitudes and intervals of P, QRS and T waves.
Supported by : 한국연구재단
- Kim H, Yazicioglu RF, Merken P, Van Hoof C, Yoo HJ. ECG signal compression and classification algorithm with quad level vector for ECG holter system. IEEE Transactions on Information Technology in Biomedicine. 2009;14(1):93-100. https://doi.org/10.1109/TITB.2009.2031638
- Makikallio TH, Barthel P, Schneider R, Bauer A, TapanCDNNnen JM, Tulppo MP, Huikuri HV. Prediction of sudden cardiac death after acute myocardial infarction: role of Holter monitoring in the modern treatment era. European heart journal. 2005;26(8):762-9. https://doi.org/10.1093/eurheartj/ehi188
- Atarashi H, Ogawa S, Idiopathic Ventricular Fibrillation Investigators. New ECG criteria for high-risk Brugada syndrome. Circulation journal, 2003;67(1):8-10. https://doi.org/10.1253/circj.67.8
- Sathyapriya L, Murali L, Manigandan T. Analysis and detection R-peak detection using Modified Pan-Tompkins algorithm. In 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies. 2014;483-7.
- Ubeyli ED. Recurrent neural networks employing Lyapunov exponents for analysis of ECG signals. Expert systems with applications. 2010;37(2):1192-9. https://doi.org/10.1016/j.eswa.2009.06.022
- Yildirim O, Plawiak P, Tan RS, Acharya UR. Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Computers in biology and medicine. 2018;102:411-20. https://doi.org/10.1016/j.compbiomed.2018.09.009
- Acharya UR, Fujita H, Oh SL, Hagiwara Y, Tan JH, Adam M. Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Information Sciences. 2017;415:190-8.
- Sannino G, De Pietro G. A deep learning approach for ECGbased heartbeat classification for arrhythmia detection. Future Generation Computer Systems. 2018;86:446-55. https://doi.org/10.1016/j.future.2018.03.057
- Goto S, Kimura M, Katsumata Y, Goto S, Kamatani T, Ichihara G, Sano M. Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients. Plos one. 2019;14(1):e0210103. https://doi.org/10.1371/journal.pone.0210103
- Agarap AF. Deep learning using rectified linear units (relu). arXiv preprint arXiv. 2018;1803.08375.
- Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, ..., Stanley HE. PhysioBank, Physio Toolkit, and PhysioNet: components of a new research resource for complex physiologic signals. circulation. 2000;101(23):e215-e220.
- Willems JL, Abreu-Lima C, Arnaud P, van Bemmel JH, Brohet C, Degani R, ... & Machado, H. The diagnostic performance of computer programs for the interpretation of electrocardiograms. New England Journal of Medicine. 1991;325(25):1767-73. https://doi.org/10.1056/NEJM199112193252503