지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
이 논문의 연구 히스토리 (4)
2022
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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.
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참고문헌 (12)
참고문헌 신청
Kim H / 2009 / ECG signal compression and classification algorithm with quad level vector for ECG holter system / IEEE Transactions on Information Technology in Biomedicine 14(1):93~100
Mäkikallio TH / 2005 / Prediction of sudden cardiac death after acute myocardial infarction : role of Holter monitoring in the modern treatment era / European heart journal 26(8):762~769
Atarashi H / 2003 / Idiopathic Ventricular Fibrillation Investigators. New ECG criteria for high-risk Brugada syndrome / Circulation journal 67(1):8~10
Sathyapriya L / 2014 / Analysis and detection R-peak detection using Modified Pan-Tompkins algorithm / 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies :483~487
Übeyli ED / 2010 / Recurrent neural networks employing Lyapunov exponents for analysis of ECG signals / Expert systems with applications 37(2):1192~1199
Mäkikallio TH / 2005 / Prediction of sudden cardiac death after acute myocardial infarction : role of Holter monitoring in the modern treatment era / European heart journal 26(8):762~769
Atarashi H / 2003 / Idiopathic Ventricular Fibrillation Investigators. New ECG criteria for high-risk Brugada syndrome / Circulation journal 67(1):8~10
Sathyapriya L / 2014 / Analysis and detection R-peak detection using Modified Pan-Tompkins algorithm / 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies :483~487
Übeyli ED / 2010 / Recurrent neural networks employing Lyapunov exponents for analysis of ECG signals / Expert systems with applications 37(2):1192~1199
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