은닉 마코프 모델을 이용한 심전도 QRS 검출 최적화에 관한 연구

다국어 초록 (Multilingual Abstract)

The heart is the body"s circulatory organ that supplies blood to the body. An electrocardiogram is the best means of measuring and diagnosing abnormal conduction of the heart muscle. Therefore, to diagnose patients suspected of heart disease, a Holter monitoring system measuring electrocardiography for 24 hours is used by doctors to examine and diagnose patients. However, diagnosing innumerable Holter data entails considerable effort by doctors directly. If QRS can be detected by an automated diagnostic system, many Holter data could be classified without diagnosing it by clinicians directly. In this paper, we tried to detect the QRS of electrocardiogram using the Hidden Markov Model. For objective verification, the onsets and offsets of QRS, which classified by the specialist, in the QT-database, were used as the reference labels. The Mexican Hat mother function was used for the wavelet transform. To study how to optimize the learning of hidden Markov models, the experiment was conducted by changing the batch size of the training data sets and the scale of the wavelet mother function. During the verification process, the mean and standard deviation of the difference between QRS onset and offset obtained from the test data sets through the hidden Markov model and the reference label classified by a specialist were used. As a result, the batch size was found to have the best performance using all 84 training data sets, and the scale of the mother function was found to have the best performance using scale j = 2, 3, 4. When the mean and standard deviation of QRS complexes detected from the hidden Markov model was -8.2822ms, ±5.821476ms(p=0.99818) onsets, respectively and -2.9588ms, ±6.5662ms(p=0.99838) offsets, respectively with 84 batch sizes and all three scale mother functions were trained, the results of onset, offset standard deviation were improved average about 22.1%, 30.9% respectively when compared with other algorithms using QT-Database

목차 (Table of Contents)

• 1. 서론
• Abstract
• 2. 이론적 배경
• 3. 제안 이론 기술
• 4. 실험 및 결과
• 5. 결론
• References

참고문헌 (Reference)

1 김정홍, "심전도 신호에서 QRS군의 단계적 검출" 한국통신학회 41 (41): 244-253, 2016

2 김민규, "상태 공간 모델에 의한 심전도 합성" 명지대학교 대학원 2013

3 O. Rioul, "Wavelets and Signal Processing" 14-38, 1991

4 E. Jacobsen, "The sliding DFT" 20 (20): 74-80, 2003

5 P. Laguna, "The QT Database"

6 N. Goldschlager, "Principles of Clinical Electrocardiography" Appleton and Lange 1989

7 Christopher M. Bishop, "Pattern Recognition and Machine Learning, Information Science and Statistics" 2006

8 J. Dumont, "Parameter optimization of a wavelet-based electrocardiogram delineator with an evolutionary algorithm" 707-710, 2005

9 John G. Webster, "Medical Instrumentation: Application and Design" John Wiley & Sons 1998

10 A. Dallali, "Fuzzy c-means clustering, Neural Network, wt, and Hrv for classification of cardiac arrhythmia" 6 (6): 112-118, 2011

11 Abibullaev Berdakh, "Epileptic seizure detection using continuous wavelet transforms and artificial neural networks" 영남대학교 대학원 2010

12 R. V. Andreao, "ECG signal analysis through hidden Markov models" 53 : 1541-1549, 2006

13 Weng Chi Chan, "ECG parameter extractor for the intelligent home healthcare embedded system" 100-113, 2005

14 S. H. Jambukia, "Classification of ECG signals using machine learning techniques : A survey" 714-721, 2015

15 David E. Mohrman, "Cardiovascular physiology" McGraw-Hill 2010

16 M. T. Johnson, "Capacity and complexity of HMM duration modeling techniques" 12 (12): 407-404, 2005

17 Katzung, Bertram G., "Basic and Clinical Pharmacology" McGraw-Hill 2009

18 P. Laguna, "Automatic detection of wave boundaries in multilead ECG signals : Validation with theCSE database" 27 (27): 45-60, 1994

19 A. I. Manriquez, "An algorithm for QRS onset and offset detection in single lead electrocardiogram records" 541-544, 2007

20 J. P. Martinez, "A wavelet-based ECG delineator : Evaluation on standard database" 51 (51): 570-581, 2004

21 Moavenian, "A qualitative comparison of artificial neural networks and support vector machines in ECG arrhythmias classification" 37 (37): 3088-3093, 2010

22 C. Torrence, "A practical guide to wavelet analysis" 79 (79): 61-78, 1998

23 Y. Ozbay, "A fuzzy clustering neural network architecture for classification of ECG arrhythmias" 36 (36): 376-388, 2006

24 S. Mallat, "A Wavelet Tour of Signal Processing" Academic Press 1998

25 Braunwald E., "A Textbook of Cardiovascular Medicine" W. B. Saunders Co. 1997