• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid

References

1 
K.-H. Park, J.-S. Kim, C. Ryu, B.-J. Choi, J. Kim, 8 2013, Unusual Waveform Detection Algorithm in Arrhythmia ECG Signal, Journal of Korean Institute of Intelligent Systems, Vol. 23, No. 4, pp. 292-297DOI
2 
Hyoung J. Jang, J. S. Lim, 2009, Detection of Arrhythmia Using Heart Rate Variability and A Fuzzy Neural Network, Journal of Internet Computing and Services, Vol. 10, No. 5, pp. 107-116Google Search
3 
Y. S. Choi, 6 2016, Artificial Intelligence: Will It Replace Human Medical Doctors?, Medical Education Forum, Vol. 18, No. 2, pp. 47-50DOI
4 
Detection of Atrial Fibrillation Using Artificial Neural NetworkGoogle Search
5 
P. Rajpurkar, A. Y. Hannun, M. Haghpanahi, C. Bourn, A. Y. Ng, 7 2017, Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks, arXiv:1707.01836 [cs], Accessed : 6 12, 2019. [Online]. Available at: http://arxiv.org/abs/1707.01836.Google Search
6 
J. S. Sahambi, Using Wavelet Transforms for ECG Charac- terization, pp. 7DOI
7 
Cuiwei Li, Chongxun Zheng, Changfeng Tai, 1 1995, Detection of ECG characteristic points using wavelet transforms, IEEE Trans. Biomed. Eng., Vol. 42, No. 1, pp. 21-28DOI
8 
S. Z. Mahmoodabadi, A. Ahmadian, M. D. Abolhasani, 2005, ECG FEATURE EXTRACTION USING DAUBECHIES WAVELETS, pp. 7-9Google Search
9 
H.-J. Park, 10 2014, A assessment of multiscale-based peak detec- tion algorithm using MIT/BIH Arrhythmia Database, The Transactions of The Korean Institute of Electrical Engineers, Vol. 63, No. 10, pp. 1441-1447DOI
10 
G. B. Moody, R. G. Mark, 6 2001, The impact of the MIT-BIH Arrhythmia Database, IEEE Eng. Med. Biol. Mag., Vol. 20, No. 3, pp. 45-50DOI
11 
F. Scholkmann, J. Boss, M. Wolf, 11 2012, An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals, Algorithms, Vol. 5, No. 4, pp. 588-603DOI
12 
, Aizerman Mark A., Braverman Emmanuel M., Rozonoer Lev I, 1964, Theoretical foundations of the potential function method in pattern recognition learning, Automation and Remote Control, pp. 25: 821-837Google Search
13 
B. E. Boser, I. M. Guyon, V. N. Vapnik, 1992, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory-COLT ’92. ISBN 089791497X, Vol. , No. , pp. 144DOI
14 
C. Cortes, V. Vapnik, 1995, Support-vector networks, Machine Learning, Vol. 20, No. 3, pp. 273DOI
15 
T. Schaul, S. Zhang, Y. LeCun, 2013, No More Pesky Lear- ning Rates, pp. 9Google Search
16 
Yann A. LeCun, 2012, Efficient backprop, Neural networks: Tricks of the trade, Springer Berlin Heidelberg, Vol. , No. , pp. 9-48DOI
17 
David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams, October 1986, Learning representations by back-propagating errors. Bibcode:1986Natur.323..533R. S2CID 205001834., Nature, Vol. 323, No. 6088, pp. 533-536, 8DOI
18 
John Duchi, Elad Hazan, Yoram Singer, 2011, Adaptive sub- gradient methods for online learning and stochastic optimi- zation, (PDF). JMLR., Vol. 12, pp. 2121-2159Google Search
19 
Geoffrey Hinton, Retrieved 19 March 2020, Lecture 6e rmsprop: Divide the gradient by a running average of its recent magnitud, (PDF), pp. 26Google Search
20 
Kingma Diederik, Jimmy Ba, 2014, Adam: A method for stocha- stic optimization, arXiv:1412.6980 [cs.LG]Google Search
21 
N. V. Thakor, 12 1984, From Holter Monitors to Automatic Defi- brillators: Developments in Ambulatory Arrhythmia Moni- toring, IEEE Trans. Biomed. Eng., vol., Vol. BME-31, No. 12, pp. 770-778DOI
22 
D. Ricciardi, 9 2016, Impact of the high-frequency cutoff of bandpass filtering on ECG quality and clinical interpretation: A comparison between 40Hz and 150Hz cutoff in a surgical preoperative adult outpatient population, Journal of Electrocardiology, Vol. 49, No. 5, pp. 691-695DOI