Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network
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Acharya, 2016, Automated characterization of arrhythmias using nonlinear features from tachycardia ECG beats
Acharya, 2007
Bawany, 2013, This is the economic impact of an aging Singaporean workforce, Singapore Bus. Rev.
Berry, 2004, Bradycardia and tachycardia occurring in older people: an introduction, Brit. J. Cardiol., 11
J. Bouvrie. Notes on convolutional neural network, 2007.
Chow, 2012, Epidemiology of arrhythmias and conduction disorders in older adults, Clinics Geriatric Med., 28, 539, 10.1016/j.cger.2012.07.003
Ciresan, 2011, Convolutional neural network committees for handwritten character classification, 1135
Desai, 2016, Diagnosis of multiclass tachycardia beats using recurrence quantification analysis and ensemble classifiers, J. Mech. Med. Biol., 16, 10.1142/S0219519416400054
Duda, 2001
Fahim, 2011, Diagnosis of cardiovascular abnormalities from compressed ECG: a datamining-based approach, IEEE Trans. Inf. Technol. Biomed., 15, 33, 10.1109/TITB.2010.2094197
Fukushima, 1980, Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biol. Cybern., 36, 193, 10.1007/BF00344251
Glorot, 2010, Understanding the difficulty of training deep feedforward neural networks, Aistats
Goldberger, 2012
Goldberger, 2000, PhysioBank, PhysioToolkit, and PhyisoNet: components of a new research resource for complex physiologic signals, Circulation, 101, e215, 10.1161/01.CIR.101.23.e215
Goodfellow, 2016
Golkov, 2016, q-Space deep learning: twelve-fold shorter and model-free diffusion MRI scans, IEEE Trans. Med. Imaging, 35, 1344, 10.1109/TMI.2016.2551324
van Grinsven, 2016, Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images, IEEE Trans. Med. Imaging, 35, 1273, 10.1109/TMI.2016.2526689
Hatipoglu, 2017, Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships, Med. Biol. Eng. Comput., 1
K. He, X. Zhang, S. Ren, J. Sun. Delving deep into rectifiers: surpassing human-level performance on image net classification, 1026–1034, 2015.
Keras Documentation. About Keras models. https://keras.io/models/about-keras-models/. (Last accessed: 09 March 2017).
Kiranyaz, 2016, Real-time patient-specific ECG classification by 1-D convolutional neural network, IEEE Trans. Biomed. Eng., 63, 664, 10.1109/TBME.2015.2468589
Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst., 12, 1097
LeCun, 1998, Gradient-based learning applied to document recognition, Proc. IEEE, 86, 2278, 10.1109/5.726791
LeCun, 1998, Convolutional networks for images, speech, and time-series
Martis, 2014, Current methods in electrocardiogram characterization, Comput. Biol. Med., 48, 133, 10.1016/j.compbiomed.2014.02.012
Martis, 2014, Computer-aided diagnosis of atrial arrhythmia using dimensionality reduction methods on transform domain representation, Biomed. Signal Process. Control, 13, 295, 10.1016/j.bspc.2014.04.001
Martis, 2013, Application of higher order statistics for atrial arrhythmia classification, Biomed. Signal Process. Control, 8, 888, 10.1016/j.bspc.2013.08.008
National Institute on Aging – turning discovery into health. Global health and aging. Assess. Costs Aging Health Care. https://www.nia.nih.gov/research/publication/global-health-and-aging/assessing-costs-aging-and-health-care. (Last accessed: 24 February 2017).
Najarian, 2012
Oquab, 2015, Is object localization for free? - weakly-supervised learning with convolutional neural networks, 685
Singh, 2006, Optimal selection of wavelet basis function applied to ECG signal denoising, Digital Signal Process., 16, 275, 10.1016/j.dsp.2005.12.003
Sirinukunwattana, 2016, Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images, IEEE Trans. Med. Imaging, 35, 1196, 10.1109/TMI.2016.2525803
United Nations. Department of economic and social affairs population division. World population aging 2015. New York, 2015.
Wang, 2001, A short-time multifractal approach for arrhythmia detection based on fuzzy neural network, IEEE Trans. Biomed. Eng., 48, 989, 10.1109/10.942588
Yan, 2016, Multi-instance deep learning: discover discriminative local anatomies for bodypart recognition, IEEE Trans. Med. Imag., 35, 1332, 10.1109/TMI.2016.2524985
Zubair, 2016, An automated ECG beat classification system using convolutional neural networks