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Mazomenos, D. Biswas, A. Acharyya, T. Chen, K. Maharatna, J. Rosengarten, J. Morgan, and N. Curzen, \u201cA low-complexity ECG feature extraction algorithm for mobile healthcare applications,\u201d IEEE J. Biomed. Health Inform., vol.17, no.2, pp.459-469, March 2013. DOI: 10.1109\/TITB.2012.2231312. 10.1109\/TITB.2012.2231312","DOI":"10.1109\/TITB.2012.2231312"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] P. Li, C. Liu, X. Wang, D. Zheng, Y. Li, and C. Liu, \u201cA low-complexity data-adaptive approach for premature ventricular contraction recognition,\u201d Signal, Image, Video Process., vol.8, no.1, pp.111-120, Jan. 2014. DOI: 10.1007\/s11760-013-0478-6. 10.1007\/s11760-013-0478-6","DOI":"10.1007\/s11760-013-0478-6"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] R. Allami, A. Stranieri, V. Balasubramanian, and H.F. Jelinek, \u201cA count data model for heart rate variability forecasting and premature ventricular contraction detection,\u201d Signal, Image, Video Process., vol.11, no.8, pp.1427-1435, Nov. 2017. DOI: 10.1007\/s11760-017-1103-x. 10.1007\/s11760-017-1103-x","DOI":"10.1007\/s11760-017-1103-x"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] K. Minami, H. Nakajima, and T. Toyoshima, \u201cReal-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network,\u201d IEEE Trans. Biomed. Eng., vol.46, no.2, pp.179-185, Feb. 1999. DOI: 10.1109\/10.740880. 10.1109\/10.740880","DOI":"10.1109\/10.740880"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] S. Chen, W. Hua, Z. Li, J. Li, and X. Gao, \u201cHeartbeat classification using projected and dynamic features of ECG signal,\u201d Biomed. Signal Process. Control, vol.31, pp.165-173, Jan. 2017. DOI: 10.1016\/j.bspc.2016.07.010. 10.1016\/j.bspc.2016.07.010","DOI":"10.1016\/j.bspc.2016.07.010"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] T. Ince, S. Kiranyaz, and M. Gabbouj, \u201cA generic and robust system for automated patient-specific classification of ECG signals,\u201d IEEE Trans. Biomed. Eng., vol.56, no.5, pp.1415-1426, May 2009. DOI: 10.1109\/TBME.2009.2013934. 10.1109\/TBME.2009.2013934","DOI":"10.1109\/TBME.2009.2013934"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] M. Llamedo and J.P. Martinez, \u201cHeartbeat classification using feature selection driven by database generalization criteria,\u201d IEEE Trans. Biomed. Eng., vol.58, no.3, pp.616-625, March 2011. DOI: 10.1109\/TBME.2010.2068048. 10.1109\/TBME.2010.2068048","DOI":"10.1109\/TBME.2010.2068048"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] K.D. Manab and A. Samit, \u201cPatient-specific ECG beat classification technique,\u201d Healthcare Technol. Lett., vol.1, no.3, pp.98-103, Spet. 2014. DOI: 10.1049\/htl.2014.0072. 10.1049\/htl.2014.0072","DOI":"10.1049\/htl.2014.0072"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] H. Sharma and K.K. Sharma, \u201cAn algorithm for sleep apnea detection from single-lead ECG using Hermite basis functions,\u201d Comput. Biol. Med., vol.77, no.1, pp.116-124, Oct. 2016. DOI: 10.1016\/j.compbiomed.2016.08.012. 10.1016\/j.compbiomed.2016.08.012","DOI":"10.1016\/j.compbiomed.2016.08.012"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] C. Song, K. Liu, X. Zhang, L. Chen, and X. Xian, \u201cAn obstructive sleep apnea detection approach using a discriminative hidden Markov model from ECG signals,\u201d IEEE Trans. Biomed. Eng., vol.63, no.7, pp.1532-1542, July 2016. DOI: 10.1109\/TBME.2015.2498199. 10.1109\/TBME.2015.2498199","DOI":"10.1109\/TBME.2015.2498199"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] W. Jiang and S.G. Kong, \u201cBlock-based neural networks for personalized ECG signal classification,\u201d IEEE Trans. Neural Netw., vol.18, no.6, pp.1750-1761, Nov. 2007. DOI: 10.1109\/TNN.2007.900239. 10.1109\/TNN.2007.900239","DOI":"10.1109\/TNN.2007.900239"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] S. Shadmand and B. Mashoufi, \u201cA new personalized ECG signal classification algorithm using block-based neural network and particle swarm optimization,\u201d Biomed. Signal Process. Control, vol.25, pp.12-23, March 2016. DOI: 10.1016\/j.bspc.2015.10.008. 10.1016\/j.bspc.2015.10.008","DOI":"10.1016\/j.bspc.2015.10.008"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] Y.H. Hu, S. Palreddy, and W.J. Tompkins, \u201cA patient-adaptable ECG beat classifer using a mixture of experts approach,\u201d IEEE Trans. Biomed. Eng., vol.44, no.9, pp.891-900, Spet. 1997. DOI: 10.1109\/10.623058. 10.1109\/10.623058","DOI":"10.1109\/10.623058"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] Z. Zhang, J. Dong, X. Luo, K.-S. Choi, and X. Wu, \u201cHeartbeat classification using disease-specific feature selection,\u201d Comput. Biol. Med., vol.46, pp.79-89, March 2014. DOI: 10.1016\/j.compbiomed.2013.11.019. 10.1016\/j.compbiomed.2013.11.019","DOI":"10.1016\/j.compbiomed.2013.11.019"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] S. Raj, K.C. Ray, and O. Shankar, \u201cCardiac arrhythmia beat classification using DOST and PSO tuned SVM,\u201d Comput. Methods Programs Biomed., vol.136, pp.163-177, Nov. 2016. DOI: 10.1016\/j.cmpb.2016.08.016. 10.1016\/j.cmpb.2016.08.016","DOI":"10.1016\/j.cmpb.2016.08.016"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] A. Khazaaee and A. Ebrahimzadeh, \u201cClassification of electrocardiogram signals with support vector machines and genetic algorithms using power spectral features,\u201d Biomed. Signal Process. Control, vol.5, no.4, pp.252-263, Oct. 2010. DOI: 10.1016\/j.bspc.2010.07.006. 10.1016\/j.bspc.2010.07.006","DOI":"10.1016\/j.bspc.2010.07.006"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] S. Kiranyaz, T. Ince, and M. Gabbouj, \u201cReal-time patient-specific ECG classification by 1-D convolutional neural networks,\u201d IEEE Trans. Biomed. Eng., vol.63, no.3, pp.664-675, March 2016. DOI: 10.1109\/TBME.2015.2468589. 10.1109\/tbme.2015.2468589","DOI":"10.1109\/TBME.2015.2468589"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] M.M.A. Rahhal, Y. Bazi, H. AlHichri, N. Alajlan, F. Melgani, and R.R. Yager, \u201cDeep learning approach for active classification of electrocardiogram signal,\u201d Inf. Sci., vol.345, pp.340-354, June 2016. DOI: 10.1016\/j.ins.2016.01.082. 10.1016\/j.ins.2016.01.082","DOI":"10.1016\/j.ins.2016.01.082"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] T. Teijeiro, P. F\u00e9lix, J. Presedo, and D. Castro, \u201cHeartbeat classification using abstract features from the abductive interpretation of the ECG,\u201d IEEE J. Biomed. Health Inform., vol.PP, no.99, p.1, Nov. 2016. DOI: 10.1109\/JBHI.2016.2631247. 10.1109\/jbhi.2016.2631247","DOI":"10.1109\/JBHI.2016.2631247"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] H. Yang, C. Kan, G. Liu, and Y. Chen, \u201cSpatiotemporal differentiation of myocardial infarctions,\u201d IEEE Trans. Autom. Sci. Eng., vol.10, no.4, pp.938-947, Oct. 2013. DOI: 10.1109\/TASE.2013.2263497. 10.1109\/TASE.2013.2263497","DOI":"10.1109\/TASE.2013.2263497"},{"key":"21","unstructured":"[21] A. Gonzalez-Garcia, A. Vezhnevets, and V. Ferrari, \u201cAn active search strategy for efficient object class detection,\u201d IEEE Conf. on Comput. Vis. Pattern Recognit. (CVPR), pp.3022-3031, 2015."},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] N. Zhang, J. Donahue, R. Girshick, and T. Darrell, \u201cPart-based R-CNNs for fine-grained category detection,\u201d European Conf. on Comput. Vis. (ECCV), vol.8689, pp.834-849, 2014. DOI: 10.1007\/978-3-319-10590-1_54. 10.1007\/978-3-319-10590-1_54","DOI":"10.1007\/978-3-319-10590-1_54"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] T. Xiao, Y. Xu, K. Yang, J. Zhang, Y. Peng, and Z. Zhang, \u201cThe application of two-level attention models in deep convolutional neural network for fine-grained image classification,\u201d IEEE Confer. on Comput. Vis. Pattern Recognit. (CVPR), pp.842-850, 2015. 10.1109\/cvpr.2015.7298685","DOI":"10.1109\/CVPR.2015.7298685"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] A.L. Goldberger, L.A.N. Amaral, L. Glass, J.M. Hausdorff, P.C. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C..-K. Peng, and H.E. Stanley, \u201cPhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals,\u201d Circulation, vol.101, no.23, pp.E215-E220, June 2000. DOI: 10.1161\/01.CIR.101.23.E215. 10.1161\/01.CIR.101.23.E215","DOI":"10.1161\/01.CIR.101.23.e215"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] A. Kennedy, D.D. Finlay, D. Guldenring, R.R. Bond, K. Moran, and J. McLaughlin, \u201cAutomated detection of atrial fibrillation using R-R intervals and multivariate-based classification,\u201d J. Electrocardiol., vol.49, no.6, pp.871-876, Nov.-Dec. 2016. 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