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Wu , \" Heartbeat classification using disease-specific feature selection,\" Elsevier Computers in biology and medicine , 2014 . Z. Zhang, J. Dong, X. Luo, K.-S. Choi, and X. Wu, \"Heartbeat classification using disease-specific feature selection,\" Elsevier Computers in biology and medicine, 2014."},{"key":"e_1_3_2_1_8_1","volume-title":"Melgani et al., \"Deep learning approach for active classification of electrocardiogram signals,\" Elsevier Information Sciences","author":"Al Rahhal M.","year":"2016","unstructured":"M. Al Rahhal , Y. Bazi , H. AlHichri , N. Alajlan , F. Melgani et al., \"Deep learning approach for active classification of electrocardiogram signals,\" Elsevier Information Sciences , 2016 . M. Al Rahhal, Y. Bazi, H. AlHichri, N. Alajlan, F. 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Stanzione et al., \"28.4 a battery-powered efficient multi-sensor acquisition system with simultaneous ECG, BIO-Z, GSR, and PPG,\" in IEEE ISSCC, 2016."},{"key":"e_1_3_2_1_17_1","volume-title":"Real-time ECG monitoring and arrhythmia detection using Android-based mobile devices,\" in IEEE EMBC","author":"Gradl S.","year":"2012","unstructured":"S. Gradl , P. Kugler , C. Lohm\u00fcller , and B. Eskofier , \" Real-time ECG monitoring and arrhythmia detection using Android-based mobile devices,\" in IEEE EMBC , 2012 . S. Gradl, P. Kugler, C. Lohm\u00fcller, and B. Eskofier, \"Real-time ECG monitoring and arrhythmia detection using Android-based mobile devices,\" in IEEE EMBC, 2012."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"L. B. Almeida \"The fractional fourier transform and time-frequency representations \" IEEE Transactions on signal processing 1994.  L. B. 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