Mathers C, Lopez A, Stein C, Fat D, Rao C. Deaths and DiseaseBurden by Cause: Global Burden of Disease Estimates for2001 by World Bank Country Groups. 2005 (revised 2005).
Thom T, Haase N, Rosamond W, Howard V. J, Rumsfeld J,Manolio T, Zheng Z. J, Flegal K, O’donnell C, Kittner S. HeartDisease and Stroke Statistics – 2006 update a report from theAmerican Heart Association Statistics Committee and StrokeStatistics Subcommittee, Circulation. 2006; 113(6):e85–e151.https://doi.org/10.1161/circulationaha.105.171600">https://doi.org/10.1161/circulationaha.105.171600
Alberdi A, Aztiria A, Basarab, A. Towards an automatic earlystress recognition system for office environments based onmultimodal measurements: a review, J. Biomed. Inform.2016; 59:49–75. https://doi.org/10.1016/j.jbi.2015.11.007">https://doi.org/10.1016/j.jbi.2015.11.007
Hadhoud MM, Eladawy MI, Farag, A. Computer aideddiagnosis of cardiac arrhythmias, In: The 2006 InternationalConference on Computer Engineering and Systems, IEEE;2006. https://doi.org/10.1109/icces.2006.320458">https://doi.org/10.1109/icces.2006.320458
Shiyovich A, Wolak A, Yacobovich L, Grosbard A, Katz A.Accuracy of diagnosing atrial flutter and atrial fibrillationfrom a surface electrocardiogram by hospital physicians:analysis of data from internal medicine departments, Am. J.Med. Sci., 2010; 340(4):271–275.https://doi.org/10.1097/maj.0b013e3181e73fcf">https://doi.org/10.1097/maj.0b013e3181e73fcf
Wang JS, Chiang WC, Hsu YL, Yang YTC. ECG arrhythmiaclassification using a probabilistic neural network with afeature reduction method. Neurocomputing. 2013; 116:38–45. https://doi.org/10.1016/j.neucom.2011.10.045">https://doi.org/10.1016/j.neucom.2011.10.045
Lin CH. Frequency-domain features for ECG beatdiscrimination using grey relational analysis-based classifier.Comput. Math. Appl. 2008; 55(4):680–690.https://doi.org/10.1016/j.camwa.2007.04.035">https://doi.org/10.1016/j.camwa.2007.04.035
Gramatikov B, Georgiev I. Wavelets as alternative to short-time Fourier transform in signal-averagedelectrocardiography, Med. Biol. Eng. Comput. 1995;33(3):482–487. https://doi.org/10.1007/bf02510534">https://doi.org/10.1007/bf02510534
Li C, Zheng C, Tai C. Detection of ECG characteristic pointsusing wavelet transforms. Biomed. Eng. IEEE Trans. 1995;42(1):21–28. https://doi.org/10.1109/10.362922">https://doi.org/10.1109/10.362922
Zhao QB, Zhang LQ. ECG feature extraction and classificationusing wavelet transform and support vector machines. In:Proceedings of the 2005 International Conference on NeuralNetworks and Brain, 2005; 1(3):1089–1092.https://doi.org/10.1109/icnnb.2005.1614807">https://doi.org/10.1109/icnnb.2005.1614807
Raj S, Ray KC. ECG signal analysis using DCT-based DOST andPSO optimized SVM. IEEE Trans. Instrum. Meas. 2017;66:470–478. https://doi.org/10.1109/tim.2016.2642758">https://doi.org/10.1109/tim.2016.2642758
El-Saadawy H, Tantawi M, Shedeed HA, Tolba MF. Hybridhierarchical method for electrocardiogram heartbeatclassification. IET Signal Processing. 2018; 12(4):506 –513.https://doi.org/10.1049/iet-spr.2017.0108">https://doi.org/10.1049/iet-spr.2017.0108
Kutlu Y, Kuntalp DA. multi-stage automatic arrhythmiarecognition and classification system. Comput. Biol. Med.,2011; 41(1):37–45.https://doi.org/10.1016/j.compbiomed.2010.11.003">https://doi.org/10.1016/j.compbiomed.2010.11.003
Das MK, Ari S. ECG beats classification using mixture offeatures. Int. Sch. Res. Not., 2014;178436.https://doi.org/10.1155/2014/178436">https://doi.org/10.1155/2014/178436
Oster J, Joachim B, Sayadi O, Nemati S, Johnson A, Clifford G.semi-supervised ECG beat classification and novelty detectionbased on switching Kalman filters. IEEE Trans. Biomed. Eng.2015; 62(9):2125-2134.https://doi.org/10.1109/tbme.2015.2402236">https://doi.org/10.1109/tbme.2015.2402236
Elhaj FA, Salim N, Harris AR, Swee TT, Ahmed T. Arrhythmiarecognition and classification using combined linear andnonlinear features of ECG signals. computer methods andprogram in biomedicine. Elsevier. 2016; 127:52–63.https://doi.org/10.1016/j.cmpb.2015.12.024">https://doi.org/10.1016/j.cmpb.2015.12.024
Moody GB, Mark GR. The impact of the MIT-BIH Arrhythmiadatabase. IEEE Eng. Med. Biol. 2001; 20(3):45–50.https://doi.org/10.1109/51.932724">https://doi.org/10.1109/51.932724
Martis RJ, Acharya UR, Lim CM, Mandana K, Ray AK,Chakraborty C. Application of higher order cumulant featuresfor cardiac health diagnosis using ECG signals. Int. J. NeuralSyst. 2013; 23(4).https://doi.org/10.1142/s0129065713500147">https://doi.org/10.1142/s0129065713500147
Thomas M, Das MK, Ari S. Automatic ECG arrhythmiaclassification using dual tree complex wavelet based features.International Journal of Electronics and Communications(AEÜ). 2015; 69(4):715-721.https://doi.org/10.1016/j.aeue.2014.12.013">https://doi.org/10.1016/j.aeue.2014.12.013
Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adam M, Gertych A,San Tan R. A deep convolutional neural network model toclassify heartbeats. Computers in Biology and Medicine.2017; 89:389-396.https://doi.org/10.1016/j.compbiomed.2017.08.022">https://doi.org/10.1016/j.compbiomed.2017.08.022
Yang W, Si Y, Wang D, Guo B. Automatic recognition ofarrhythmia based on principal component analysis networkand linear support vector machine. Computers in Biology andMedicine. 2018; 101:22-32.https://doi.org/10.1016/j.compbiomed.2018.08.003">https://doi.org/10.1016/j.compbiomed.2018.08.003
Oh SL, Ng EY, Tan RS, Acharya UR. Automated diagnosis ofarrhythmia using combination of CNN and LSTM techniqueswith variable length heart beats. Computers in Biology andMedicine. 2018; 102:278-287.https://doi.org/10.1016/j.compbiomed.2018.06.002">https://doi.org/10.1016/j.compbiomed.2018.06.002