This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Abhinav-Vishwa, M. K., Lal, S. D., and Vardwaj, P.. 2011. Classification of arrhythmic ECG data using machine learning techniques. International Journal of Interactive Multimedia and Artificial Intelligence 1 4: 68–71.Abhinav-VishwaM. K.LalS. D., and VardwajP.2011Classification of arrhythmic ECG data using machine learning techniques14687110.9781/ijimai.2011.1411Search in Google Scholar
Alfarhan, K. A., Mashor, M. Y., Saad, A. M., AbdulAzeez, H., and Al-Qaisee, M.. 2017. Effects of the window size and feature extraction approach for arrhythmia classification. Journal of Biomimetics, Biomaterials and Biomedical Engineering 30: 1–11.AlfarhanK. A.MashorM. Y.SaadA. M.AbdulAzeezH., and Al-QaiseeM.2017Effects of the window size and feature extraction approach for arrhythmia classification3011110.4028/www.scientific.net/JBBBE.30.1Search in Google Scholar
Benali, R., Dib, N., and Bereksi, F. R.. 2010. Cardiac arrhythmia diagnosis using a neuro-fuzzy approach. Journal of Mechanics in Medicine and Biology 10 3: 417–429.BenaliR.DibN., and BereksiF. R.2010Cardiac arrhythmia diagnosis using a neuro-fuzzy approach10341742910.1142/S021951941000354XSearch in Google Scholar
Berkaya, S. K., Uysal, A. K., Gunal, E. S., Ergin, S., Gunal, S., and Gulmezoglu. 2018. A survey on ECG analysis. Biomedical Signal Processing and Control 43: 216–235.BerkayaS. K.UysalA. K.GunalE. S.ErginS.GunalS., and Gulmezoglu2018A survey on ECG analysis4321623510.1016/j.bspc.2018.03.003Search in Google Scholar
Brans, J. P., and Mareschal, B.. 1994. The PROMCALC & GAIA decision support system for multicriteria decision aid. Decision Support System 12 4-5: 297–310.BransJ. P., and MareschalB.1994The PROMCALC & GAIA decision support system for multicriteria decision aid124-529731010.1016/0167-9236(94)90048-5Search in Google Scholar
Buhmann, M. D.. 2000. Radial basis functions. Acta Numerica 9: 1–38.BuhmannM. D.2000Radial basis functions913810.1017/S0962492900000015Search in Google Scholar
Celin, S., and Vasanth, K.. 2017. Survey on the methods for detecting arrhythmias using heart rate signals. Journal of Pharmaceutical Sciences and Research 9 p. 183.CelinS., and VasanthK.2017Survey on the methods for detecting arrhythmias using heart rate signals9183Search in Google Scholar
Che Soh, A., Chow, K. K., Mohammad Yusuf, U. K., Ishak, A. J., Hassan, M. K., and Khamis, S.. 2014. Development of neural network-based electronic nose for herbs recognition. International Journal on Smart Sensing & Intelligent Systems 7 2: 584–609.Che SohA.ChowK. K.Mohammad YusufU. K.IshakA. J.HassanM. K., and KhamisS.2014Development of neural network-based electronic nose for herbs recognition7258460910.21307/ijssis-2017-671Search in Google Scholar
Dalvi, R. D. F., Zago, G. T., and Andreao, R. V.. 2016. Heartbeat classification system based on neural networks and dimensionality reduction. Research on Biomedical Engineering 32 4: 318–326.DalviR. D. F.ZagoG. T., and AndreaoR. V.2016Heartbeat classification system based on neural networks and dimensionality reduction32431832610.1590/2446-4740.05815Search in Google Scholar
De Chazel, P., O’Dwyer, M., and Reilly, R. B.. 2004. Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Transactions on Biomedical Engineering 51 7: 1196–1206.De ChazelP.O’DwyerM., and ReillyR. B.2004Automatic classification of heartbeats using ECG morphology and heartbeat interval features5171196120610.1109/TBME.2004.82735915248536Search in Google Scholar
Demski, A., and Llamedo, S. M.. 2016. ECG-kit: a MATLAB toolbox for cardiovascular signal processing. Journal of Open Research Software 4 1.DemskiA., and LlamedoS. M.2016ECG-kit: a MATLAB toolbox for cardiovascular signal processing4110.5334/jors.86Search in Google Scholar
Erkaymaz, O., Ozer, M., and Perc, M.. 2017. Performance of small-world feedforward neural networks for the diagnosis of diabetes. Applied Mathematics and Computation 311: 22–28.ErkaymazO.OzerM., and PercM.2017Performance of small-world feedforward neural networks for the diagnosis of diabetes311222810.1016/j.amc.2017.05.010Search in Google Scholar
Farhidzadeh, H.. 2015. Probabilistic neural network training for semi-supervised classifiers. arXiv preprint arXiv, 1509–01271.FarhidzadehH.2015Probabilistic neural network training for semi-supervised classifiers150901271Search in Google Scholar
Haihua, X., Xianchuan, Y., Dan, H., and Sha, D.. 2015. Sensitivity analysis of hierarchical hybrid fuzzy-neural network. International Journal on Smart Sensing & Intelligent Systems 8 3: 1837–54.HaihuaX.XianchuanY.DanH., and ShaD.2015Sensitivity analysis of hierarchical hybrid fuzzy-neural network8318375410.21307/ijssis-2017-832Search in Google Scholar
Isa, S. M., Suryana, M. E., Akbar, M. A., Noviyanto, A., Jatmiko, W., and Arymurthy, A. M.. 2013. Performance analysis of ECG signal compression using SPIHT. International Journal on Smart Sensing & Intelligent Systems 6 5.IsaS. M.SuryanaM. E.AkbarM. A.NoviyantoA.JatmikoW., and ArymurthyA. M.2013Performance analysis of ECG signal compression using SPIHT6510.21307/ijssis-2017-624Search in Google Scholar
Jain, A., Sharma, V., and Sharma, V.. 2017. Big data mining using supervised machine learning approaches for Hadoop with Weka distribution. International Journal of Computational Intelligence Research 13 8: 2095–2111.JainA.SharmaV., and SharmaV.2017Big data mining using supervised machine learning approaches for Hadoop with Weka distribution13820952111Search in Google Scholar
Kelwade, J. P., and Salankar, S. S.. 2015. Prediction of cardiac arrhythmia using artificial neural network. International Journal of Computer Applications 115 20: 30–35.KelwadeJ. P., and SalankarS. S.2015Prediction of cardiac arrhythmia using artificial neural network11520303510.5120/20270-2679Search in Google Scholar
Khan, R. Z., and Jabbar, H.. 2009. Training algorithms for supervised machine learning: comparative study. International Journal of Management & Information Technology 4 3: 354–360.KhanR. Z., and JabbarH.2009Training algorithms for supervised machine learning: comparative study4335436010.24297/ijmit.v4i3.773Search in Google Scholar
Kiranyaz, S., Ince, T., and Gabbouj, M.. 2015. Real-time patient-specific ECG classification by 1D convolutional neural networks. IEEE Transactions on Biomedical Engineering 63 3: 664–675.KiranyazS.InceT., and GabboujM.2015Real-time patient-specific ECG classification by 1D convolutional neural networks63366467510.1109/TBME.2015.246858926285054Search in Google Scholar
Kohli, N., Verma, N. K., and Roy, A.. 2010. SVM based methods for arrhythmia classification in ECG. Proceeding of the International Conference on Computer and Communication Technology (ICCCT), India.KohliN.VermaN. K., and RoyA.2010Proceeding of the International Conference on Computer and Communication Technology (ICCCT)India10.1109/ICCCT.2010.5640480Search in Google Scholar
Kotsiantis, S. B., Zaharakis, I., and Pintelas, P.. 2007. Supervised machine learning: a review of classification techniques. Emerging Artificial Intelligence Applications in Computer Engineering 160: 3–24.KotsiantisS. B.ZaharakisI., and PintelasP.2007Supervised machine learning: a review of classification techniques16032410.1007/s10462-007-9052-3Search in Google Scholar
Lassoued, H., and Ketata, R.. 2017. Artificial neural network classifier for heartbeat arrhythmia detection. Proceeding of the International Conference on Automatic and Signal Processing (ATS), Engineering and Technology-PET, March 22-24, Sousse.LassouedH., and KetataR.2017Proceeding of the International Conference on Automatic and Signal Processing (ATS), Engineering and Technology-PETMarch 22-24SousseSearch in Google Scholar
Lassoued, H., and Ketata, R.. 2018. ECG multi-class classification using neural network as machine learning model. Proceeding of the International Conference on Advanced Systems and Electric Technologies (IC_ASET), IEEE, March 19-22.LassouedH., and KetataR.2018Proceeding of the International Conference on Advanced Systems and Electric Technologies (IC_ASET), IEEEMarch 19-22Search in Google Scholar
Li, J., Li, X., Huang, B., and Zhao, L.. 2016. Hopfield neural network approach for supervised nonlinear spectral unmixing. IEEE Geoscience and Remote Sensing Letters 13 7: 1002–1006.LiJ.LiX.HuangB., and ZhaoL.2016Hopfield neural network approach for supervised nonlinear spectral unmixing1371002100610.1109/LGRS.2016.2560222Search in Google Scholar
Mao, K. Z., Tan, K. C., and Ser, W.. 2000. Probabilistic neural-network structure determination for pattern classification. IEEE Transactions on Neural Networks 11 4: 1009–1016.MaoK. Z.TanK. C., and SerW.2000Probabilistic neural-network structure determination for pattern classification1141009101610.1109/72.85778118249828Search in Google Scholar
Martis, R. J., and Chakraborty, C. H.. 2011. Arrhythmia disease diagnosis using neural network, SVM, and genetic algorithm-optimized k-means clustering. Journal of Mechanics in Medicine and Biology 11 4: 897–915.MartisR. J., and ChakrabortyC. H.2011Arrhythmia disease diagnosis using neural network, SVM, and genetic algorithm-optimized k-means clustering11489791510.1142/S0219519411004101Search in Google Scholar
Matul Imah, E., Jatmiko, W., and Basaruddin, T.. 2013. Electrocardiogram for biometrics by using Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ): integrating feature extraction and classification. International Journal on Smart Sensing & Intelligent Systems 6 5: 1891–1917.Matul ImahE.JatmikoW., and BasaruddinT.2013Electrocardiogram for biometrics by using Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ): integrating feature extraction and classification651891191710.21307/ijssis-2017-619Search in Google Scholar
Ozbay, Y., Ceylan, R., and Karlik, K.. 2006. A Fuzzy clustering neural network architecture for classification of ECG arrhythmias. Computers in Biology and Medicine 36 4: 376–388.OzbayY.CeylanR., and KarlikK.2006A Fuzzy clustering neural network architecture for classification of ECG arrhythmias36437638810.1016/j.compbiomed.2005.01.006Search in Google Scholar
Ponomariov, V., Chirila, L., Apipie, F. M., Abate, R., Rusu, M., Wu, Z., Liehn, E. A., and Bucur, I.. 2017. Artificial intelligence versus doctors’ intelligence: a glance on machine learning benefaction in electrocardiography. Discoveries 5: 1–9.PonomariovV.ChirilaL.ApipieF. M.AbateR.RusuM.WuZ.LiehnE. A., and BucurI.2017Artificial intelligence versus doctors’ intelligence: a glance on machine learning benefaction in electrocardiography51910.15190/d.2017.6Search in Google Scholar
Rafiq, M. Y., Bugmann, G., and Easterbrook, D. J.. 2001. Neural network design for engineering applications. Computers & Structures 79 17: 1541–1552.RafiqM. Y.BugmannG., and EasterbrookD. J.2001Neural network design for engineering applications79171541155210.1016/S0045-7949(01)00039-6Search in Google Scholar
Rai, H. M., Anurag, T., and Shailja, S.. 2013. ECG signal processing for abnormalities detection using multi-resolution wavelet transform and Artificial Neural Network classifier. Measurement 46 9: 3238–3246.RaiH. M.AnuragT., and ShailjaS.2013ECG signal processing for abnormalities detection using multi-resolution wavelet transform and Artificial Neural Network classifier4693238324610.1016/j.measurement.2013.05.021Search in Google Scholar
Rather, A. M., Agarwal, A., and Sastry, V. N.. 2015. Recurrent neural network and a hybrid model for prediction of stock returns. Expert Systems with Applications 42: 3243–3241.RatherA. M.AgarwalA., and SastryV. N.2015Recurrent neural network and a hybrid model for prediction of stock returns423243324110.1016/j.eswa.2014.12.003Search in Google Scholar
Reilly, R. B., and Lee, T. C.. 2010. Electrograms (ECG, EEG, EMG, EOG). Technology and Health Care 18: 443–58.ReillyR. B., and LeeT. C.2010Electrograms (ECG, EEG, EMG, EOG)184435810.3233/THC-2010-060421099006Search in Google Scholar
Savalia, S., Eder, A., and Vahid, E.. 2017. Classification of cardiovascular disease using feature extraction and artificial neural networks. Journal of Biosciences and Medicines 5: 64–79.SavaliaS.EderA., and VahidE.2017Classification of cardiovascular disease using feature extraction and artificial neural networks5647910.4236/jbm.2017.511008Search in Google Scholar
Savic, M., Kurbalija, V., Ivanovic, M., and Bosnic, Z.. 2017. A feature selection method based on feature correlation networks. Proceeding of the International Conference on Model and Data Engineering, Springer, Barcelona.SavicM.KurbalijaV.IvanovicM., and BosnicZ.2017Proceeding of the International Conference on Model and Data Engineering, SpringerBarcelona10.1007/978-3-319-66854-3_19Search in Google Scholar
Seshagiri, S., and Khalil, H. K.. 2000. Output feedback control of nonlinear systems using RBF neural networks. IEEE Transactions on Neural Networks 11 1: 69–79.SeshagiriS., and KhalilH. K.2000Output feedback control of nonlinear systems using RBF neural networks111697910.1109/72.82251118249740Search in Google Scholar
Silipo, R., and Marchesi, C.. 1998. Artificial neural networks for automatic ECG analysis. IEEE Transactions on Signal Processing 46 5: 1417–1425.SilipoR., and MarchesiC.1998Artificial neural networks for automatic ECG analysis4651417142510.1109/78.668803Search in Google Scholar
Silva, I., and Moody, G. B.. 2014. An open-source toolbox for analysing and processing Physionet databases in Matlab and octave. Journal of open research software 2 1.SilvaI., and MoodyG. B.2014An open-source toolbox for analysing and processing Physionet databases in Matlab and octave2110.5334/jors.bi462766226525081Search in Google Scholar
Sonawane, J. S., Patil, D. R., and Thakare, V. S.. 2013. Survey on decision support system for heart disease. International Journal of Advancements in Technology 4 1: 89–96.SonawaneJ. S.PatilD. R., and ThakareV. S.2013Survey on decision support system for heart disease418996Search in Google Scholar
Tomar, M. S., Bandil, M. M. K., and Singh, M. D.. 2013. Multi resolution analysis of ECG for arrhythmia using soft-computing Techniques. Methodology 3 5: 1663–1668.TomarM. S.BandilM. M. K., and SinghM. D.2013Multi resolution analysis of ECG for arrhythmia using soft-computing Techniques3516631668Search in Google Scholar
Yadav, A.R., Anand, R. S., and Dewal, A. M.. 2014. Analysis and classification of hardwood species based on Coiflet DWT feature extraction and WEKA workbench. Proceeding of the International Conference on Signal Processing and Integrated Networks (SPIN), Noida India.YadavA.R.AnandR. S., and DewalA. M.2014Proceeding of the International Conference on Signal Processing and Integrated Networks (SPIN)Noida India10.1109/SPIN.2014.6776912Search in Google Scholar
Zhao, N., and Li, Z.. 2017. Viscosity prediction of different ethylene glycol/water based nanofluids using a RBF neural network. Applied Science 7 4, p. 409.ZhaoN., and LiZ.2017Viscosity prediction of different ethylene glycol/water based nanofluids using a RBF neural network7440910.3390/app7040409Search in Google Scholar