63
Views
7
CrossRef citations to date
0
Altmetric
Research Article

Combining neural networks and ANFIS classifiers for supervised examining of electrocardiogram beats

Pages 484-497 | Received 28 May 2013, Accepted 31 Jul 2013, Published online: 18 Sep 2013

References

  • Ozbay, Y., Ceylan, R., and Karlik, B., 2006, A fuzzy clustering neural network architecture for classification of ECG arrhythmias. Computers in Biology and Medicine, 36, 376–388
  • Lin, C., Du, Y., and Chen, T., 2008, Adaptive wavelet network for multiple cardiac arrhythmias recognition. Expert Systems with Applications, 34, 2601–2611
  • Benitez, D., Gaydecki, P.A., Zaidi, A., Fitzpatrick, A.P., 2001, The use of the Hilbert transform in ECG signal analysis. Computers in Biology and Medicine, 31, 399–406
  • Christov, I., Gómez-Herrero, G., Krasteva, V., Jekova, I., Gotchev, A., and Egiazarian, K., 2006, Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification. Medical Engineering & Physics, 28, 876–887
  • De Chazal, P., 2004, Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Transactions on Biomedical Engineering, 51, 1196–1206
  • Kara, S., and Okandan, M., 2007, Atrial fibrillation classification with artificial neural networks. Pattern Recognition, 40, 2967–2973
  • Lin, C.-H., 2008, Frequency-domain features for ECG beat discrimination using grey relational analysis-based classifier. Computers & Mathematics with Applications, 55, 680–690
  • Tsipouras, M.G., and Fotiadis, D.I., 2004, Automatic arrhythmia detection based on time and time-frequency analysis of heart rate variability. Computer Methods and Programs in Biomedicine, 74, 95–108
  • Abes, N., and Kudo, M., 2006, Non-parametric classifier-independent feature selection. Pattern Recognition, 39, 737–746
  • Christov, I., and Bortolan, G., 2004, Ranking of pattern recognition parameters for premature ventricular contractions classification by neural networks. Physiological Measurement, 25, 1281–1290
  • Christov, I., Jekova, I., and Bortolan, G., 2005, Premature ventricular contraction classification by the K th nearest-neighbours rule. Physiological Measurement, 26, 123–130
  • Chudácek, V., Georgoulas, G., Lhotská, L., Stylios, C., Petrík, M., and Cepek, M., 2009, Examining cross-database global training to evaluate five different methods for ventricular beat classification. Physiological Measurement, 30, 661–677
  • Exarchos, T.P., Tsipouras, M.G., Exarchos, C.P., Papaloukas, C., Fotiadis, D.I., and Michalis, L.K., 2007, A methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision tree. Artificial Intelligence in Medicine, 40, 187–200
  • Lin, C., Du, Y., and Chen, T., 2009, Nonlinear interpolation fractal classifier for multiple cardiac arrhythmias recognition. Chaos, Solitons & Fractals, 42, 2570–2581
  • Liu, H., Sun, J., Liu, L., and Zhang, H., 2009, Feature selection with dynamic mutual information. Pattern Recognition, 42, 1330–1339
  • Minhas, F.-A.A., and Arif, M., 2008, Robust electrocardiogram (ECG) beat classification using discrete wavelet transform. Physiological Measurement, 29, 555–570
  • Peng, H., Long, F., and Ding, C., 2005, Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 1226–1238
  • Polat, K., Kara, S., Güven, A., and Güneş, S., 2009, Usage of class dependency based feature selection and fuzzy weighted pre-processing methods on classification of macular disease. Expert Systems with Applications, 36, 2584–2591
  • Tsipouras, M., 2007, A framework for fuzzy expert system creation—application to cardiovascular diseases. IEEE Transactions on Biomedical Engineering, 54, 2089–2105
  • Yu, S.-N., and Chen, Y.-H., 2009, Noise-tolerant electrocardiogram beat classification based on higher order statistics of subband components. Artificial Intelligence in Medicine, 46, 165–178
  • Noponen, K., Kortelainen, J., and Seppänen, T., 2009, Invariant trajectory classification of dynamical systems with a case study on ECG. Pattern Recognition, 42, 1832–1844
  • Owis, M.I., Abou-Zied, A.H., Youssef, A.-B.M., and Kadah, Y.M., 2002, Study of features based on nonlinear dynamical modeling in ECG arrhythmia detection and classification. IEEE Transactions on Bio-Medical Engineering, 49, 733–736
  • Rohani Sarvestani, R., Boostani, R., and Roopaei, M., 2009, VT and VF classification using trajectory analysis. Nonlinear Analysis: Theory, Methods & Applications, 71, e55–e61
  • De Chazal, P., and Reilly, R., 2006, A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features. IEEE Transactions on Biomedical Engineering, 53, 2535–2543
  • Nilsson, M., Funk, P., Olsson, E.M.G., von Schéele, B., and Xiong, N., 2006, Clinical decision-support for diagnosing stress-related disorders by applying psychophysiological medical knowledge to an instance-based learning system. Artificial Intelligence in Medicine, 36, 159–176
  • Tsipouras, M.G., Fotiadis, D.I., and Sideris, D., 2005, An arrhythmia classification system based on the RR-interval signal. Artificial Intelligence in Medicine, 33, 237–250
  • Acharya, U., Sankaranarayanan, M., Nayak, J., Xiang, C., and Tamura, T., 2008, Automatic identification of cardiac health using modeling techniques: A comparative study. Information Sciences, 178, 4571–4582
  • Kannathal, N., and Lim, C. (2006). Cardiac state diagnosis using adaptive neuro-fuzzy technique. Medical Engineering & Physics, 28(8), 809--815
  • Yu, S., and Chou, K., 2008, Integration of independent component analysis and neural networks for ECG beat classification. Expert Systems with Applications, 34, 2841–2846
  • Yu, S.-N., and Chou, K.-T., 2009, Selection of significant independent components for ECG beat classification. Expert Systems with Applications, 36, 2088–2096
  • Melgani, F., and Bazi, Y., 2008, Classification of electrocardiogram signals with support vector machines and particle swarm optimization. IEEE Transactions on Information Technology in Biomedicine, 12, 667–677
  • Yu, S.-N., and Chou, K.-T., 2007, A switchable scheme for ECG beat classification based on independent component analysis. Expert Systems with Applications, 33, 824–829
  • Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K., and Stanley, H.E., 2000, PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101, e215–e220
  • Moody, G.B., and Mark, R.G., 2001, The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine: The Quarterly Magazine af The Engineering in Medicine & Biology Society, 20, 45–50
  • Zipes, D.P., Camm, A.J., Borggrefe, M., Buxton, A.E., Chaitman, B., Fromer, M., Gregoratos, G., Klein, G., Moss, A.J., Myerburg, R.J., Priori, S.G., Quinones, M.A., Roden, D.M., Silka, M.J., and Tracy, C., 2006, ACC/AHA/ESC 2006 guidelines for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death -- executive summary: A report of the American College of Cardiology/American Heart Association Task Force and the European Society of Cardiology Committee for Practice Guidelines (Writing Committee to Develop Guidelines for Management of Patients with Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death). Circulation, 114, 1088–1132 . Published online before print August 21, 2006. DOI: 10.1161/CIRCULATIONAHA.106.178104
  • Mallat, S., 1999, A Wavelet Tour of Signal Processing, 2nd ed., (New York: Academic Press)
  • Ghaffari, A., Homaeinezhad, M.R., Akraminia, M., Atarod, M., and Daevaeiha, M., 2009, A robust wavelet-based multi-lead Electrocardiogram delineation algorithm. Medical Engineering & Physics, 31, 1219–1227
  • Ghaffari, A., Homaeinezhad, M.R., Khazraee, M., and Daevaeiha, M.M., 2010, Segmentation of holter ECG waves via analysis of a discrete wavelet-derived multiple skewness-kurtosis based metric. Annals of Biomedical Engineering, 38, 1497–1510
  • Boyacioglu, M.A., and Avci, D., 2010, An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange. Expert Systems with Applications, 37, 7908–7912
  • Homaeinezhad, M.R., Sabzevari, S.A.H., Ghaffari, A., and Daevaeiha, M., 2012, High-accuracy characterization of ambulatory holter electrocardiogram eventsevents: A comparative study between Walsh-Hadamard Transform, First-Derivative-Based and Intelligent Techniques. International Journal of Systems Biology and Biomedical Technologies, 1, 40–71
  • Hu, Y., Palreddy, S., and Tompkins, W., 1997, A patient-adaptable ECG beat classifier using a mixture of experts approach. IEEE Transactions on Biomedical Engineering, 44, 891–900
  • Naseri, H., and Homaeinezhad, M.R., 2012, Computerized quality assessment of phonocardiogram signal measurement-acquisition parameters. Journal of Medical Engineering & Technology, 36, 308–318
  • Naseri, H., Homaeinezhad, M.R., Pourkhajeh, H., 2013, An expert electrocardiogram quality evaluation algorithm based on signal mobility factors. Medical Engineering & Technology, 37, 282--291
  • Naseri, H., Pourkhajeh, H., Homaeinezhad, M.R., 2013. A unified procedure for detecting, quantifying, and validating electrocardiogram T-wave alternans. Medical & Biological Engineering & Computing, 51, 1031--1042
  • Naseri, H., Homaeinezhad, M.R., and Pourkhajeh, H., 2013, Noise/spike detection in phonocardiogram signal as a cyclic random process with non-stationary period interval. Computers in Biology and Medicine, 43, 1205–1213
  • Homaeinezhad, M.R., Tavakkoli, E., Afshar, A., Atyabi, S.A., and Ghaffari, A., 2011, Neuro-ANFIS architecture for ECG rhythm-type recognition using different QRS geometrical-based features. Iranian Journal of Electrical & Electronic Engineering, 7, 70–83
  • Linh, T., Osowski, S., and Stodolski, M., 2003, On-line heart beat recognition using Hermite polynomials and neuro-fuzzy network. IEEE Transactions on Instrumentation and Measurement, 52, 1224–1231

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.