메뉴 건너뛰기




Volumn 10, Issue 9, 2014, Pages 1582-1590

Spline activated neural network for classifying cardiac arrhythmia

Author keywords

Arrhythmia classification; ECG; Feed forward neural network; Multilayer perceptron; RR interval

Indexed keywords


EID: 84900431107     PISSN: 15493636     EISSN: None     Source Type: Journal    
DOI: 10.3844/jcssp.2014.1582.1590     Document Type: Article
Times cited : (5)

References (42)
  • 1
    • 0003794268 scopus 로고
    • 1st Edn., Reston Publishing Company, Inc. (A Prentice-Hall Company), Virginia. ISBN: 0835913759
    • Ahmed, N. and T. Natarajan, 1983. Discrete-Time Signals and Systems. 1st Edn., Reston Publishing Company, Inc. (A Prentice-Hall Company), Virginia. ISBN: 0835913759, pp: 398.
    • (1983) Discrete-Time Signals and Systems , pp. 398
    • Ahmed, N.1    Natarajan, T.2
  • 4
    • 50049126632 scopus 로고    scopus 로고
    • Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal
    • DOI: 10.1016/j.artmed.2008.04.007
    • Asl, B.M., S.K. Setarehdan and M. Mohebbi, 2008. Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. Artificial Intell. Med., 44: 51-64. DOI: 10.1016/j.artmed.2008.04.007.
    • (2008) Artificial Intell. Med. , vol.44 , pp. 51-64
    • Asl, B.M.1    Setarehdan, S.K.2    Mohebbi, M.3
  • 5
    • 26444619558 scopus 로고    scopus 로고
    • A neural network approach for patient-specific 12-Lead ECG synthesis in patient monitoring environments
    • Sept. 19-22, IEEE Xplore Press, France, DOI: 10.1109/CIC.2004.1442896
    • Atoui, H., J. Fayn and P. Rube, 2004. A neural network approach for patient-specific 12-Lead ECG synthesis in patient monitoring environments. Proceedings of the Computers in Cardiology, Sept. 19-22, IEEE Xplore Press, France, pp: 161-164. DOI: 10.1109/CIC.2004.1442896.
    • (2004) Proceedings of the Computers in Cardiology , pp. 161-164
    • Atoui, H.1    Fayn, J.2    Rube, P.3
  • 7
    • 0036503554 scopus 로고    scopus 로고
    • Wavelets, fractals and radial basis functions
    • DOI: 10.1109/78.984733
    • Blu, T. and M. Unser, 2002. Wavelets, fractals and radial basis functions. IEEE Trans. Signal Proc., 50: 543-553. DOI: 10.1109/78.984733.
    • (2002) IEEE Trans. Signal Proc. , vol.50 , pp. 543-553
    • Blu, T.1    Unser, M.2
  • 8
    • 0000621802 scopus 로고    scopus 로고
    • Multivariable functional interpolation and adaptive networks
    • Broomhead, D. and D. Lowe, 1998. Multivariable functional interpolation and adaptive networks. Complex Syst., 2: 321-355.
    • (1998) Complex Syst. , vol.2 , pp. 321-355
    • Broomhead, D.1    Lowe, D.2
  • 9
    • 33846438201 scopus 로고    scopus 로고
    • Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network
    • DOI: 10.1016/j.eswa.2006.05.014
    • Ceylan, R. and Y. Ozbay, 2007. Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network. Expert Syst. Applic., 33: 286-295. DOI: 10.1016/j.eswa.2006.05.014.
    • (2007) Expert Syst. Applic. , vol.33 , pp. 286-295
    • Ceylan, R.1    Ozbay, Y.2
  • 10
    • 80053525768 scopus 로고    scopus 로고
    • Feedforward neural networks training: A comparison between genetic algorithm and back-propagation learning algorithm
    • Che, Z.G., T.A. Chiang and Z.H. Che, 2011. Feedforward neural networks training: A comparison between genetic algorithm and back-propagation learning algorithm. Int. J. Innov. Comput. Inform. Control., 7: 5839-5850.
    • (2011) Int. J. Innov. Comput. Inform. Control. , vol.7 , pp. 5839-5850
    • Che, Z.G.1    Chiang, T.A.2    Che, Z.H.3
  • 11
    • 0038274521 scopus 로고    scopus 로고
    • Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea
    • DOI: 10.1109/TBME.2003.812203
    • De Chazal, P., C. Heneghan, E. Sheridan, R. Reilly and P. Nolan et al., 2003. Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea. IEEE Trans. BME, 50: 686-696. DOI: 10.1109/TBME.2003.812203.
    • (2003) IEEE Trans. BME , vol.50 , pp. 686-696
    • De Chazal, P.1    Heneghan, C.2    Sheridan, E.3    Reilly, R.4    Nolan, P.5
  • 12
    • 84555190723 scopus 로고    scopus 로고
    • Weighted conditional random fields for supervised interpatient heartbeat classification
    • DOI: 10.1109/TBME.2011.2171037
    • De Lannoy, G., D. François, J. Delbeke and M. Verleysen, 2012. Weighted conditional random fields for supervised interpatient heartbeat classification. IEEE Trans. BME, 59: 241-247. DOI: 10.1109/TBME.2011.2171037.
    • (2012) IEEE Trans. BME , vol.59 , pp. 241-247
    • De Lannoy, G.1    François, D.2    Delbeke, J.3    Verleysen, M.4
  • 13
    • 41149104813 scopus 로고    scopus 로고
    • An ECG signals compression method and its validation using neural networks
    • DOI: 10.1109/TBME.2008.918465
    • Fira, C.M. and L. Goras, 2008. An ECG signals compression method and its validation using neural networks. IEEE Trans. BME, 5: 1319-1326. DOI: 10.1109/TBME.2008.918465.
    • (2008) IEEE Trans. BME , vol.5 , pp. 1319-1326
    • Fira, C.M.1    Goras, L.2
  • 14
    • 0025267657 scopus 로고
    • A comparison of the noise sensitivity of nine QRS detection algorithms
    • DOI: 10.1109/10.43620
    • Friesen, G.M., T.C. Jannett, M.A. Jadallah, S.L. Yates and S.R. Quint, 1990. A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Trans. BME, 37: 85-98. DOI: 10.1109/10.43620.
    • (1990) IEEE Trans. BME , vol.37 , pp. 85-98
    • Friesen, G.M.1    Jannett, T.C.2    Jadallah, M.A.3    Yates, S.L.4    Quint, S.R.5
  • 15
    • 3042565159 scopus 로고    scopus 로고
    • Cardiac arrhythmia classification using autoregressive modelling
    • DOI: 10.1186/1475-925X-1-5
    • Ge, D., N. Srinivasan and S. M. Krishnan, 2002. Cardiac arrhythmia classification using autoregressive modelling. Biomed. Eng. Online, 1: 5-5. DOI: 10.1186/1475-925X-1-5.
    • (2002) Biomed. Eng. Online , vol.1 , pp. 5-5
    • Ge, D.1    Srinivasan, N.2    Krishnan, S.M.3
  • 17
    • 84855452697 scopus 로고    scopus 로고
    • Feed Forward Neural Networks: An Introduction
    • Sandberg, I.W. (Ed.), John Wiley and Sons, New York, ISBN-10: 0471349119
    • Haykin, S., 2001. Feed Forward Neural Networks: An Introduction. In: Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives, Sandberg, I.W. (Ed.), John Wiley and Sons, New York, ISBN-10: 0471349119, pp: 1-16.
    • (2001) Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives , pp. 1-16
    • Haykin, S.1
  • 20
    • 67649208265 scopus 로고    scopus 로고
    • A generic and robust system for automated patient-specific classification of ECG signals, IEEE Trans
    • DOI: 10.1109/TBME.2009.2013934
    • Ince, T., S. Kiranyaz and M. Gabbouj, 2009. A generic and robust system for automated patient-specific classification of ECG signals, IEEE Trans. BME, 56: 1415-1426. DOI: 10.1109/TBME.2009.2013934.
    • (2009) BME , vol.56 , pp. 1415-1426
    • Ince, T.1    Kiranyaz, S.2    Gabbouj, M.3
  • 24
    • 76849115999 scopus 로고    scopus 로고
    • ECG signal compression and classification algorithm with quad level vector for ECG Holter system
    • DOI: 10.1109/TITB.2009.2031638
    • Kim, H., R.F. Yazicioglu, P. Merken, C. Van Hoof and H.G. Yoo, 2010. ECG signal compression and classification algorithm with quad level vector for ECG Holter system. IEEE Trans. Inf. Technol. Biomed., 14: 93-100. DOI: 10.1109/TITB.2009.2031638.
    • (2010) IEEE Trans. Inf. Technol. Biomed. , vol.14 , pp. 93-100
    • Kim, H.1    Yazicioglu, R.F.2    Merken, P.3    Van Hoof, C.4    Yoo, H.G.5
  • 25
    • 74049158271 scopus 로고    scopus 로고
    • Robust algorithm for arrhythmia classification in ECG using extreme learning machine
    • DOI: 10.1186/1475-925X-8-31
    • Kim, J., H.S. Shin, K. Shin and M. Lee, 2009. Robust algorithm for arrhythmia classification in ECG using extreme learning machine. Biomed. Eng. Online, 8: 31-31. DOI: 10.1186/1475-925X-8-31.
    • (2009) Biomed. Eng. Online , vol.8 , pp. 31-31
    • Kim, J.1    Shin, H.S.2    Shin, K.3    Lee, M.4
  • 26
    • 84900424531 scopus 로고    scopus 로고
    • Automatic detection of electrocardiogram ST segment: Application in ischemic disease diagnosis
    • Lee, D.H., J.W. Park, J. Choi, A. Rabbi and R. Fazel-Rezai, 2013. Automatic detection of electrocardiogram ST segment: Application in ischemic disease diagnosis. Int. J. Advan. Comput. Sci. Appli., 4: 150-155.
    • (2013) Int. J. Advan. Comput. Sci. Appli. , vol.4 , pp. 150-155
    • Lee, D.H.1    Park, J.W.2    Choi, J.3    Rabbi, A.4    Fazel-Rezai, R.5
  • 27
    • 84866602497 scopus 로고    scopus 로고
    • A wearable sensor module with a neural-network-based activity classification algorithm for daily energy expenditure estimation
    • DOI: 10.1109/TITB.2012.2206602
    • Lin, C., Y. Yang and J. Wang, 2012. A wearable sensor module with a neural-network-based activity classification algorithm for daily energy expenditure estimation. IEEE Trans. Inf. Technol. Biomed., 16: 991-998. DOI: 10.1109/TITB.2012.2206602.
    • (2012) IEEE Trans. Inf. Technol. Biomed. , vol.16 , pp. 991-998
    • Lin, C.1    Yang, Y.2    Wang, J.3
  • 28
    • 0023331258 scopus 로고
    • An introduction to computing with neural nets
    • DOI: 10.1109/MASSP.1987.1165576
    • Lippman, R., 1987. An introduction to computing with neural nets. IEEE ASSP Magazine, 4: 4-22. DOI: 10.1109/MASSP.1987.1165576.
    • (1987) IEEE ASSP Magazine , vol.4 , pp. 4-22
    • Lippman, R.1
  • 30
    • 79952156441 scopus 로고    scopus 로고
    • Heartbeat classification using feature selection driven by database generalization criteria
    • DOI: 10.1109/TBME.2010.2068048
    • Llamedo, M. and J.P. Martínez, 2011. Heartbeat classification using feature selection driven by database generalization criteria. IEEE Trans. Biomed. Eng., 58: 616-625. DOI: 10.1109/TBME.2010.2068048.
    • (2011) IEEE Trans. Biomed. Eng. , vol.58 , pp. 616-625
    • Llamedo, M.1    Martínez, J.P.2
  • 31
    • 84900434305 scopus 로고    scopus 로고
    • Cross validation evaluation for breast cancer prediction using multilayer perceptron neural networks
    • DOI: 10.3844/ajeassp.2011.576.585
    • Mojarad, S.A., S.S. Dlay, W.L. Woo and G.V. Sherbet, 2011. Cross validation evaluation for breast cancer prediction using multilayer perceptron neural networks. Am. J. Eng. Applied Sci., 4: 576-585. DOI: 10.3844/ajeassp.2011.576.585.
    • (2011) Am. J. Eng. Applied Sci. , vol.4 , pp. 576-585
    • Mojarad, S.A.1    Dlay, S.S.2    Woo, W.L.3    Sherbet, G.V.4
  • 32
    • 0034953193 scopus 로고    scopus 로고
    • The impact of the MIT-BIH arrhythmia database
    • DOI: 10.1109/51.932724
    • Moody, G.B. and R.G. Mark, 2001. The impact of the MIT-BIH arrhythmia database. Eng. Medi. Biol. Magazine IEEE, 20: 45-50. DOI: 10.1109/51.932724.
    • (2001) Eng. Medi. Biol. Magazine IEEE , vol.20 , pp. 45-50
    • Moody, G.B.1    Mark, R.G.2
  • 33
    • 23844468161 scopus 로고    scopus 로고
    • Credit scoring model based on radial basis function network
    • 28-30, IEEE Xplore Press, China, DOI: 10.1109/IWVDVT.2005.1504632
    • Pang, S., 2005. Credit scoring model based on radial basis function network. Proceedings of the IEEE International Workshop on VLSl Design and Video Tech. May, 28-30, IEEE Xplore Press, China, pp: 389-392. DOI: 10.1109/IWVDVT.2005.1504632.
    • (2005) Proceedings of the IEEE International Workshop on VLSl Design and Video Tech. May , pp. 389-392
    • Pang, S.1
  • 34
    • 78249264429 scopus 로고    scopus 로고
    • Active learning methods for electrocardiographic signal classification
    • DOI: 10.1109/TITB.2010.2048922
    • Pasolli, E. and R. Melgani, 2010. Active learning methods for electrocardiographic signal classification. IEEE Trans. Inf. Technol. Biomed., 14: 1405-1416. DOI: 10.1109/TITB.2010.2048922.
    • (2010) IEEE Trans. Inf. Technol. Biomed. , vol.14 , pp. 1405-1416
    • Pasolli, E.1    Melgani, R.2
  • 36
    • 79951792887 scopus 로고    scopus 로고
    • Modular neural network based arrhythmia classification system using ECG signal data
    • Shivajirao, M.J., L.N. Sanjay and A.G. Ashok, 2011. Modular neural network based arrhythmia classification system using ECG signal data. Int. J. Inf. Technol. Knowl. Manage., 4: 205-209.
    • (2011) Int. J. Inf. Technol. Knowl. Manage. , vol.4 , pp. 205-209
    • Shivajirao, M.J.1    Sanjay, L.N.2    Ashok, A.G.3
  • 37
    • 33646153511 scopus 로고    scopus 로고
    • Optimal selection of wavelet basis function applied to ECG signal denoising
    • DOI: 10.1016/j.dsp.2005.12.003
    • Singh, B.N. and A.K. Tiwari, 2006. Optimal selection of wavelet basis function applied to ECG signal denoising. Digital Signal Proc., 16: 275-287. DOI: 10.1016/j.dsp.2005.12.003.
    • (2006) Digital Signal Proc. , vol.16 , pp. 275-287
    • Singh, B.N.1    Tiwari, A.K.2
  • 38
    • 16244422622 scopus 로고    scopus 로고
    • An arrhythmia classification system based on the RRinterval signal
    • DOI: 10.1016/j.artmed.2004.03.007
    • Tsipouras, M.G., D.I. Fotiadis and D. Sideris, 2005. An arrhythmia classification system based on the RRinterval signal. Artificial Intell. Med., 33: 237-250. DOI: 10.1016/j.artmed.2004.03.007.
    • (2005) Artificial Intell. Med. , vol.33 , pp. 237-250
    • Tsipouras, M.G.1    Fotiadis, D.I.2    Sideris, D.3
  • 39
    • 0032029155 scopus 로고    scopus 로고
    • Learning and approximation capabilities of adaptive spline activation function neural networks
    • DOI: 10.1016/S0893-6080(97)00118-4
    • Vecci, L., R. Piazza and A. Uncini, 1998. Learning and approximation capabilities of adaptive spline activation function neural networks. Neural Netw., 11: 259-270. DOI: 10.1016/S0893-6080(97)00118-4.
    • (1998) Neural Netw. , vol.11 , pp. 259-270
    • Vecci, L.1    Piazza, R.2    Uncini, A.3
  • 41
    • 84866562436 scopus 로고    scopus 로고
    • Heartbeat classification using morphological and dynamic features of ECG signals, IEEE Trans
    • DOI: 10.1109/TBME.2012.2213253
    • Ye, C., Kumar and M.T. Coimbra, 2012. Heartbeat classification using morphological and dynamic features of ECG signals, IEEE Trans. BME, 59: 2930-2941. DOI: 10.1109/TBME.2012.2213253.
    • (2012) BME , vol.59 , pp. 2930-2941
    • Ye, C.1    Kumar2    Coimbra, M.T.3
  • 42
    • 34248190653 scopus 로고    scopus 로고
    • Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network
    • DOI: 10.1016/j.patrec.2007.01.017
    • Yu, S.N. and Y.H. Chen, 2007. Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network. Periodical J. Patt. Recog. Lett., 28: 1142-1150. DOI: 10.1016/j.patrec.2007.01.017.
    • (2007) Periodical J. Patt. Recog. Lett. , vol.28 , pp. 1142-1150
    • Yu, S.N.1    Chen, Y.H.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.