Abstract
KLN (Koniocortex Like Network) is a novel Bioinspired Artificial Neural Network that models relevant biological properties of neurons as Synaptic Directionality, Long Term Potenciation, Long Term Depression, Metaplasticity and Intrinsic plasticity, together with natural normalization of sensory inputs and Winner-Take-All competitive learning. As a result, KLN performs a Deeper Learning on DataSets showing several high order properties of biological brains as: associative memory, scalability and even continuous learning. KLN learning is originally unsupervised and its architecture is inspired in the koniocortex, the first cortical layer receiving sensory inputs where map reorganization and feature extraction have been identified, as is the case of the visual cortex. This new model has shown big potential on synthetic inputs and research is now on application performance in complex problems involving real data in comparison with state-of-art supervised and unsupervised techniques. In this paper we apply KLN to explore its capabilities on one of the biggest problem of nowadays society and medical community, as it is the early detection of cardiovascular disease. The world’s number one killer, with 17,9 million deaths every year. Results of KLN on the classification of Cardiac arrhythmias from the well-known MIT-BIH cardiac arrhythmias database are reported.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Miller, K.D.: Synaptic economics: competition and cooperation in synaptic plasticity. Neuron 17, 371–374 (1996)
Kaski, S., Kohonen, T.: Winner-take-all networks for physiological models of competitive learning. Neural Netw. 7(6/7), 973–984 (1994)
Fukai, T., Tanaka, S.: A simple neural network exhibiting selective activation of neuronal ensembles: from winner-take-all to winners-share-all. Neural Comput. 9(1), 77–97 (1997)
Mao, Z.H., Massaquoi, S.G.: Dynamics of winner-take-all competition in recurrent neural networks with lateral inhibition. IEEE Trans. Neural Netw. 18, 55–69 (2007)
Yang, J.F., Chen, C.M.: Winner-take-all neural network using the highest threshold. IEEE Trans. Neural Netw. 11, 194–199 (2000)
Andina, D., Alvarez-Vellisco, A., Aleksandar, J., Fombellida, J.: Artificial metaplasticity can improve artificial neural network learning. Intell. Autom. Soft Comput.; Spec. Issue Signal Process. Soft Comput. 15(4), 681–694 (2009)
Andina, D., Ropero-Pelaez, J.: On the biological plausibility of artificial metaplasticity learning algorithm. Neurocomputing (2012). https://doi.org/10.1016/j.neucom.2012.09.028
Ropero-Peláez, F.J., Andina, D.: Do biological synapses perform probabilistic computations? Neurocomputing (2012). https://doi.org/10.1016/j.neucom.2012.08.042
Ropero-Peláez, F.J., Aguiar-Furucho, M.A., Andina, D.: Intrinsic plasticity for natural competition in koniocortex-like neural networks. Int. J. Neural Syst. 16(5) (2016)
Fombellida, J., Martin-Rubio, I., Romera-Zarza, A., Andina, D.: KLN, a new biological koniocortex based unsupervised neural network: competitive results on credit scoring. Natural Comput. (2018). https://doi.org/10.1007/s11047-018-9698-6
World Health Organization: Global status report on noncommunicable diseases, Geneva (2010)
World Health Organization: Global Atlas on Cardiovascular Disease Prevention and Control, Geneva (2010)
Dey, N., Prasad Dash, T., Dash, S.: ECG signal denoising by functional link artificial neural network (FLANN). Int. J. Biomed. Eng. Technol. 7(4), 377–389 (2011)
Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45–50 (2001)
Peláez, F.J.R., Andina, D.: The koniocortex-like network: a new biologically plausible unsupervised neural network. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Toledo-Moreo, F.J., Adeli, H. (eds.) IWINAC 2015. LNCS, vol. 9107, pp. 163–174. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18914-7_17
Minami, K., Nakajima, H., Toyoshima, T.: Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network. IEEE Trans. Biomed. Eng. 46(2), 179–185 (1999)
Owis, M.I., Youssef, A.B.M., Kadah, Y.M.: Characterization of ECG signals based on blind source separation. Med. Biol. Eng. Comput. 40(5), 557–564 (2002)
Yu, S.N., Chou, K.T.: Integration of independent component analysis and neural networks for ECG beat classification. Expert Syst. Appl. 34(4), 2841–2846 (2008)
Ghorbanian, P., Jalali, A., Ghaffari, A., Nataraji, A.: An improved procedure for detection of heart arrhythmias with novel pre-processing techniques. Expert Syst. 29(5), 478–491 (2009)
Benchaib, Y., Marcano-Cedeño, A., Torres-Alegre, S., Andina, D.: Application of artificial metaplasticity neural networks to cardiac arrhythmias classification. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo Moreo, F.J. (eds.) IWINAC 2013. LNCS, vol. 7930, pp. 181–190. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38637-4_19
Alonso-Atienza, F., Morgado, E., Fernandez-Martinez, L., Garcia-Alberola, A., Rojo-Alvarez, J.: Detection of life-threatening arrhythmias using feature selection and support vector machines. IEEE Trans. Biomed. Eng. 61(3), 832–840 (2014)
Torres-Alegre, S., Fombellida, J., Piñuela-Izquierdo, J.A., Andina, D.: Artificial metaplasticity: application to MIT-BIH arrhythmias database. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Toledo-Moreo, F.J., Adeli, H. (eds.) IWINAC 2015. LNCS, vol. 9107, pp. 133–142. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18914-7_14
Elhaj, F., Salim, N., Harris, A., Swee, T., Ahmed, T.: Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Comput. Methods Programs Biomed. 127, 52–63 (2016)
Kiranyaz, S., Ince, T., Gabbouj, M.: Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans. Biomed. Eng. 63, 664–675 (2016)
Shanshan, C., Wei, H., Zhi, L., Jian, L., Xingjiao, G.: Heartbeat classification using projected and dynamic features of ECG signal. Biomed. Signal Process. Control 31, 165–173 (2017)
Torres-Alegre, S., Fombellida, J., Piñuela-Izquierdo, J.A., Andina, D.: AMSOM: artificial metaplasticity in SOM neural networks. Application to MIT-BIH arrhythmias data base. Neural Comput. Appl. (2018). https://doi.org/10.1007/s00521-018-3576-0
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Torres-Alegre, S., Benchaib, Y., Ferrández Vicente, J.M., Andina, D. (2019). Application of Koniocortex-Like Networks to Cardiac Arrhythmias Classification. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science(), vol 11487. Springer, Cham. https://doi.org/10.1007/978-3-030-19651-6_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-19651-6_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19650-9
Online ISBN: 978-3-030-19651-6
eBook Packages: Computer ScienceComputer Science (R0)