Abstract
Various recurrent neural networks have been utilised for medical data analysis and classifications. In this paper, the ability of using dynamic neural network to medicine related problems has been examined. Furthermore, a survey on the use of recurrent neural network architectures in medical applications will be discussed. A case study using the Elman, the Jordan and Layer recurrent networks for the classifications of Uterine Electrohysterography signals for the prediction of term and preterm delivery for pregnant women are presented.
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
Similar content being viewed by others
References
Arvind, R., Karthik, B., Sriraam, N., Kannan, J.K.: Automated detection of PD resting tremor using PSD with recurrent neural network classifier. In: 2010 International Conference on Advances in Recent Technologies in Communication and Computing, pp. 414–417 (2010). doi:10.1109/ARTCom.2010.33
Baghamoradi, S., Naji, M., Aryadoost, H.: Evaluation of cepstral analysis of EHG signals to prediction of preterm labor. In: 18th Iranian Conference on Biomedical Engineering, pp. 1–3, Tehran, Iran (2011)
Chendeb, M., Khalil, M., Hewson, D., Duchêne, J.: Classification of non stationary signals using multiscale decomposition. J. Biomed. Sci. Eng. 03(02), 193–199 (2010). doi:10.4236/jbise.2010.32025
Chung, J.R., Kwon, J., Choe, Y.: Evolution of recollection and prediction in neural networks. In: 2009 International Joint Conference on Neural Networks, pp. 571–577 (2009). doi:10.1109/IJCNN.2009.5179065
Übeyli, E.D.: Recurrent neural networks employing Lyapunov exponents for analysis of ECG signals. Expert Syst. Appl. 37(2), 1192–1199 (2010). doi:10.1016/j.eswa.2009.06.022
Fele-Žorž, G., Kavšek, G., Novak-Antolič, Z., Jager, F.: A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups. Med. Biol. Eng. Compu. 46(9), 911–922 (2008). doi:10.1007/s11517-008-0350-y
Forney, E.M., Anderson, C.W.: Classification of EEG during imagined mental tasks by forecasting with Elman recurrent neural networks. In: The 2011 International Joint Conference on Neural Networks, pp. 2749–2755 (2011). doi:10.1109/IJCNN.2011.6033579
Garfield, R.E., Maner, W.L., MacKay, L.B., Schlembach, D., Saade, G.R.: Comparing uterine electromyography activity of antepartum patients versus term labor patients. Am. J. Obstet. Gynecol. 193(1), 23–29 (2005). doi:10.1016/j.ajog.2005.01.050
Guler, N., Ubeyli, E., Guler, I.: Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst. Appl. 29(3), 506–514 (2005). doi:10.1016/j.eswa.2005.04.011
Haykin, S.: Neural Networks A comprehensive Foundation. Prentice Hall PTR, New Jersey (1998)
Ilbay, K., Übeyli, E. D., Ilbay, G., Budak, F.: A new application of recurrent neural networks for EMG-based diagnosis of carpal tunnel syndrome. In: Cardot, H. (ed.), Recurrent Neural Network for Temporal Data Processing. InTech (2011). doi:10.5772/631
Koskela, T., Varsta, M., Heikkonen, J., Kaski, K.: Temporal sequence processing using recurrent SOM. In: Proceedings of the Second International Conference Knowledge-Based Intelligent Electronic Systems, pp. 21–23. IEEE, Adelaide. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=725861&tag=1. Accessed 1998
Kumar, S.P., Sriraam, N., Benakop, P.G.: Automated detection of epileptic seizures using wavelet entropy feature with recurrent neural network classifier. In: TENCON 2008–2008 IEEE Region 10 Conference, pp. 1–5. IEEE, Hyderabad (2008). doi:10.1109/TENCON.2008.4766836
Leman, H., Marque, C., Gondry, J.: Use of the electrohysterogram signal for characterization of contractions during pregnancy. IEEE Trans. Bio-med. Eng. 46(10), 1222–1229 (1999)
Ling, S.H., Leung, F.H.F., Leung, K.F., Lam, H.K., Iu, H.H.C.: An improved GA based modified dynamic neural network for cantonese-digit speech recognition. In: Grimm, M., Korschel, K. (eds.) Robust Speech Recognition and Understanding, p. 460. I-Tech, Vienna (2007)
Liu, B., Wang, M., Yu, H., Yu, L., Liu, Z.: Study of feature classification methods in BCI based on neural networks. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, vol. 3, pp. 2932–2935. China (2005). doi:10.1109/IEMBS.2005.1617088
Makarov, V.A., Song, Y., Velarde, M.G., Hübner, D., Cruse, H.: Elements for a general memory structure: properties of recurrent neural networks used to form situation models. Biol. Cybern. 98(5), 371–395 (2008). doi:10.1007/s00422-008-0221-5
Marshall, J.: Regulation of activity in uterine smooth muscle. Physiol. Rev. Suppl. (1962)
Petrosian, A.A., Prokhorov, D.V., Schiffer, R.B.: Recurrent neural network based approach for early recognition of alzheimer ’ s disease in EEG. Clin. Neurophysiol. 112(8), 1378–1387 (2001)
Physionet: Physionet (2011). http://physionet.org/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Hussain, A.J., Fergus, P., Al-Jumeily, D., Alaskar, H., Radi, N. (2015). The Utilisation of Dynamic Neural Networks for Medical Data Classifications- Survey with Case Study. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_80
Download citation
DOI: https://doi.org/10.1007/978-3-319-22053-6_80
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22052-9
Online ISBN: 978-3-319-22053-6
eBook Packages: Computer ScienceComputer Science (R0)