Generative Autoencoder Kernels on Deep Learning for Brain Activity Analysis

Year 2018, , 311 - 322, 10.10.2018

Gokhan Altan , Yakup Kutlu

https://doi.org/10.28978/nesciences.468978

Cited By: 5

Abstract

Deep Learning (DL) is a two-step classification model that consists feature learning, generating feature representations using unsupervised ways and the supervised learning stage at the last step of model using at least two hidden layers on the proposed structures by fully connected layers depending on of the artificial neural networks. The optimization of the predefined classification parameters for the supervised models eases reaching the global optimality with exact zero training error. The autoencoder (AE) models are the highly generalized ways of the unsupervised stages for the DL to define the output weights of the hidden neurons with various representations. As alternatively to the conventional Extreme Learning Machines (ELM) AE, Hessenberg decomposition-based ELM autoencoder (HessELM-AE) is a novel kernel to generate different presentations of the input data within the intended sizes of the models. The aim of the study is analyzing the performance of the novel Deep AE kernel for clinical availability on electroencephalogram (EEG) with stroke patients. The slow cortical potentials (SCP) training in stroke patients during eight neurofeedback sessions were analyzed using Hilbert-Huang Transform. The statistical features of different frequency modulations were fed into the Deep ELM model for generative AE kernels. The novel Deep ELM-AE kernels have discriminated the brain activity with high classification performances for positivity and negativity tasks in stroke patients.

Keywords

Deep Learning, SCP, Hilbert-Huang Transform, Autoencoder, Deep ELM

References

• Allahverdi, N., Altan, G., & Kutlu, Y. (2016). Diagnosis of Coronary Artery Disease Using Deep Belief Networks. 2. International Conference on Engineering and Natural Science, Sarajevo, Bosnia, 40–46.
• Allahverdi, N., Altan, G., & Kutlu, Y. (2018). Deep Learning for COPD Analysis Using Lung Sounds. In 1st International Conference on Control and Optimization with Industrial Applications (COIA) (pp. 74–76). Baku, Azerbaijan.
• Altan, G., & Kutlu, Y. (2018). Hessenberg Elm Autoencoder Kernel For Deep Learning. Journal of Engineering Technology and Applied Sciences, 3(2), 141–151. https://doi.org/10.30931/jetas.450252.
• Altan, G., Kutlu, Y., & Allahverdi, N. (2016a). A new approach to early diagnosis of congestive heart failure disease by using Hilbert–Huang transform. Computer Methods and Programs in Biomedicine, 137, 23–34. https://doi.org/10.1016/J.CMPB.2016.09.003.
• Altan, G., Kutlu, Y., & Allahverdi, N. (2016b). Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke. International Journal of Applied Mathematics, Electronics and Computers, 4(Special Issue-1), 205–210. https://doi.org/10.18100/ijamec.270307.
• Altan, G., Kutlu, Y., Pekmezci, A. Ö., & Nural, S. (2018). Deep learning with 3D-second order difference plot on respiratory sounds. Biomedical Signal Processing and Control, 45, 58–69. https://doi.org/10.1016/j.bspc.2018.05.014.
• Altan, G., Kutlu, Y., Pekmezci, A. Ö., & Nural, S. (2018). Deep learning with 3D-second order difference plot on respiratory sounds. Biomedical Signal Processing and Control, 45, 58–69. https://doi.org/10.1016/j.bspc.2018.05.014.
• Altan, G., Kutlu, Y., Pekmezci, A. Ö., & Yayık, A. (2018). Diagnosis of Chronic Obstructive Pulmonary Disease using Deep Extreme Learning Machines with LU Autoencoder Kernel. In 7th International Conference on Advanced Technologies (ICAT’18) (pp. 618–622). Antalya.
• Barata, J. C. A., & Hussein, M. S. (2012). The Moore-Penrose Pseudoinverse: A Tutorial Review of the Theory. Brazilian Journal of Physics. https://doi.org/10.1007/s13538-011-0052-z
• Birbaumer, N., Elbert, T., Canavan, A. G., & Rockstroh, B. (1990). Slow potentials of the cerebral cortex and behavior. Physiological Reviews, 70(1), 1–41.
• Bosch, V., Mecklinger, A., & Friederici, A. D. (2001). Slow cortical potentials during retention of object, spatial, and verbal information. Cognitive Brain Research, 10(3), 219–237. https://doi.org/10.1016/S0926-6410(00)00040-9
• Devrim, M., Demiralp, T., Kurt, A., & Yücesir, I. (1999). Slow cortical potential shifts modulate the sensory threshold in human visual system. Neuroscience Letters, 270(1), 17–20. https://doi.org/10.1016/S0304-3940(99)00456-5
• Ergenoglu, T., Demiralp, T., Beydagi, H., Karamürsel, S., Devrim, M., & Ermutlu, N. (1998). Slow cortical potential shifts modulate P300 amplitude and topography in humans. Neuroscience Letters, 251(1), 61–64. https://doi.org/10.1016/S0304-3940(98)00498-4
• Göksu, H. (2018). BCI oriented EEG analysis using log energy entropy of wavelet packets. Biomedical Signal Processing and Control, 44, 101–109. https://doi.org/10.1016/j.bspc.2018.04.002
• Guang-bin Huang, Qin-yu Zhu, C. S. (2006). Extreme learning machine: A new learning scheme of feedforward neural networks. Neurocomputing. https://doi.org/10.1109/IJCNN.2004.1380068
• Hinterberger, T., Schmidt, S., Neumann, N., Mellinger, J., Blankertz, B., Curio, G., & Birbaumer, N. (2004). Brain-computer communication and slow cortical potentials. IEEE Transactions on Biomedical Engineering, 51(6), 1011–1018. https://doi.org/10.1109/TBME.2004.827067
• Hou, Y., & Tian, H. (2010). An automatic modulation recognition algorithm based on HHT and SVD. In Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010 (Vol. 8, pp. 3577–3581). https://doi.org/10.1109/CISP.2010.5647536
• Huang, M., Wu, P., Liu, Y., Bi, L., & Chen, H. (2008). Application and contrast in brain-computer interface Between hilbert-huang transform and wavelet transform. In Proceedings of the 9th International Conference for Young Computer Scientists, ICYCS 2008 (pp. 1706–1710). https://doi.org/10.1109/ICYCS.2008.537
• Huang, N. E., & Wu, Z. (2008). a Review on Hilbert-Huang Transform : Method and Its Applications. October, 46(2007), 1–23. https://doi.org/10.1029/2007RG000228.1.INTRODUCTION
• Kotchoubey, B., Schneider, D., Schleichert, H., Strehl, U., Uhlmann, C., Blankenhorn, V., … Birbaumer, N. (1996). Self-regulation of slow cortical potentials in epilepsy: A retrial with analysis of influencing factors. Epilepsy Research, 25(3), 269–276. https://doi.org/10.1016/S0920-1211(96)00082-4
• Kotchoubey, B., Strehl, U., Uhlmann, C., Holzapfel, S., König, M., Fröscher, W., … Birbaumer, N. (2001). Modification of slow cortical potentials in patients with refractory epilepsy: a controlled outcome study. Epilepsia, 42(3), 406–416.
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances In Neural Information Processing Systems. https://doi.org/http://dx.doi.org/10.1016/j.protcy.2014.09.007
• Kutlu, Y., Altan, G., Iscimen, B., Dogdu, S. A., & Turan, C. (2017). Recognition of Species of Triglidae Family using Deep Learning. Journal of Black Sea / Mediterranean Environment, 23(1), 56–65. Retrieved from http://www.blackmeditjournal.org/index.php/component/k2/item/574
• Kutlu, Y., Yayık, A., Yildirim, E., & Yildirim, S. (2017). LU triangularization extreme learning machine in EEG cognitive task classification. Neural Computing and Applications, pp. 1–10. https://doi.org/10.1007/s00521-017-3142-1
• Li, Y., Yingle, F., Gu, L., & Qinye, T. (2009). Sleep stage classification based on EEG hilbert-huang transform. In 2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009 (pp. 3676–3681). https://doi.org/10.1109/ICIEA.2009.5138842
• Malmivuo, J., & Plonsey, R. (2012). Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields. Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields. https://doi.org/10.1093/acprof:oso/9780195058239.001.0001
• Oweis, R. J., & Abdulhay, E. W. (2011). Seizure classification in EEG signals utilizing Hilbert-Huang transform. Biomedical Engineering Online, 10, 38. https://doi.org/10.1186/1475-925X-10-38
• Ozdemir, N., & Yildirim, E. (2014). Patient specific seizure prediction system using hilbert spectrum and Bayesian networks classifiers. Computational and Mathematical Methods in Medicine, 2014. https://doi.org/10.1155/2014/572082
• Pham, M., Hinterberger, T., Neumann, N., Kübler, A., Hofmayer, N., Grether, A., … Birbaumer, N. (2005). An auditory brain-computer interface based on the self-regulation of slow cortical potentials. Neurorehabilitation and Neural Repair, 19(3), 206–218. https://doi.org/10.1177/1545968305277628
• Ruben, R., Helena, E., Andreas, H., & et al. (2014). Slow cortical potential training in stroke. Germany.
• Sanei, S., & Chambers, J. a. (2007). EEG Signal Processing. Chemistry & biodiversity (Vol. 1). https://doi.org/10.1002/9780470511923 Schneider, F., Elbert, T., Heimann, H., Welker, a, Stetter, F., Mattes, R., … Mann, K. (1993). Self-regulation of slow cortical potentials in psychiatric patients: alcohol dependency. Biofeedback and Self-Regulation, 18(1), 23–32.
• Siniatchkin, M., Kirsch, E., Kropp, P., Stephani, U., & Gerber, W. D. (2000). Slow cortical potentials in migraine families. Cephalalgia, 20(10), 881–892. https://doi.org/10.1046/j.1468-2982.2000.00132.x
• Stern, R. M., Ray, W. J., & Quigley, K. S. (2001). Psychophysiological recording (2nd ed.). Journal of Psychophysiology. https://doi.org/10.1027//0269-8803.15.1.47
• Tang, J., Deng, C., & Huang, G.-B. (2016). Extreme Learning Machine for Multilayer Perceptron. IEEE Transactions on Neural Networks and Learning Systems, 27(4), 809–821. https://doi.org/10.1109/TNNLS.2015.2424995
• Wong, T.-T. (2015). Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognition, 48(9), 2839–2846. https://doi.org/http://dx.doi.org/10.1016/j.patcog.2015.03.009