Adenuar Purnomo, Handayani Tjandrasa


Deep learning is commonly used to solve problems such as biomedical problems and many other problems. The most common architecture used to solve those problems is Convolutional Neural Network (CNN) architecture. However, CNN may be prone to overfitting, and the convergence may be slow. One of the methods to overcome the overfitting is batch normalization (BN). BN is commonly used after the convolutional layer. In this study, we proposed a further usage of BN in CNN architecture. BN is not only used after the convolutional layer but also used after the fully connected layer. The proposed architecture is tested to detect types of seizures based on EEG signals. The data used are several sessions of recording signals from many patients. Each recording session produces a recorded EEG signal. EEG signal in each session is first passed through a bandpass filter. Then 26 relevant channels are taken, cut every 2 seconds to be labeled the type of epileptic seizure. The truncated signal is concatenated with the truncated signal from other sessions, divided into two datasets, a large dataset, and a small dataset. Each dataset has four types of seizures. Each dataset is equalized using the undersampling technique. Each dataset is then divided into test and train data to be tested using the proposed architecture. The results show the proposed architecture achieves 46.54% accuracy for the large dataset and 93.33% accuracy for the small dataset. In future studies, the batch normalization parameter will be further investigated to reduce overfitting.

Full Text:



K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proc. 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–14, 2015.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 770–778, 2016.

M. Hosseini, D. Pompili, K. Elisevich, and H. Soltanian-Zadeh, “Optimized Deep Learning for EEG Big Data and Seizure Prediction BCI via Internet of Things,” IEEE Trans. Big Data, vol. 3, no. 4, pp. 392–404, Dec. 2017.

A. H. Ansari, P. J. Cherian, and H. H. Sciences, “Neonatal Seizure Detection Using Deep Convolutional Neural Networks,” in Proc. IEEE 27th International Workshop on Machine Learning for Signal Processing, 2017.

R. San-segundo, M. Gil-martín, L. F. D. Haro-enríquez, and J. Manuel, “Classification of epileptic EEG recordings using signal transforms and,” Comput. Biol. Med., vol. 109, pp. 148–158, 2019.

M. Zhou et al., “Epileptic Seizure Detection Based on EEG Signals and CNN,” Front Neuroinform, vol. 12, no. 95, pp. 1–14, 2018.

A. Emami, N. Kunii, T. Matsuo, T. Shinozaki, and K. Kawai, “NeuroImage: Clinical Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images,” NeuroImage Clin., vol. 22, no. May 2018, p. 101684, 2019.

H. Tjandrasa, S. Djanali, and F. X. Arunanto, “Feature extraction using combination of intrinsic mode functions and power spectrum for EEG signal classification,” Proc. - 2016 9th Int. Congr. Image Signal Process. Biomed. Eng. Informatics, CISP-BMEI 2016, pp. 1498–1502, 2017.

H. Tjandrasa and S. Djanali, “Classification of EEG signals using single channel independent component analysis, power spectrum, and linear discriminant analysis,” in Lecture Notes in Electrical Engineering, 2016, vol. 387, pp. 259–268.

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-Based Learning Applied to Document Recognition,” in IEEE Proceedings, 1998.

Y. LeCun, P. Haffner, L. Bottou, and Y. Bengio, “Object recognition with gradient-based learning,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 1681, pp. 319–345, 1999.

M. Liu, W. Wu, Z. Gu, Z. Yu, F. F. Qi, and Y. Li, “Deep learning based on Batch Normalization for P300 signal detection,” Neurocomputing, vol. 275, pp. 288–297, 2018.

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proc. 32nd Int. Conf. Mach. Learn. ICML 2015, vol. 1, pp. 448–456, 2015.

N. D. Truong, A. D. Nguyen, L. Kuhlmann, M. R. Bonyadi, and J. Yang, “A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis,” arXiv, pp. 1–8, 2017.

L. Yin, C. Zhang, and Z. Cui, “Experimental research on real-time acquisition and monitoring of wearable EEG based on TGAM module,” Comput. Commun., vol. 151, pp. 76–85, 2020.

J. Amin, M. Sharif, M. A. Anjum, M. Raza, and S. A. C. Bukhari, “Convolutional neural network with batch normalization for glioma and stroke lesion detection using MRI,” Cogn. Syst. Res., vol. 59, pp. 304–311, 2020.

D. Jing, D. Liu, S. Zhang, and Z. Guo, “Fatigue driving detection method based on EEG analysis in low-voltage and hypoxia plateau environment,” Int. J. Transp. Sci. Technol., vol. 9, no. 4, 2020.

S. Raghu, N. Sriraam, Y. Temel, S. V. Rao, and P. L. Kubben, “EEG based multi-class seizure type classification using convolutional neural network and transfer learning,” Neural Networks, vol. 124, pp. 202–212, 2020.

S. Raghu, N. Sriraam, and P. L. Kubben, “Automated detection of epileptic seizures using successive decomposition index and support vector machine classifier in long-term EEG,” Neural Comput. Appl., vol. 1, 2019.

V. Srinivasan, C. Eswaran, and N. Sriraam, “Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks,” IEEE Trans. Inf. Technol. Biomed., vol. 11, no. 3, pp. 288–295, May 2007.

D. Lin, F. Lin, Y. Lv, F. Cai, and D. Cao, “Chinese Character CAPTCHA Recognition and performance estimation via deep neural network,” Neurocomputing, vol. 288, pp. 11–19, 2018.

D. Macêdo, C. Zanchettin, A. L. I. Oliveira, and T. Ludermir, “Enhancing batch normalized convolutional networks using displaced rectifier linear units: A systematic comparative study,” Expert Syst. Appl., vol. 124, pp. 271–281, 2019.

J. Wang, S. Li, Z. An, X. Jiang, W. Qian, and S. Ji, “Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines,” Neurocomputing, vol. 329, pp. 53–65, 2019.

I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, “Chapter 10 - Deep learning,” in Machine Learning: A Constrained-based Approach, Morgan Kaufmann, 2017.

Q. Zhang, M. Zhang, T. Chen, Z. Sun, Y. Ma, and B. Yu, “Recent advances in convolutional neural network acceleration,” Neurocomputing, vol. 323, pp. 37–51, 2019.

O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, 2015.

M. D. Zeiler and R. Fergus, “Visualizing and Understanding Convolutional Networks,” CoRR, vol. abs/1311.2, 2013.

C. Szegedy et al., “Going deeper with convolutions,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 07-12-June, pp. 1–9, 2015.

H. Tjandrasa, Klasifikasi Sinyal EEG dan Aplikasinya. Surabaya: ITS Press, 2017.



  • There are currently no refbacks.