首页 » 文章 » 文章详细信息
COMPLEXITY Volume 2020 ,2020-03-31
Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy
Research Article
Fabio Pisano 1 Giuliana Sias 1 Alessandra Fanni 1 Barbara Cannas 1 António Dourado 2 Barbara Pisano 1 Cesar A. Teixeira 2
Show affiliations
DOI:10.1155/2020/4825767
Received 2019-09-09, accepted for publication 2020-02-22, Published 2020-03-31
PDF
摘要

The Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. The performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. The capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. This contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions.

授权许可

Copyright © 2020 Fabio Pisano et al. 2020
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

通讯作者

Fabio Pisano.Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari 09123, Italy, unica.it.fabio.pisano@diee.unica.it

推荐引用方式

Fabio Pisano,Giuliana Sias,Alessandra Fanni,Barbara Cannas,António Dourado,Barbara Pisano,Cesar A. Teixeira. Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy. COMPLEXITY ,Vol.2020(2020)

您觉得这篇文章对您有帮助吗?
分享和收藏
0

是否收藏?

参考文献
[1] A. Kavitha, V. Krishnaveni. (2016). A novel automatic stepwise signal processing based computer aided diagnosis system for epilepsy-seizure detection and classification for EEG. Biomedical Research.10. DOI: 10.1093/brain/122.6.1017.
[2] T. N. Alotaiby, S. A. Alshebeili, T. Alshawi, I. Ahmad. et al.(2014). EEG seizure detection and prediction algorithms: a survey. EURASIP Journal on Advances in Signal Processing.2014(1):183. DOI: 10.1093/brain/122.6.1017.
[3] I. Ullah, M. Hussain, E.-U.-H. Qazi, H. Aboalsamh. et al.(2018). An automated system for epilepsy detection using EEG brain signals based on deep learning approach. Expert Systems With Applications.107:61-71. DOI: 10.1093/brain/122.6.1017.
[4] F. Rosenow, K. M. Kleim, H. M. Hamer. (2015). Non-invasive EEG evaluation in epilepsy diagnosis. Expert Rev Neurother. Expert Review of Neurotherapeutics.15(4). DOI: 10.1093/brain/122.6.1017.
[5] F. Provini, G. Plazzi, P. Tinuper, S. Vandi. et al.(1999). Nocturnal frontal lobe epilepsy: a clinical and polygraphic overview of 100 consecutive cases. Brain.122(6):1017-1031. DOI: 10.1093/brain/122.6.1017.
[6] K. Cuppens, L. Lagae, B. Ceulemans, S. Van Huffel. et al.Detection of nocturnal frontal lobe seizures in pediatric patients by means of accelerometers: a first study. . DOI: 10.1093/brain/122.6.1017.
[7] B. Pisano, C. A. Teixeira, A. Dourado, A. Fanni. et al.(2019). Application of Self Organizing Map to Identify Nocturnal Epileptic Seizures. Neural Computing and Applications.10. DOI: 10.1093/brain/122.6.1017.
[8] K. He, X. Zhang, S. Ren, J. Sun. et al.Deep residual learning for image recognition. :770-778. DOI: 10.1093/brain/122.6.1017.
[9] P. Swami, T. K. Gandhi, B. K. Panigrahi, M. Tripathi. et al.(2016). A novel robust diagnostic model to detect seizures in electroencephalography. Expert Systems with Applications.56:116-130. DOI: 10.1093/brain/122.6.1017.
[10] S. Pouyanfar. (2018). A survey on deep learning: algorithms, techniques, and applications. ACM Computing Surveys.51(5):92-36. DOI: 10.1093/brain/122.6.1017.
[11] G. Litjens, T. Kooi, B. E. Bejnordi. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis.42:60-88. DOI: 10.1093/brain/122.6.1017.
[12] K. He. (1991). American Electroencephalographic Society guidelines for standard electrode position nomenclature. Journal of Clinical Neurophysiology.8(2):200-202. DOI: 10.1093/brain/122.6.1017.
[13] N. Qian. (1999). On the momentum term in gradient descent learning algorithms. Neural Networks.12(1):145-151. DOI: 10.1093/brain/122.6.1017.
[14] K. Simonyan, A. Zisserman. Very deep convolutional networks for large-scale image recognition. :1-14. DOI: 10.1093/brain/122.6.1017.
[15] M. Bonnet. (1992). EEG arousals: scoring rules and examples. Sleep.15(2):173-184. DOI: 10.1093/brain/122.6.1017.
[16] P. Ryvlin, S. Rheims, G. Risse. (2006). Nocturnal frontal lobe epilepsy. Epilepsia.47(2):83-86. DOI: 10.1093/brain/122.6.1017.
[17] A. Emami, N. Kunii, T. Matsuo, T. Shinozaki. et al.(2019). Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images. NeuroImage: Clinical.22. DOI: 10.1093/brain/122.6.1017.
[18] T. M. E. Nijsen, J. B. A. M. Arends, P. A. M. Griep, P. J. M. Cluitmans. et al.(2005). The potential value of three-dimensional accelerometry for detection of motor seizures in severe epilepsy. Epilepsy & Behavior.7(1):74-84. DOI: 10.1093/brain/122.6.1017.
[19] M. Zabihi, S. Kiranyaz, A. B. Rad, A. K. Katsaggelos. et al.(2016). Analysis of high-dimensional phase space via poincaré section for patient-specific seizure detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering.24(3):386-398. DOI: 10.1093/brain/122.6.1017.
[20] U. R. Acharya, S. Vinitha Sree, G. Swapna, R. J. Martis. et al.(2013). Automated EEG analysis of epilepsy: a review. Knowledge-Based Systems.45:147-165. DOI: 10.1093/brain/122.6.1017.
[21] O. Faust, U. R. Acharya, H. Adeli, A. Adeli. et al.(2015). Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure.26:56-64. DOI: 10.1093/brain/122.6.1017.
[22] L. Wang, W. Xue, Y. Li. (2017). Automatic epileptic seizure detection in EEG signals using multi-domain feature extraction and nonlinear analysis. Entropy.19(6):222. DOI: 10.1093/brain/122.6.1017.
[23] T. M. E. Nijsen, R. M. Aarts, P. J. M. Cluitmans, P. A. M. Griep. et al.(2010). Time-frequency analysis of accelerometry data for detection of myoclonic seizures. IEEE Transactions on Information Technology in Biomedicine.14(5):1197-1203. DOI: 10.1093/brain/122.6.1017.
[24] X. Wei, L. Zhou, Z.. : Chen, L. Zhang. et al.(2018). Automatic seizure detection using three dimensional CNN based on multi-channel EEG. BMC Medical Informatics and Decision Making.18(5):111. DOI: 10.1093/brain/122.6.1017.
[25] M. Z. Alom. (2019). A State-of-the-Art Survey on Deep Learning Theory and Architectures. Electronics.8. DOI: 10.1093/brain/122.6.1017.
[26] Y. Park, L. Luo, K. K. Parhi, T. Netoff. et al.(2011). Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia.52(10):1761-1770. DOI: 10.1093/brain/122.6.1017.
[27] M. Bandarabadi, C. A. Teixeira, F. Sales, A. Dourado. et al.(2011). Wepilet, optimal orthogonal wavelets for epileptic seizure prediction with one single surface channel. IEEE Engineering in Medicine and Biology Society.2011:7059-7062. DOI: 10.1093/brain/122.6.1017.
[28] B. Pisano, B. Cannas, G. Milioli. Autosomal dominant nocturnal frontal lobe epilepsy seizure characterization through wavelet transform of EEG records and self organizing maps. :1-6. DOI: 10.1093/brain/122.6.1017.
[29] U. R. Acharya, H. Fujita, V. K. Sudarshan, S. Bhat. et al.(2015). Application of entropies for automated diagnosis of epilepsy using EEG signals: a review. Knowledge-Based Systems.88:85-96. DOI: 10.1093/brain/122.6.1017.
[30] D. Chen, S. Wan, J. Xiang, F. S. Bao. et al.(2017). A high-performance seizure detection algorithm based on Discrete Wavelet Transform (DWT) and EEG. Plos One.12(3). DOI: 10.1093/brain/122.6.1017.
[31] H. H. Jasper. (1958). Report of the committee on methods of clinical examination in electroencephalography. Electroencephalography and Clinical Neurophysiology.10(2):370-375. DOI: 10.1093/brain/122.6.1017.
[32] S. J. Pan, Q. Yang. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering.22(10):1345-1359. DOI: 10.1093/brain/122.6.1017.
[33] I. Goodfellow, Y. Bengio, A. Courville. (2016). Deep Learning (15.2 Transfer Learning and Domain Adaptation). DOI: 10.1093/brain/122.6.1017.
[34] University of Bonn. (2019). Bonn Epilepsiae Data Base. DOI: 10.1093/brain/122.6.1017.
[35] J. Klatt, H. Feldwisch-Drentrup, M. Ihle. (2012). The EPILEPSIAE database: an extensive electroencephalography database of epilepsy patients. Epilepsia.53(9):1669-1676. DOI: 10.1093/brain/122.6.1017.
[36] U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan. et al.(2018). Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in Biology and Medicine.100:270-278. DOI: 10.1093/brain/122.6.1017.
文献评价指标
浏览 18次
下载全文 0次
评分次数 0次
用户评分 0.0分
分享 0次