EEG Signal Analysis for Seizure Detection Using Recurrence Plots and Tchebichef Moments

This paper deals with seizure detection by processing EEG signals. In this context a methodology for transforming 1D EEG signals to 2D images thus the detection task can be accomplished by the Convolutional Neural Networks (CNNs) is proposed. The introduced method utilizing the high compactness of the Tchebichef moments along with the highly informative Recurrence Plots (RPs) that permits the application of pre-trained CNN models to detect the seizure cases. The proposed scheme provides improved detection accuracy up to 98% for the case of the Resnet18 model, while it shows outstanding robustness to additive (97%) as well as multiplicative (90%) noisy conditions. In this sense, the method outperforms the conventional approach of using the RPs to describe the raw EEG signals, by a factor of 1 %-5%. These results are very promising and justify the efficiency of the introduced method, towards establishing a concrete and robust EEG signals analysis approach.

[1]  Georg Dorffner,et al.  Detection of the EEG Artifacts by the Means of the (Extended) Kalman Filter , 2001 .

[2]  Jürgen Kurths,et al.  Recurrence plots for the analysis of complex systems , 2009 .

[3]  Reza Abiri,et al.  A comprehensive review of EEG-based brain–computer interface paradigms , 2019, Journal of neural engineering.

[4]  D. Song,et al.  EEG Based Emotion Identification Using Unsupervised Deep Feature Learning , 2015 .

[5]  Peter Hult,et al.  Detection of the 3rdHeart Sound Using Recurrence Time Statistics , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[6]  Dimitrios I. Fotiadis,et al.  Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks , 2007, Comput. Intell. Neurosci..

[7]  A. Giuliani,et al.  Recurrence Quantification Analysis and Principal Components in the Detection of Short Complex Signals , 1997, chao-dyn/9712017.

[8]  Shailesh D. Kamble,et al.  Review of EEG Signal Classification , 2019 .

[9]  Dimitris E. Koulouriotis,et al.  Efficient and accurate computation of geometric moments on gray-scale images , 2008, Pattern Recognit..

[10]  Gorjan Alagic,et al.  #p , 2019, Quantum information & computation.

[11]  Clinton Fookes,et al.  Deep facial analysis: A new phase I epilepsy evaluation using computer vision , 2018, Epilepsy & Behavior.

[12]  Basil G. Mertzios,et al.  Efficient computation of Zernike and Pseudo-Zernike moments for pattern classification applications , 2010, Pattern Recognition and Image Analysis.

[13]  César Camacho-Bello,et al.  Some computational aspects of Tchebichef moments for higher orders , 2018, Pattern Recognit. Lett..

[14]  Sim Heng Ong,et al.  Image Analysis by Tchebichef Moments , 2001, IEEE Trans. Image Process..

[15]  Banghua Yang,et al.  Automatic ocular artifacts removal in EEG using deep learning , 2018, Biomed. Signal Process. Control..

[16]  Xiaonei Zhang,et al.  GRP-DNet: A gray recurrence plot-based densely connected convolutional network for classification of epileptiform EEG , 2020, Journal of Neuroscience Methods.

[17]  George I. Lambrou,et al.  Signal2Image Modules in Deep Neural Networks for EEG Classification , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[18]  E. Bromfield,et al.  An Introduction to Epilepsy , 2006 .

[19]  Rajashri Khanai,et al.  Classification of Epileptic Seizures using Recurrence Plots and Machine Learning Techniques , 2019, 2019 International Conference on Communication and Signal Processing (ICCSP).

[20]  Lojini Logesparan,et al.  The impact of signal normalization on seizure detection using line length features , 2015, Medical & Biological Engineering & Computing.

[21]  D. Ruelle,et al.  Recurrence Plots of Dynamical Systems , 1987 .

[22]  Ying Liang,et al.  Automated Detection of High-Frequency Oscillations in Epilepsy Based on a Convolutional Neural Network , 2019, Front. Comput. Neurosci..

[23]  Manoranjan Paul,et al.  Epileptic seizure detection by analyzing EEG signals using different transformation techniques , 2014, Neurocomputing.

[24]  R. Mukundan,et al.  A Fast 4 $\times$ 4 Forward Discrete Tchebichef Transform Algorithm , 2007, IEEE Signal Processing Letters.

[25]  Justin A. Blanco,et al.  Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement , 2011, Journal of neural engineering.

[26]  Lojini Logesparan,et al.  Optimal features for online seizure detection , 2012, Medical & Biological Engineering & Computing.

[27]  Information and Communication Technology for Intelligent Systems , 2019, Smart Innovation, Systems and Technologies.

[28]  Kyung-Ok Cho,et al.  Comparison of different input modalities and network structures for deep learning-based seizure detection , 2020, Scientific Reports.

[29]  K Lehnertz,et al.  Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  Rana Fayyaz Ahmad,et al.  Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques , 2015, Australasian Physical & Engineering Sciences in Medicine.

[31]  U. Rajendra Acharya,et al.  Author's Personal Copy Biomedical Signal Processing and Control Automated Diagnosis of Epileptic Eeg Using Entropies , 2022 .

[32]  George A. Papakostas.  Improving the Recognition Performance of Moment Features by Selection , 2015, Feature Selection for Data and Pattern Recognition.

[33]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[34]  Dimitris E. Koulouriotis,et al.  Computation strategies of orthogonal image moments: A comparative study , 2010, Appl. Math. Comput..

[35]  C. Dang,et al.  Using recurrence plot for determinism analysis of EEG recordings in genetic absence epilepsy rats , 2008, Clinical Neurophysiology.

[36]  M. Teague.  Image analysis via the general theory of moments , 1980 .

[37]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[38]  Danna Zhou,et al.  d. , 1840, Microbial pathogenesis.

[39]  Qiang Chen,et al.  Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification , 2020, Frontiers in Neurology.

[40]  J. Zbilut,et al.  Embeddings and delays as derived from quantification of recurrence plots , 1992 .

[41]  Miss A.O. Penney.  (b) , 1974, The New Yale Book of Quotations.

[42]  Fadwa Al-Azzo,et al.  Classification and discrimination of focal and non-focal EEG signals based on deep neural network , 2017, 2017 International Conference on Current Research in Computer Science and Information Technology (ICCIT).

[43]  J. Koenderink.  Q… , 2014, Les noms officiels des communes de Wallonie, de Bruxelles-Capitale et de la communaute germanophone.

[44]  Dimitris A. Karras,et al.  Image coding using a wavelet based Zernike moments compression technique , 2002, 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628).

[45]  M. Shamim Hossain,et al.  Applying Deep Learning for Epilepsy Seizure Detection and Brain Mapping Visualization , 2019, ACM Trans. Multim. Comput. Commun. Appl..