Prediction of Seizure via Residual Networks Based on Decision Fusion

Two seizure prediction models are built based on a decision fusion strategy and residual network by using spatial coupling features and introducing an attention mechanism. First, eight frequency bands are filtered, and the correlation matrices are computed for each frequency of eighteen channels. Second, the eight 18x18 matrices are input to the residual module for classification, and the results are concatenated to form a vector. A fully connected layer is used for decision fusion. Third, to emphasize the coupling relationship among the different frequency bands, a cubic matrix formed by the eight 18x18 matrices is inputted to an attention network, resulting in the enhanced features. A seizure prediction model is thus proposed by combining the nine decisions. The performance of the model is compared with those from state-of-the-art methods, and the sensitivity of the proposed model is improved by 4.45%.

[1]  Josemir W Sander,et al.  Premature mortality in active convulsive epilepsy in rural Kenya , 2014, Neurology.

[2]  Jiaxiang Zhang,et al.  Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine , 2016, Journal of Neuroscience Methods.

[3]  Jiawei Yang,et al.  Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram , 2018, Neural Networks.

[4]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Ali H. Shoeb,et al.  Application of machine learning to epileptic seizure onset detection and treatment , 2009 .

[6]  O. Sourina,et al.  STEW: Simultaneous Task EEG Workload Data Set , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  Jörg Henkel,et al.  Highly efficient and accurate seizure prediction on constrained IoT devices , 2018, 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[8]  Kaspar Anton Schindler,et al.  Assessing seizure dynamics by analysing the correlation structure of multichannel intracranial EEG. , 2006, Brain : a journal of neurology.

[9]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[10]  Nitesh V. Chawla,et al.  Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier , 2017, Neurocomputing.

[11]  Zhiwen Liu,et al.  Seizure Prediction In EEG Records Based On Spatial-Frequency Features And Preictal Period Selection , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[12]  Guido Sanguinetti,et al.  Single-trial classification of EEG in a visual object task using ICA and machine learning , 2014, Journal of Neuroscience Methods.

[13]  Maja Stikic,et al.  EEG-based classification of positive and negative affective states , 2014 .

[14]  Reinhold Scherer,et al.  SAFE: An EEG dataset for stable affective feature selection , 2020, Adv. Eng. Informatics.

[15]  Progress in the treatment of epilepsy. , 1990, Journal of neurology, neurosurgery, and psychiatry.

[16]  Ihsan Ullah,et al.  An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach , 2018, Expert Syst. Appl..

[17]  Michalis E. Zervakis,et al.  A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals , 2018, Comput. Biol. Medicine.

[18]  Mohammad Hossein Sedaaghi,et al.  An efficient seizure prediction method using KNN-based undersampling and linear frequency measures , 2014, Journal of Neuroscience Methods.

[19]  Qin Lin,et al.  Classification of Epileptic EEG Signals with Stacked Sparse Autoencoder Based on Deep Learning , 2016, ICIC.

[20]  Leigh R. Hochberg,et al.  Predicting seizures from local field potentials recorded via intracortical microelectrode arrays , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[21]  Mojtaba Bandarabadi,et al.  Epileptic seizure prediction using relative spectral power features , 2015, Clinical Neurophysiology.

[22]  Fei Wang,et al.  Poster paper: Predicting seizures from electroencephalography recordings: A knowledge transfer strategy , 2016, 2016 IEEE International Conference on Healthcare Informatics (ICHI).

[23]  Michalis E. Zervakis,et al.  Discrimination of Preictal and Interictal Brain States from Long-Term EEG Data , 2017, 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS).

[24]  Qiaoli Yang,et al.  Chaos feature study in fractional Fourier domain for preictal prediction of epileptic seizure , 2017, Neurocomputing.

[25]  Gernot R. Müller-Putz,et al.  Domain Adaptation Techniques for EEG-Based Emotion Recognition: A Comparative Study on Two Public Datasets , 2019, IEEE Transactions on Cognitive and Developmental Systems.

[26]  Olga Sourina,et al.  Real-time EEG-based emotion monitoring using stable features , 2015, The Visual Computer.

[27]  Weidong Zhou,et al.  A low computation cost method for seizure prediction , 2014, Epilepsy Research.

[28]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[29]  Brian Litt,et al.  Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients , 2003, IEEE Transactions on Biomedical Engineering.

[30]  Sumaira Tasnim,et al.  Ensemble Classifiers and Their Applications: A Review , 2014, ArXiv.

[31]  Albert Y. Zomaya,et al.  A Review of Ensemble Methods in Bioinformatics , 2010, Current Bioinformatics.