Automatic Seizure Detection using Fully Convolutional Nested LSTM.

The automatic seizure detection system can effectively help doctors to monitor and diagnose epilepsy thus reducing their workload. Many outstanding studies have given good results in the two-class seizure detection problems, but most of them are based on hand-wrought feature extraction. This study proposes an end-to-end automatic seizure detection system based on deep learning, which does not require heavy preprocessing on the EEG data or feature engineering. The fully convolutional network with three convolution blocks is first used to learn the expressive seizure characteristics from EEG data. Then these robust EEG features pertinent to seizures are presented as an input to the Nested Long Short-Term Memory (NLSTM) model to explore the inherent temporal dependencies in EEG signals. Lastly, the high-level features obtained from the NLSTM model are fed into the softmax layer to output predicted labels. The proposed method yields an accuracy range of 98.44-100% in 10 different experiments based on the Bonn University database. A larger EEG database is then used to evaluate the performance of the proposed method in real-life situations. The average sensitivity of 97.47%, specificity of 96.17%, and false detection rate of 0.487 per hour are yielded. For CHB-MIT Scalp EEG database, the proposed model also achieves a segment-level sensitivity of 94.07% with a false detection rate of 0.66 per hour. The excellent results obtained on three different EEG databases demonstrate that the proposed method has good robustness and generalization power under ideal and real-life conditions.

[1]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[2]  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.

[3]  Brian Litt,et al.  Detection of seizure precursors from depth-EEG using a sign periodogram transform , 2003, IEEE Transactions on Biomedical Engineering.

[4]  H. Adeli,et al.  Analysis of EEG records in an epileptic patient using wavelet transform , 2003, Journal of Neuroscience Methods.

[5]  C. Elger,et al.  Epileptic Seizures and Epilepsy: Definitions Proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE) , 2005, Epilepsia.

[6]  Hojjat Adeli,et al.  A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy , 2007, IEEE Transactions on Biomedical Engineering.

[7]  Hojjat Adeli,et al.  Improved spiking neural networks for EEG classification and epilepsy and seizure detection , 2007, Integr. Comput. Aided Eng..

[8]  Hojjat Adeli,et al.  Mixed-Band Wavelet-Chaos-Neural Network Methodology for Epilepsy and Epileptic Seizure Detection , 2007, IEEE Transactions on Biomedical Engineering.

[9]  V. Srinivasan,et al.  Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks , 2007, IEEE Transactions on Information Technology in Biomedicine.

[10]  Elif Derya íbeyli.  Wavelet/mixture of experts network structure for EEG signals classification , 2008 .

[11]  Hojjat Adeli,et al.  Principal Component Analysis-Enhanced Cosine Radial Basis Function Neural Network for Robust Epilepsy and Seizure Detection , 2008, IEEE Transactions on Biomedical Engineering.

[12]  Hojjat Adeli,et al.  A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection , 2009, Neural Networks.

[13]  A. Aarabi,et al.  A fuzzy rule-based system for epileptic seizure detection in intracranial EEG , 2009, Clinical Neurophysiology.

[14]  H. Adeli,et al.  Automated EEG-Based Diagnosis of Neurological Disorders: Inventing the Future of Neurology , 2010 .

[15]  Luigi Chisci,et al.  Real-Time Epileptic Seizure Prediction Using AR Models and Support Vector Machines , 2010, IEEE Transactions on Biomedical Engineering.

[16]  Bijaya K. Panigrahi,et al.  Expert model for detection of epileptic activity in EEG signature , 2010, Expert Syst. Appl..

[17]  U. Rajendra Acharya,et al.  Automatic Detection of Epileptic EEG Signals Using Higher Order cumulant Features , 2011, Int. J. Neural Syst..

[18]  U. Rajendra Acharya,et al.  Application of Recurrence Quantification Analysis for the Automated Identification of Epileptic EEG Signals , 2011, Int. J. Neural Syst..

[19]  Mahmut Ozer,et al.  EEG signals classification using the K-means clustering and a multilayer perceptron neural network model , 2011, Expert Syst. Appl..

[20]  E. C. Chua,et al.  Improved patient specific seizure detection during pre-surgical evaluation , 2011, Clinical Neurophysiology.

[21]  Paolo Bonato,et al.  Development of a Body Sensor Network to Detect Motor Patterns of Epileptic Seizures , 2012, IEEE Transactions on Biomedical Engineering.

[22]  Barry D. Van Veen,et al.  Seizure Detection Using the Phase-Slope Index and Multichannel ECoG , 2012, IEEE Transactions on Biomedical Engineering.

[23]  U. Rajendra Acharya,et al.  Application of Non-Linear and Wavelet Based Features for the Automated Identification of Epileptic EEG signals , 2012, Int. J. Neural Syst..

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

[25]  J. Crowcroft,et al.  Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine , 2012, Journal of Neuroscience Methods.

[26]  Weidong Zhou,et al.  Comparison of ictal and interictal EEG signals using Fractal Features , 2013, Int. J. Neural Syst..

[27]  R. Backofen,et al.  Semi-Supervised Prediction of SH2-Peptide Interactions from Imbalanced High-Throughput Data , 2013, PloS one.

[28]  Graeme D. Jackson,et al.  Cortical excitability and Refractory epilepsy: a Three-Year longitudinal Transcranial Magnetic Stimulation Study , 2013, Int. J. Neural Syst..

[29]  U. Rajendra Acharya,et al.  Automated EEG analysis of epilepsy: A review , 2013, Knowl. Based Syst..

[30]  H. Khamis,et al.  Frequency–moment signatures: A method for automated seizure detection from scalp EEG , 2013, Clinical Neurophysiology.

[31]  U. Rajendra Acharya,et al.  Application of Intrinsic Time-Scale Decomposition (ITD) to EEG signals for Automated seizure Prediction , 2013, Int. J. Neural Syst..

[32]  R. Raedt,et al.  A Decade of Experience with Deep Brain Stimulation for patients with Refractory Medial Temporal Lobe epilepsy , 2013, Int. J. Neural Syst..

[33]  Yusuf Uzzaman Khan,et al.  A Wavelet-Statistical Features Approach for Nonconvulsive Seizure Detection , 2014, Clinical EEG and neuroscience.

[34]  Yusuf Uzzaman Khan,et al.  Seizure prediction using statistical dispersion measures of intracranial EEG , 2014, Biomed. Signal Process. Control..

[35]  Lanlan Chen,et al.  A framework on wavelet-based nonlinear features and extreme learning machine for epileptic seizure detection , 2014, Biomed. Signal Process. Control..

[36]  William P. Marnane,et al.  Neonatal Seizure Detection Using Atomic Decomposition With a Novel Dictionary , 2014, IEEE Transactions on Biomedical Engineering.

[37]  Yi Chai,et al.  Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM , 2014, Biomed. Signal Process. Control..

[38]  Weidong Zhou,et al.  Epileptic EEG Classification Based on Kernel Sparse Representation , 2014, Int. J. Neural Syst..

[39]  Weidong Zhou,et al.  Automatic seizure detection using Stockwell transform and boosting algorithm for long-term EEG , 2015, Epilepsy & Behavior.

[40]  Moncef Gabbouj,et al.  Epileptic Seizure Classification of EEG Time-Series Using Rational Discrete Short-Time Fourier Transform , 2015, IEEE Transactions on Biomedical Engineering.

[41]  Andreas Schulze-Bonhage,et al.  Early Seizure Detection Algorithm Based on Intracranial EEG and Random Forest Classification , 2015, Int. J. Neural Syst..

[42]  H. Adeli,et al.  Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis , 2015, Seizure.

[43]  Weidong Zhou,et al.  Multifractal Analysis and Relevance Vector Machine-Based Automatic Seizure Detection in Intracranial EEG , 2015, Int. J. Neural Syst..

[44]  Gregory D. Hager,et al.  Temporal Convolutional Networks: A Unified Approach to Action Segmentation , 2016, ECCV Workshops.

[45]  Bijaya K. Panigrahi,et al.  A novel robust diagnostic model to detect seizures in electroencephalography , 2016, Expert Syst. Appl..

[46]  Aruna Tiwari,et al.  A novel genetic programming approach for epileptic seizure detection , 2016, Comput. Methods Programs Biomed..

[47]  P. Geethanjali,et al.  DWT Based Detection of Epileptic Seizure From EEG Signals Using Naive Bayes and k-NN Classifiers , 2016, IEEE Access.

[48]  Sherin M. Youssef,et al.  A hybrid automated detection of epileptic seizures in EEG records , 2016, Comput. Electr. Eng..

[49]  Ravishankar K. Iyer,et al.  Seizure Forecasting and the Preictal State in Canine Epilepsy , 2017, Int. J. Neural Syst..

[50]  Francisco Sales,et al.  A Realistic Seizure Prediction Study Based on Multiclass SVM , 2017, Int. J. Neural Syst..

[51]  Piotr Suffczynski,et al.  Changes of Ionic Concentrations During Seizure Transitions - A Modeling Study , 2017, Int. J. Neural Syst..

[52]  Mehrdad Nourani,et al.  Multi-Biosignal Analysis for Epileptic Seizure Monitoring , 2017, Int. J. Neural Syst..

[53]  R. B. Pachori,et al.  Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals , 2017 .

[54]  Kebin Jia,et al.  A Multi-view Deep Learning Method for Epileptic Seizure Detection using Short-time Fourier Transform , 2017, BCB.

[55]  Andrew G. Lamperski,et al.  Seizure Control in a Computational Model Using a Reinforcement Learning Stimulation Paradigm , 2017, Int. J. Neural Syst..

[56]  Rong Li,et al.  Epileptic Discharge Related Functional Connectivity Within and Between Networks in Benign Epilepsy with Centrotemporal Spikes , 2017, Int. J. Neural Syst..

[57]  Gonzalo Alarcón,et al.  The Role of Thalamus Versus Cortex in Epilepsy: Evidence from Human Ictal Centromedian Recordings in Patients Assessed for Deep Brain Stimulation , 2017, Int. J. Neural Syst..

[58]  Malek Adjouadi,et al.  A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy , 2017, Int. J. Neural Syst..

[59]  Gregor Strobbe,et al.  EEG Derived Brain Activity Reflects Treatment Response from Vagus Nerve Stimulation in Patients with Epilepsy , 2017, Int. J. Neural Syst..

[60]  Wim Van Paesschen,et al.  Online Automated Seizure Detection in Temporal Lobe Epilepsy Patients Using Single-lead ECG , 2017, Int. J. Neural Syst..

[61]  Klaus Lehnertz,et al.  Which Brain Regions are Important for Seizure Dynamics in Epileptic Networks? Influence of Link Identification and EEG Recording Montage on Node Centralities , 2017, Int. J. Neural Syst..

[62]  U. Rajendra Acharya,et al.  A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension , 2017, Pattern Recognit. Lett..

[63]  Dimitris Kugiumtzis,et al.  Dynamics of Epileptiform Discharges Induced by Transcranial Magnetic Stimulation in Genetic Generalized Epilepsy , 2017, Int. J. Neural Syst..

[64]  Tim Oates,et al.  Time series classification from scratch with deep neural networks: A strong baseline , 2016, 2017 International Joint Conference on Neural Networks (IJCNN).

[65]  Yuxuan Yang,et al.  Visibility Graph from Adaptive Optimal Kernel Time-Frequency Representation for Classification of Epileptiform EEG , 2017, Int. J. Neural Syst..

[66]  Yang Li,et al.  Epileptic Seizure Detection Based on Time-Frequency Images of EEG Signals Using Gaussian Mixture Model and Gray Level Co-Occurrence Matrix Features , 2018, Int. J. Neural Syst..

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

[68]  Dan Wang,et al.  Automated Detector of High Frequency Oscillations in Epilepsy Based on Maximum Distributed Peak Points , 2018, Int. J. Neural Syst..

[69]  U. Rajendra Acharya,et al.  Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals , 2017, Comput. Biol. Medicine.

[70]  Weidong Zhou,et al.  Epileptic EEG Identification via LBP Operators on Wavelet Coefficients , 2018, Int. J. Neural Syst..

[71]  Fan Zhang,et al.  Automatic seizure detection based on kernel robust probabilistic collaborative representation , 2018, Medical & Biological Engineering & Computing.

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

[73]  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.

[74]  Yusuf Uzzaman Khan,et al.  Automatic Seizure Detection Based on Morphological Features Using One-Dimensional Local Binary Pattern on Long-Term EEG , 2018, Clinical EEG and neuroscience.

[75]  Song Tan,et al.  Aberrant Thalamocortical Connectivity in Juvenile Myoclonic Epilepsy , 2018, Int. J. Neural Syst..

[76]  Weidong Zhou,et al.  Epileptic Seizure Prediction Using Diffusion Distance and Bayesian Linear Discriminate Analysis on Intracranial EEG , 2018, Int. J. Neural Syst..

[77]  Mustafa Talha Avcu,et al.  Seizure Detection Using Least Eeg Channels by Deep Convolutional Neural Network , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[78]  Rabab K. Ward,et al.  Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals , 2019, Clinical Neurophysiology.

[79]  Muhammad Bilal,et al.  Automatic Seizure Detection Using Multi-Resolution Dynamic Mode Decomposition , 2019, IEEE Access.

[80]  Weidong Zhou,et al.  Automatic Seizure Detection Based on S-Transform and Deep Convolutional Neural Network , 2020, Int. J. Neural Syst..