One dimensional convolutional neural networks for seizure onset detection using long-term scalp and intracranial EEG

Abstract Epileptic seizure detection using scalp electroencephalogram (sEEG) and intracranial electroencephalogram (iEEG) has attracted widespread attention in recent two decades. The accurate and rapid detection of seizures not only reflects the efficiency of the algorithm, but also greatly reduces the burden of manual detection during long-term electroencephalogram (EEG) recording. In this work, a stacked one-dimensional convolutional neural network (1D-CNN) model combined with a random selection and data augmentation (RS-DA) strategy is proposed for seizure onset detection. Firstly, we segmented the long-term EEG signals using 2-s sliding windows. Then, the 2-s interictal and ictal segments were classified by the stacked 1D-CNN model. During model training, a RS-DA strategy was applied to solve the problem of sample imbalance, and the patient-specific model was trained with event-based K-fold (K is the number of seizures per patient) cross validation for detecting all seizures of each patient. Finally, we evaluated the performances of the proposed approach in the two levels: the segment-based level and the event-based level. The proposed method was tested on two long-term EEG datasets: the CHB-MIT sEEG dataset and the SWEC-ETHZ iEEG dataset. For the CHB-MIT sEEG dataset, we achieved 88.14% sensitivity, 99.62% specificity and 99.54% accuracy in the segment-based level. From the perspective of the event-based level, 99.31% sensitivity, 0.2/h false detection rate (FDR) and mean 8.1-s latency were achieved. For the SWEC-ETHZ iEEG dataset, in the segment-based level, 90.09% sensitivity, 99.81% specificity and 99.73% accuracy were obtained. In the event-based level, 97.52% sensitivity, 0.07/h FDR and mean 13.2-s latency were attained. From these results, we can see that our method can effectively use both sEEG and iEEG data to detect epileptic seizures, and this may provide a reference for the clinical application of seizure onset detection.

[1]  Luca Benini,et al.  Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms , 2019, 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[2]  Weidong Zhou,et al.  Seizure Onset Detection Using Empirical Mode Decomposition and Common Spatial Pattern , 2021, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  Dongrui Wu,et al.  Deep Multi-View Feature Learning for EEG-Based Epileptic Seizure Detection , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[4]  Yang Li,et al.  A unified multi-level spectral-temporal feature learning framework for patient-specific seizure onset detection in EEG signals , 2020, Knowl. Based Syst..

[5]  Jasmin Kevric,et al.  Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction , 2018, Biomed. Signal Process. Control..

[6]  Yonghua Wang,et al.  Scalp EEG epileptogenic zone recognition and localization based on long-term recurrent convolutional network , 2020, Neurocomputing.

[7]  Zhang Yang,et al.  Performance evaluation of Empirical Mode Decomposition and Discrete Wavelet Transform for computerized hypoxia detection and prediction , 2018, 2018 26th Signal Processing and Communications Applications Conference (SIU).

[8]  Wenbin Hu,et al.  Epileptic Signal Classification With Deep EEG Features by Stacked CNNs , 2020, IEEE Transactions on Cognitive and Developmental Systems.

[9]  Mohamed Senouci,et al.  Seizure detection with single-channel EEG using Extreme Learning Machine , 2016, 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA).

[10]  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..

[11]  Natarajan Sriraam,et al.  Automated detection of epileptic seizures using successive decomposition index and support vector machine classifier in long-term EEG , 2019, Neural Computing and Applications.

[12]  Wei Zhao,et al.  MNL-Network: A Multi-Scale Non-local Network for Epilepsy Detection From EEG Signals , 2020, Frontiers in Neuroscience.

[13]  M. L. Dewal,et al.  Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine , 2014, Neurocomputing.

[14]  John Thomas,et al.  A deep Learning Scheme for Automatic Seizure Detection from Long-Term Scalp EEG , 2018, 2018 52nd Asilomar Conference on Signals, Systems, and Computers.

[15]  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.

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

[17]  Junzhong Zou,et al.  Automatic epileptic EEG detection using convolutional neural network with improvements in time-domain , 2019, Biomed. Signal Process. Control..

[18]  Krisnachai Chomtho,et al.  A Comparison of Deep Neural Networks for Seizure Detection in EEG Signals , 2019, bioRxiv.

[19]  Ram Bilas Pachori,et al.  A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform , 2017, IEEE Transactions on Biomedical Engineering.

[20]  Fenglong Ma,et al.  A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning , 2018, 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[21]  Fangzhou Xu,et al.  Scalp EEG classification using deep Bi-LSTM network for seizure detection , 2020, Comput. Biol. Medicine.

[22]  Wanzhong Chen,et al.  Patient-specific seizure detection method using nonlinear mode decomposition for long-term EEG signals , 2020, Medical & Biological Engineering & Computing.

[23]  Asghar Zarei,et al.  Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy based features of EEG signals , 2021, Comput. Biol. Medicine.

[24]  Youfang Lin,et al.  Representation based on ordinal patterns for seizure detection in EEG signals , 2020, Comput. Biol. Medicine.

[25]  A. Schulze-Bonhage,et al.  Latencies from intracranial seizure onset to ictal tachycardia: A comparison to surface EEG patterns and other clinical signs , 2015, Epilepsia.

[26]  Suparerk Janjarasjitt,et al.  Epileptic seizure classifications of single-channel scalp EEG data using wavelet-based features and SVM , 2017, Medical & Biological Engineering & Computing.

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

[28]  Tongguang Ni,et al.  Auto-Weighted Multi-View Discriminative Metric Learning Method With Fisher Discriminative and Global Structure Constraints for Epilepsy EEG Signal Classification , 2020, Frontiers in Neuroscience.

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

[30]  Tingxi Wen,et al.  Deep Convolution Neural Network and Autoencoders-Based Unsupervised Feature Learning of EEG Signals , 2018, IEEE Access.

[31]  H. Pourghassem,et al.  Patient-Specific Epileptic Seizure Onset Detection Algorithm Based on Spectral Features and IPSONN Classifier , 2013, 2013 International Conference on Communication Systems and Network Technologies.

[32]  Ayman El-Sayed,et al.  Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals , 2021, Brain Informatics.

[33]  Moncef Gabbouj,et al.  Patient-Specific Seizure Detection Using Nonlinear Dynamics and Nullclines , 2020, IEEE Journal of Biomedical and Health Informatics.

[34]  Ali H. Shoeb,et al.  Application of Machine Learning To Epileptic Seizure Detection , 2010, ICML.

[35]  Khan M. Iftekharuddin,et al.  Real-Time Epileptic Seizure Detection Using EEG , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[36]  Chun-An Chou,et al.  Adaptive Seizure Onset Detection Framework Using a Hybrid PCA–CSP Approach , 2018, IEEE Journal of Biomedical and Health Informatics.

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

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

[39]  Alessio Burrello,et al.  An Ensemble of Hyperdimensional Classifiers: Hardware-Friendly Short-Latency Seizure Detection With Automatic iEEG Electrode Selection , 2020, IEEE Journal of Biomedical and Health Informatics.

[40]  Guanghong Gong,et al.  Detection Analysis of Epileptic EEG Using a Novel Random Forest Model Combined With Grid Search Optimization , 2019, Front. Hum. Neurosci..

[41]  Khan M. Iftekharuddin,et al.  Deep recurrent neural network for seizure detection , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[42]  Chavin Satirasethawong,et al.  Amplitude-integrated EEG processing and its performance for automatic seizure detection , 2015, 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).

[43]  K. Lehnertz,et al.  Seizure prediction — ready for a new era , 2018, Nature Reviews Neurology.

[44]  Qiang Cheng,et al.  Automated Classification of Seizures against Nonseizures: A Deep Learning Approach , 2019, ArXiv.

[45]  O. Farooq,et al.  Automated seizure detection in scalp EEG using multiple wavelet scales , 2012, 2012 IEEE International Conference on Signal Processing, Computing and Control.

[46]  Saeed Mian Qaisar,et al.  Effective epileptic seizure detection by using level-crossing EEG sampling sub-bands statistical features selection and machine learning for mobile healthcare , 2021, Comput. Methods Programs Biomed..

[47]  Kebin Jia,et al.  A Multi-View Deep Learning Framework for EEG Seizure Detection , 2019, IEEE Journal of Biomedical and Health Informatics.