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Tajadini B, Seydnejad SR, Rezakhani S. Short-term epileptic seizures prediction based on cepstrum analysis and signal morphology. BMC Biomed Eng 2024; 6:6. [PMID: 38946007 PMCID: PMC11215831 DOI: 10.1186/s42490-024-00081-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 04/29/2024] [Indexed: 07/02/2024] Open
Abstract
This article aims to provide and implement a patient-specific seizure (for Intervention Time (IT) detection) prediction algorithm using non-invasive data to develop warning devices to prevent further patient injury and reduce stress. Employing algorithms with high initial data volume and computations time to increase the accuracy is an important problem in prediction issues. Consequently, reduction of calculations is met by applying only two effective EEG signal channels without manual removal of artifacts by visual inspection as the algorithm's input. Autoregression (AR) modeling and Cepstrum detect changes due to IT period. We carry out the goal of higher accuracy by increasing sensitivity to interictal epileptiform discharges or artifacts and reduce errors caused by them, taking advantage of the discrete wavelet transform and the comparison of two channels epochs by applying the median filter. Averaging and positive envelope methods are introduced to patient-specific thresholds become more differentiated as soon as possible and can be lead to sooner prediction. We examined this method on a mathematical model of adult epilepsy as well as on 10 patients with EEG data. The results of our experiments confirm that performance of the proposed approach in accuracy and average false prediction rate is superior to other algorithms. Simulation results have been shown the robustness of our proposed method to artifacts and errors, which is a step towards the development of real-time alarm devices by non-invasive techniques.
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Affiliation(s)
- Bahar Tajadini
- Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Saeid R Seydnejad
- Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
| | - Soheila Rezakhani
- Department of Neurology, Neuroscience Research Center, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran
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Brogin JAF, Faber J, Reyes-Garcia SZ, Cavalheiro EA, Bueno DD. Epileptic seizure suppression: A computational approach for identification and control using real data. PLoS One 2024; 19:e0298762. [PMID: 38416729 PMCID: PMC10901337 DOI: 10.1371/journal.pone.0298762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 01/31/2024] [Indexed: 03/01/2024] Open
Abstract
Epilepsy affects millions of people worldwide every year and remains an open subject for research. Current development on this field has focused on obtaining computational models to better understand its triggering mechanisms, attain realistic descriptions and study seizure suppression. Controllers have been successfully applied to mitigate epileptiform activity in dynamic models written in state-space notation, whose applicability is, however, restricted to signatures that are accurately described by them. Alternatively, autoregressive modeling (AR), a typical data-driven tool related to system identification (SI), can be directly applied to signals to generate more realistic models, and since it is inherently convertible into state-space representation, it can thus be used for the artificial reconstruction and attenuation of seizures as well. Considering this, the first objective of this work is to propose an SI approach using AR models to describe real epileptiform activity. The second objective is to provide a strategy for reconstructing and mitigating such activity artificially, considering non-hybrid and hybrid controllers - designed from ictal and interictal events, respectively. The results show that AR models of relatively low order represent epileptiform activities fairly well and both controllers are effective in attenuating the undesired activity while simultaneously driving the signal to an interictal condition. These findings may lead to customized models based on each signal, brain region or patient, from which it is possible to better define shape, frequency and duration of external stimuli that are necessary to attenuate seizures.
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Affiliation(s)
- João A. F. Brogin
- Department of Mechanical Engineering, São Paulo State University (UNESP), School of Engineering of Ilha Solteira, Ilha Solteira, São Paulo, Brazil
| | - Jean Faber
- Department of Neurology and Neurosurgery, Federal University of São Paulo (UNIFESP), São Paulo, São Paulo, Brazil
| | - Selvin Z. Reyes-Garcia
- Departamento de Ciencias Morfológicas, Facultad de Ciencias Médicas, Universidad Nacional Autónoma de Honduras, Tegucigalpa, Honduras
| | - Esper A. Cavalheiro
- Department of Neurology and Neurosurgery, Federal University of São Paulo (UNIFESP), São Paulo, São Paulo, Brazil
| | - Douglas D. Bueno
- Department of Mathematics, São Paulo State University (UNESP), School of Engineering of Ilha Solteira, Ilha Solteira, São Paulo, Brazil
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Wang Z, Hou S, Xiao T, Zhang Y, Lv H, Li J, Zhao S, Zhao Y. Lightweight Seizure Detection Based on Multi-Scale Channel Attention. Int J Neural Syst 2023; 33:2350061. [PMID: 37845193 DOI: 10.1142/s0129065723500612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2023]
Abstract
Epilepsy is one kind of neurological disease characterized by recurring seizures. Recurrent seizures can cause ongoing negative mental and cognitive damage to the patient. Therefore, timely diagnosis and treatment of epilepsy are crucial for patients. Manual electroencephalography (EEG) signals analysis is time and energy consuming, making automatic detection using EEG signals particularly important. Many deep learning algorithms have thus been proposed to detect seizures. These methods rely on expensive and bulky hardware, which makes them unsuitable for deployment on devices with limited resources due to their high demands on computer resources. In this paper, we propose a novel lightweight neural network for seizure detection using pure convolutions, which is composed of inverted residual structure and multi-scale channel attention mechanism. Compared with other methods, our approach significantly reduces the computational complexity, making it possible to deploy on low-cost portable devices for seizures detection. We conduct experiments on the CHB-MIT dataset and achieves 98.7% accuracy, 98.3% sensitivity and 99.1% specificity with 2.68[Formula: see text]M multiply-accumulate operations (MACs) and only 88[Formula: see text]K parameters.
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Affiliation(s)
- Ziwei Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Sujuan Hou
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Tiantian Xiao
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Yongfeng Zhang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Hongbin Lv
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Jiacheng Li
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Shanshan Zhao
- Department of Hematology, Heze Hospital of Traditional Chinese Medicine, Heze 274000, P. R. China
| | - Yanna Zhao
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
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Martineau T, He S, Vaidyanathan R, Tan H. Hyper-parameter tuning and feature extraction for asynchronous action detection from sub-thalamic nucleus local field potentials. Front Hum Neurosci 2023; 17:1111590. [PMID: 37292583 PMCID: PMC10244770 DOI: 10.3389/fnhum.2023.1111590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 05/04/2023] [Indexed: 06/10/2023] Open
Abstract
Introduction Decoding brain states from subcortical local field potentials (LFPs) indicative of activities such as voluntary movement, tremor, or sleep stages, holds significant potential in treating neurodegenerative disorders and offers new paradigms in brain-computer interface (BCI). Identified states can serve as control signals in coupled human-machine systems, e.g., to regulate deep brain stimulation (DBS) therapy or control prosthetic limbs. However, the behavior, performance, and efficiency of LFP decoders depend on an array of design and calibration settings encapsulated into a single set of hyper-parameters. Although methods exist to tune hyper-parameters automatically, decoders are typically found through exhaustive trial-and-error, manual search, and intuitive experience. Methods This study introduces a Bayesian optimization (BO) approach to hyper-parameter tuning, applicable through feature extraction, channel selection, classification, and stage transition stages of the entire decoding pipeline. The optimization method is compared with five real-time feature extraction methods paired with four classifiers to decode voluntary movement asynchronously based on LFPs recorded with DBS electrodes implanted in the subthalamic nucleus of Parkinson's disease patients. Results Detection performance, measured as the geometric mean between classifier specificity and sensitivity, is automatically optimized. BO demonstrates improved decoding performance from initial parameter setting across all methods. The best decoders achieve a maximum performance of 0.74 ± 0.06 (mean ± SD across all participants) sensitivity-specificity geometric mean. In addition, parameter relevance is determined using the BO surrogate models. Discussion Hyper-parameters tend to be sub-optimally fixed across different users rather than individually adjusted or even specifically set for a decoding task. The relevance of each parameter to the optimization problem and comparisons between algorithms can also be difficult to track with the evolution of the decoding problem. We believe that the proposed decoding pipeline and BO approach is a promising solution to such challenges surrounding hyper-parameter tuning and that the study's findings can inform future design iterations of neural decoders for adaptive DBS and BCI.
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Affiliation(s)
- Thomas Martineau
- Biomechatronics Group, Department of Mechanical Engineering, Imperial College London, London, United Kingdom
| | - Shenghong He
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Ravi Vaidyanathan
- Biomechatronics Group, Department of Mechanical Engineering, Imperial College London, London, United Kingdom
- UK Dementia Research Institute-Care Research and Technology, Imperial College London, London, United Kingdom
| | - Huiling Tan
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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Massoud YM, Abdelzaher M, Kuhlmann L, Abd El Ghany MA. General and patient-specific seizure classification using deep neural networks. ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING 2023. [DOI: 10.1007/s10470-023-02153-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 01/04/2023] [Accepted: 02/22/2023] [Indexed: 09/02/2023]
Abstract
AbstractSeizure prediction algorithms have been central in the field of data analysis for the improvement of epileptic patients’ lives. The most recent advancements of which include the use of deep neural networks to present an optimized, accurate seizure prediction system. This work puts forth deep learning methods to automate the process of epileptic seizure detection with electroencephalogram (EEG) signals as input; both a patient-specific and general approach are followed. EEG signals are time structure series motivating the use of sequence algorithms such as temporal convolutional neural networks (TCNNs), and long short-term memory networks. We then compare this methodology to other prior pre-implemented structures, including our previous work for seizure prediction using machine learning approaches support vector machine and random under-sampling boost. Moreover, patient-specific and general seizure prediction approaches are used to evaluate the performance of the best algorithms. Area under curve (AUC) is used to select the best performing algorithm to account for the imbalanced dataset. The presented TCNN model showed the best patient-specific results than that of the general approach with, AUC of 0.73, while ML model had the best results for general classification with AUC of 0.75.
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Nazari J, Motie Nasrabadi A, Menhaj MB, Raiesdana S. Epilepsy seizure prediction with few-shot learning method. Brain Inform 2022; 9:21. [PMID: 36112246 PMCID: PMC9481757 DOI: 10.1186/s40708-022-00170-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 08/23/2022] [Indexed: 11/27/2022] Open
Abstract
Epileptic seizures prediction and timely alarms allow the patient to take effective and preventive actions. In this paper, a convolutional neural network (CNN) is proposed to diagnose the preictal period. Our goal is for those epileptic patients in whom seizures occur late and it is very challenging to record the preictal signal for them. In the previous works, generalized methods were inevitably used for this group of patients which were not very accurate. Our approach to solve this problem is to provide a few-shot learning method. This method, having the previous knowledge, is trained with only a small number of samples, learns new tasks and reduces the efforts to collect more data. Evaluation results for three patients from the CHB–MIT database, for a 10-min seizure prediction horizon (SPH) and a 20-min seizure occurrence period (SOP), averaged sensitivity of 95.70% and a false prediction rate (FPR) of 0.057/h and for the 5-min prediction horizon and the 25-min seizure occurrence period averaged sensitivity of 98.52% and a false prediction rate of (FPR) of 0.045/h. The proposed few-shot learning method, based on previous knowledge gained from the generalizable method, is regulated with a few new patient samples for the patient. Our results show that the accuracy obtained in this method is higher than the generalizable methods.
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Gao Y, Liu A, Cui X, Qian R, Chen X. A general sample-weighted framework for epileptic seizure prediction. Comput Biol Med 2022; 150:106169. [PMID: 36252368 DOI: 10.1016/j.compbiomed.2022.106169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/16/2022] [Accepted: 10/01/2022] [Indexed: 11/18/2022]
Abstract
OBJECTIVE Effective epileptic seizure prediction can make the patients know the onset of the seizure in advance to take timely preventive measures. Many studies based on machine learning methods have been proposed to tackle this problem and achieve significant progress in recent years. However, most studies treat each EEG training sample's contribution to the model as equal, while different samples have different predictive effects on epileptic seizures (e.g., preictal samples from different times). To this end, in this paper, we propose a general sample-weighted framework for patient-specific epileptic seizure prediction. METHODS Specifically, we define the mapping from the sample weights of training sets to the performance of the validation sets as the fitness function to be optimized. Then, the genetic algorithm is employed to optimize this fitness function and obtain the optimal sample weights. Finally, we obtain the final model by using the training sets with optimized sample weights. RESULTS To evaluate the effectiveness of our framework, we conduct extensive experiments on both traditional machine learning methods and prevalent deep learning methods. Our framework can significantly improve performance based on these methods. Among them, our framework based on Transformer achieves an average sensitivity of 94.6%, an average false prediction rate of 0.06/h, and an average AUC of 0.939 in 12 pediatric patients from the CHB-MIT database with the leave-one-out method, which outperforms the state-of-the-art methods. CONCLUSION This study provides new insights into the field of epileptic seizure prediction by considering the discrepancies between EEG samples. Moreover, we develop a general sample-weighted framework, which applies to almost all classical classification methods and can significantly improve performance based on these methods.
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Affiliation(s)
- Yikai Gao
- Epilepsy Center, Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China; School of Information Science and Technology, University of Science and Technology of China (USTC), Hefei 230027, China
| | - Aiping Liu
- School of Information Science and Technology, University of Science and Technology of China (USTC), Hefei 230027, China; USTC IAT-Huami Joint Laboratory for Brain-Machine Intelligence, Institute of Advanced Technology, University of Science and Technology of China, Hefei 230088, China.
| | - Xinrui Cui
- School of Information Science and Technology, University of Science and Technology of China (USTC), Hefei 230027, China
| | - Ruobing Qian
- Epilepsy Center, Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China
| | - Xun Chen
- Epilepsy Center, Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China; School of Information Science and Technology, University of Science and Technology of China (USTC), Hefei 230027, China; USTC IAT-Huami Joint Laboratory for Brain-Machine Intelligence, Institute of Advanced Technology, University of Science and Technology of China, Hefei 230088, China.
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Li C, Lammie C, Dong X, Amirsoleimani A, Azghadi MR, Genov R. Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022; 16:609-625. [PMID: 35737626 DOI: 10.1109/tbcas.2022.3185584] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly. However, despite significant performance improvements, their hardware implementation using conventional technologies, such as Complementary Metal-Oxide-Semiconductor (CMOS), in power and area-constrained settings remains a challenging task; especially when many recording channels are used. In this paper, we propose a novel low-latency parallel Convolutional Neural Network (CNN) architecture that has between 2-2,800x fewer network parameters compared to State-Of-The-Art (SOTA) CNN architectures and achieves 5-fold cross validation accuracy of 99.84% for epileptic seizure detection, and 99.01% and 97.54% for epileptic seizure prediction, when evaluated using the University of Bonn Electroencephalogram (EEG), CHB-MIT and SWEC-ETHZ seizure datasets, respectively. We subsequently implement our network onto analog crossbar arrays comprising Resistive Random-Access Memory (RRAM) devices, and provide a comprehensive benchmark by simulating, laying out, and determining hardware requirements of the CNN component of our system. We parallelize the execution of convolution layer kernels on separate analog crossbars to enable 2 orders of magnitude reduction in latency compared to SOTA hybrid Memristive-CMOS Deep Learning (DL) accelerators. Furthermore, we investigate the effects of non-idealities on our system and investigate Quantization Aware Training (QAT) to mitigate the performance degradation due to low Analog-to-Digital Converter (ADC)/Digital-to-Analog Converter (DAC) resolution. Finally, we propose a stuck weight offsetting methodology to mitigate performance degradation due to stuck [Formula: see text] memristor weights, recovering up to 32% accuracy, without requiring retraining. The CNN component of our platform is estimated to consume approximately 2.791 W of power while occupying an area of 31.255 mm2 in a 22 nm FDSOI CMOS process.
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Merk T, Peterson V, Köhler R, Haufe S, Richardson RM, Neumann WJ. Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation. Exp Neurol 2022; 351:113993. [PMID: 35104499 PMCID: PMC10521329 DOI: 10.1016/j.expneurol.2022.113993] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 11/18/2021] [Accepted: 01/22/2022] [Indexed: 12/30/2022]
Abstract
Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.
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Affiliation(s)
- Timon Merk
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany
| | - Victoria Peterson
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, United States
| | - Richard Köhler
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany
| | - Stefan Haufe
- Berlin Center for Advanced Neuroimaging (BCAN), Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, United States
| | - Wolf-Julian Neumann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany.
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Yu Z, Albera L, Jeannes RLB, Kachenoura A, Karfoul A, Yang C, Shu H. Epileptic Seizure Prediction Using Deep Neural Networks via Transfer Learning and Multi-Feature Fusion. Int J Neural Syst 2022; 32:2250032. [DOI: 10.1142/s0129065722500320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Gao Y, Chen X, Liu A, Liang D, Wu L, Qian R, Xie H, Zhang Y. Pediatric Seizure Prediction in Scalp EEG Using a Multi-Scale Neural Network With Dilated Convolutions. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022; 10:4900209. [PMID: 35356539 PMCID: PMC8936768 DOI: 10.1109/jtehm.2022.3144037] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 12/15/2021] [Accepted: 01/03/2022] [Indexed: 11/09/2022]
Abstract
Objective: Epileptic seizure prediction based on scalp electroencephalogram (EEG) is of great significance for improving the quality of life of patients with epilepsy. In recent years, a number of studies based on deep learning methods have been proposed to address this issue and achieve excellent performance. However, most studies on epileptic seizure prediction by EEG fail to take full advantage of temporal-spatial multi-scale features of EEG signals, while EEG signals carry information in multiple temporal and spatial scales. To this end, in this study, we proposed an end-to-end framework by using a temporal-spatial multi-scale convolutional neural network with dilated convolutions for patient-specific seizure prediction. Methods: Specifically, the model divides the EEG processing pipeline into two stages: the temporal multi-scale stage and the spatial multi-scale stage. In each stage, we firstly extract the multi-scale features along the corresponding dimension. A dilated convolution block is then conducted on these features to expand our model's receptive fields further and systematically aggregate global information. Furthermore, we adopt a feature-weighted fusion strategy based on an attention mechanism to achieve better feature fusion and eliminate redundancy in the dilated convolution block. Results: The proposed model obtains an average sensitivity of 93.3%, an average false prediction rate of 0.007 per hour, and an average proportion of time-in-warning of 6.3% testing in 16 patients from the CHB-MIT dataset with the leave-one-out method. Conclusion: Our model achieves superior performance in comparison to state-of-the-art methods, providing a promising solution for EEG-based seizure prediction.
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Affiliation(s)
- Yikai Gao
- School of Information Science and TechnologyUniversity of Science and Technology of China (USTC) Hefei 230027 China
| | - Xun Chen
- Epilepsy Center, Department of NeurosurgeryThe First Affiliated Hospital of USTC, Division of Life Sciences and MedicineUniversity of Science and Technology of China, Hefei Anhui 230001 China.,USTC IAT-Huami Joint Laboratory for Brain-Machine IntelligenceInstitute of Advanced Technology, University of Science and Technology of China Hefei 230088 China
| | - Aiping Liu
- School of Information Science and TechnologyUniversity of Science and Technology of China (USTC) Hefei 230027 China.,USTC IAT-Huami Joint Laboratory for Brain-Machine IntelligenceInstitute of Advanced Technology, University of Science and Technology of China Hefei 230088 China
| | - Deng Liang
- School of Information Science and TechnologyUniversity of Science and Technology of China (USTC) Hefei 230027 China
| | - Le Wu
- School of Information Science and TechnologyUniversity of Science and Technology of China (USTC) Hefei 230027 China
| | - Ruobing Qian
- Epilepsy Center, Department of NeurosurgeryThe First Affiliated Hospital of USTC, Division of Life Sciences and MedicineUniversity of Science and Technology of China, Hefei Anhui 230001 China
| | - Hongtao Xie
- School of Information Science and TechnologyUniversity of Science and Technology of China (USTC) Hefei 230027 China
| | - Yongdong Zhang
- School of Information Science and TechnologyUniversity of Science and Technology of China (USTC) Hefei 230027 China
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Ehrens D, Cervenka MC, Bergey GK, Jouny CC. Dynamic training of a novelty classifier algorithm for real-time detection of early seizure onset. Clin Neurophysiol 2022; 135:85-95. [PMID: 35065325 PMCID: PMC8857071 DOI: 10.1016/j.clinph.2021.12.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 11/19/2021] [Accepted: 12/26/2021] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To develop an adaptive framework for seizure detection in real-time that is practical to use in the Epilepsy Monitoring Unit (EMU) as a warning signal, and whose output helps characterize epileptiform activity. METHODS Our algorithm was tested on intracranial EEG from epilepsy patients admitted to the EMU for presurgical evaluation. Our framework uses a one-class Support Vector Machine (SVM) that is being trained dynamically according to past activity in all available channels to classify the novelty of the current activity. In this study we compared multiple configurations using a one-class SVM to assess if there is significance over specific neural features or electrode locations. RESULTS Our results show that the algorithm reaches a sensitivity of 87% for early-onset seizure detection and of 97.7% as a generic seizure detection. CONCLUSIONS Our algorithm is capable of running in real-time and achieving a high performance for early seizure-onset detection with a low false positive rate and robustness in detection of different type of seizure-onset patterns. SIGNIFICANCE This algorithm offers a solution to warning systems in the EMU as well as a tool for seizure characterization during post-hoc analysis of intracranial EEG data for surgical resection of the epileptogenic network.
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Affiliation(s)
- Daniel Ehrens
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Mackenzie C. Cervenka
- Department of Neurology, Johns Hopkins University School of
Medicine, Baltimore, MD, USA
| | - Gregory K. Bergey
- Department of Neurology, Johns Hopkins University School of
Medicine, Baltimore, MD, USA
| | - Christophe C. Jouny
- Department of Neurology, Johns Hopkins University School of
Medicine, Baltimore, MD, USA
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Cherian R, Kanaga EG. Theoretical and Methodological Analysis of EEG based Seizure Detection and Prediction: An Exhaustive Review. J Neurosci Methods 2022; 369:109483. [DOI: 10.1016/j.jneumeth.2022.109483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 01/13/2022] [Accepted: 01/13/2022] [Indexed: 02/07/2023]
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Moharamzadeh N, Motie Nasrabadi A. A fuzzy sensitivity analysis approach to estimate brain effective connectivity and its application to epileptic seizure detection. BIOMED ENG-BIOMED TE 2021; 67:19-32. [PMID: 34953180 DOI: 10.1515/bmt-2021-0058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 11/26/2021] [Indexed: 11/15/2022]
Abstract
The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach to introduce a connectivity measure for detecting causal interactions between interactive input time series. The results of simulations indicate that this method is successful in detecting causal connectivity. After validating the performance of the proposed method on synthetic linear and nonlinear interconnected time series, it is applied to epileptic intracranial Electroencephalography (EEG) signals. The result of applying the proposed method on Freiburg epileptic intracranial EEG data recorded during seizure shows that the proposed method is capable of discriminating between the seizure and non-seizure states of the brain.
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Affiliation(s)
- Nader Moharamzadeh
- Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
| | - Ali Motie Nasrabadi
- Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
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15
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Kipiński L, Kordecki W. Time-series analysis of trial-to-trial variability of MEG power spectrum during rest state, unattended listening, and frequency-modulated tones classification. J Neurosci Methods 2021; 363:109318. [PMID: 34400211 DOI: 10.1016/j.jneumeth.2021.109318] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 08/07/2021] [Accepted: 08/09/2021] [Indexed: 11/19/2022]
Abstract
BACKGROUND The nonstationarity of EEG/MEG signals is important for understanding the functioning of the human brain. From our previous research we know that short, 250-500-ms MEG signals are variance-nonstationary. The covariance of a stochastic process is mathematically associated with its spectral density, therefore we investigate how the spectrum of such nonstationary signals varies in time. NEW METHOD We analyse data from 148-channel MEG, which represent rest state, unattended listening, and frequency-modulated tones classification. We transform short-time MEG signals to the frequency domain and for the dominant frequencies of 8-12 Hz we prepare the time series representing their trial-to-trial variability. Then, we test them for level- and trend-stationarity, unit root, heteroscedasticity, and gaussianity, and propose ARMA-modelling for their description. RESULTS The analysed time series have weak-stationarity properties independently of the functional state of the brain and channel localization. Only a small percentage of them, mostly related to the cognitive task, reveal nonstationarity. The obtained mathematical models show that the spectral density of the analysed signals depends on only two to three previous trials. COMPARISON WITH EXISTING METHODS The presented method has limitations related to FFT resolution and univariate models, but it is computationally simple and allows obtaining a low-complex stochastic model of the EEG/MEG spectrum variability. CONCLUSIONS Although physiological short-time MEG signals are in principle nonstationary in time, their power spectrum at the dominant (alpha) frequencies varies as a weakly stationary process. The proposed methodology has possible applications in prediction of EEG/MEG spectral properties in theoretical and clinical neuroscience.
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Affiliation(s)
- Lech Kipiński
- Department of Pathophysiology, Wrocław Medical University, 50-367 Wrocław, Poland.
| | - Wojciech Kordecki
- The Witelon State University of Applied Sciences in Legnica, 59-220 Legnica, Poland.
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16
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17
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Assali I, Jlassi I, Aissi M, Blaiech AG, Carrère M, Bedoui MH. Comparison by multivariate auto-regressive method of seizure prediction for real patients and virtual patients. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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18
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A novel time-varying modeling and signal processing approach for epileptic seizure detection and classification. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05330-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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19
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Yu PN, Liu CY, Heck CN, Berger TW, Song D. A sparse multiscale nonlinear autoregressive model for seizure prediction. J Neural Eng 2021; 18. [PMID: 33470981 DOI: 10.1088/1741-2552/abdd43] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 01/19/2021] [Indexed: 11/11/2022]
Abstract
Objectives.Accurate seizure prediction is highly desirable for medical interventions such as responsive electrical stimulation. We aim to develop a classification model that can predict seizures by identifying preictal states, i.e. the precursor of a seizure, based on multi-channel intracranial electroencephalography (iEEG) signals.Approach.A two-level sparse multiscale classification model was developed to classify interictal and preictal states from iEEG data. In the first level, short time-scale linear dynamical features were extracted as autoregressive (AR) model coefficients; arbitrary (usually long) time-scale linear and nonlinear dynamical features were extracted as Laguerre-Volterra AR model coefficients; root-mean-square error of model prediction was used as a feature representing model unpredictability. In the second level, all features were fed into a sparse classifier to discriminate the iEEG data between interictal and preictal states.Main results. The two-level model can accurately classify seizure states using iEEG data recorded from ten canine and human subjects. Adding arbitrary (usually long) time-scale and nonlinear features significantly improves model performance compared with the conventional AR modeling approach. There is a high degree of variability in the types of features contributing to seizure prediction across different subjects.Significance. This study suggests that seizure generation may involve distinct linear/nonlinear dynamical processes caused by different underlying neurobiological mechanisms. It is necessary to build patient-specific classification models with a wide range of dynamical features.
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Affiliation(s)
- Pen-Ning Yu
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, United States of America
| | - Charles Y Liu
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, United States of America.,Department of Neurological Surgery, University of Southern California, Los Angeles, CA 90033, United States of America.,Department of Neurology, University of Southern California, Los Angeles, CA 90033, United States of America.,USC Neurorestoration Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America.,Rancho Los Amigos National Rehabilitation Center, Downey, CA, 90242, United States of America
| | - Christianne N Heck
- Department of Neurology, University of Southern California, Los Angeles, CA 90033, United States of America.,USC Neurorestoration Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Theodore W Berger
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, United States of America
| | - Dong Song
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, United States of America
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20
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Ma D, Yuan S, Shang J, Liu J, Dai L, Kong X, Xu F. The Automatic Detection of Seizure Based on Tensor Distance And Bayesian Linear Discriminant Analysis. Int J Neural Syst 2021; 31:2150006. [PMID: 33522459 DOI: 10.1142/s0129065721500064] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Electroencephalogram (EEG) plays an important role in recording brain activity to diagnose epilepsy. However, it is not only laborious, but also not very cost effective for medical experts to manually identify the features on EEG. Therefore, automatic seizure detection in accordance with the EEG recordings is significant for the diagnosis and treatment of epilepsy. Here, a new method for detecting seizures using tensor distance (TD) is proposed. First, the time-frequency characteristics of EEG signals are obtained by wavelet transformation, and the tensor representation of EEG signals is then obtained. Tucker decomposition is used to obtain the principal components of the EEG tensor. After, the distances between different categories of EEG tensors are calculated as the EEG features. Finally, the TD features are classified through the Bayesian Linear Discriminant Analysis (Bayesian LDA) classifier. The performance of this method is measured by the sensitivity, specificity, and recognition accuracy. Results indicate 95.12% sensitivity, 97.60% specificity, 97.60% recognition accuracy, and a false detection rate of 0.76 per hour in the invasive EEG dataset, which included 566.57[Formula: see text]h of EEG recording data from 21 patients. Taken together, the results show that TD has a good detection effect for seizure classification and that this method has high computational speed and great potential for real-time diagnosis.
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Affiliation(s)
- Delu Ma
- School of Computer Science, Qufu Normal University, Rizhao, Shandong 276826, P. R. China
| | - Shasha Yuan
- School of Computer Science, Qufu Normal University, Rizhao, Shandong 276826, P. R. China
| | - Junliang Shang
- School of Computer Science, Qufu Normal University, Rizhao, Shandong 276826, P. R. China
| | - Jinxing Liu
- School of Computer Science, Qufu Normal University, Rizhao, Shandong 276826, P. R. China
| | - Lingyun Dai
- School of Computer Science, Qufu Normal University, Rizhao, Shandong 276826, P. R. China
| | - Xiangzhen Kong
- School of Computer Science, Qufu Normal University, Rizhao, Shandong 276826, P. R. China
| | - Fangzhou Xu
- Department of Physics, School of Electronic and Information Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250353, P. R. China
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21
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Sharp decrease in the Laplacian matrix rank of phase-space graphs: a potential biomarker in epilepsy. Cogn Neurodyn 2021; 15:649-659. [PMID: 34367366 DOI: 10.1007/s11571-020-09662-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 11/01/2020] [Accepted: 12/29/2020] [Indexed: 10/22/2022] Open
Abstract
In this paper, phase space reconstruction from stereo-electroencephalography data of ten patients with focal epilepsy forms a series of graphs. Those obtained graphs reflect the transition characteristics of brain dynamical system from pre-seizure to seizure of epilepsy. Interestingly, it is found that the rank of Laplacian matrix of these graphs has a sharp decrease when a seizure is close to happen, which thus might be viewed as a new potential biomarker in epilepsy. In addition, the reliability of this method is numerically verified with a coupled mass neural model. In particular, our simulation suggests that this potential biomarker can play the roles of predictive effect or delayed awareness, depending on the bias current of the Gaussian noise. These results may give new insights into the seizure detection.
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Qi Y, Ding L, Wang Y, Pan G. Learning Robust Features from Nonstationary Brain Signals by Multi-Scale Domain Adaptation Networks for Seizure Prediction. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2021.3100270] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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23
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24
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Bhanot N, Mariyappa N, Anitha H, Bhargava GK, Velmurugan J, Sinha S. Seizure detection and epileptogenic zone localisation on heavily skewed MEG data using RUSBoost machine learning technique. Int J Neurosci 2020; 132:963-974. [PMID: 33272081 DOI: 10.1080/00207454.2020.1858828] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Background: Epilepsy is a neurological disorder which is characterised by recurrent and involuntary seizures. Magnetoencephalography (MEG) is clinically used as a presurgical tool in locating the epileptogenic zone by localising either interictal epileptic discharges (IEDs) or ictal activities. The localisation of ictal onset provides reliable and more accurate seizure onset zones rather than localising the IEDs. Ictals or seizures are presently detected during MEG analysis by manually inspecting the recorded data. This is laborious when the duration of recordings is longer. Methods: We propose a novel method which uses statistical features such as short-time permutation entropy (STPE), gradient of STPE (GSTPE), short-time energy (STE) and short-time mean (STM) extracted from the ictal and interictal MEG data of drug resistant epilepsy patients group. Since the data is heavily skewed, the RUSBoost algorithm with k-fold cross-validation is used to classify the data into ictal and interictal by using the four feature vectors. This method is further used for localising the epileptogenic region using region-specific classifications by means of the RUSBoost algorithm. Results: The accuracy obtained for seizure detection is 93.4%. The specificity and sensitivity for the same are 93%. The localisation accuracies for each lobe are in the range of 88.1-99.1%. Discussion: Through this ictus detection method, the current scenario of laborious inspection of the ictal MEG can be reduced. The proposed system, thus, can be implemented in real-time as a better and more efficient method for seizure detection and further it can prove to be highly beneficial for patients and health-care professionals during real-time MEG recording. Furthermore, the identification of the epileptogenic lobe can provide clinicians with useful insights, and a pre-cursor for source localisation.
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Affiliation(s)
- Nipun Bhanot
- Manipal Institute of Technology, Electronics and Communication, Manipal, India
| | | | - H Anitha
- Manipal Institute of Technology, Electronics and Communication, Manipal, India
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25
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Zhang Y, Yang R, Zhou W. Roughness-Length-Based Characteristic Analysis of Intracranial EEG and Epileptic Seizure Prediction. Int J Neural Syst 2020; 30:2050072. [DOI: 10.1142/s0129065720500720] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
To identify precursors of epileptic seizures, an EEG characteristic analysis is carried out based on a roughness-length method, where fractal dimensions and intercept values are extracted to measure the structure complexity and the amplitude roughness of EEG signals in different phases. Using the significant changes of the fractal dimension and intercept in the preictal phase with respect to those in the interictal phase, a patient-specific seizure prediction algorithm is then proposed by combining with a gradient boosting classifier. The probabilistic outputs of the trained gradient boosting classifier are further processed by threshold comparison and rule-based judgment to distinguish preictal EEG from interictal EEG and to generate seizure alerts. The prediction algorithm was evaluated on 20 patients’ intracranial EEG recordings from the Freiburg EEG database, which contains the preictal periods of 65 seizures and 499[Formula: see text]h interictal EEG. Setting the seizure prediction horizon as 2[Formula: see text]min, averaged sensitivity values of 90.42% and 91.67% with averaged false prediction rates of 0.12/h and 0.10/h were achieved for seizure occurrence periods of 30 and 50[Formula: see text]min, respectively. These results demonstrate the ability of fractal dimension and intercept metrics in predicting the occurrence of seizures.
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Affiliation(s)
- Yanli Zhang
- School of Information and Electronic Engineering, Shandong Technology and Business University, 191 Binhai Middle Road, Yantai 264005, P. R. China
| | - Rendi Yang
- School of Electromechanical and Automotive Engineering, Yantai University, Yantai 264005, P. R. China
| | - Weidong Zhou
- School of Microelectronics, Shandong University, Jinan 250100, P. R. China
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26
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EEG Synchronization Analysis for Seizure Prediction: A Study on Data of Noninvasive Recordings. Processes (Basel) 2020. [DOI: 10.3390/pr8070846] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Objective: Epilepsy is a neurological disorder arising from anomalies of the electrical activity in the brain, affecting ~65 million individuals worldwide. Prediction methods, typically based on machine learning methods, require a large amount of data for training, in order to correctly classify seizures with small false alarm rates. Methods: In this work, we present a new database containing EEG scalp signals of 14 epileptic patients acquired at the Unit of Neurology and Neurophysiology of the University of Siena, Italy. Furthermore, a patient-specific seizure prediction method, based on the detection of synchronization patterns in the EEG, is proposed and tested on the data of the database. The use of noninvasive EEG data aims to explore the possibility of developing a noninvasive monitoring/control device for the prediction of seizures. The prediction method employs synchronization measures computed over all channel pairs and a computationally inexpensive threshold-based classification approach. Results and conclusions: The experimental analysis, performed by inspection and by the proposed threshold-based classifier on all the patients of the database, shows that the features extracted by the synchronization measures are able to detect preictal and ictal states and allow the prediction of the seizures few minutes before the seizure onsets.
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27
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Ayodele K, Ikezogwo W, Komolafe M, Ogunbona P. Supervised domain generalization for integration of disparate scalp EEG datasets for automatic epileptic seizure detection. Comput Biol Med 2020; 120:103757. [DOI: 10.1016/j.compbiomed.2020.103757] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Revised: 04/07/2020] [Accepted: 04/07/2020] [Indexed: 11/24/2022]
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28
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Büyükçakır B, Elmaz F, Mutlu AY. Hilbert Vibration Decomposition-based epileptic seizure prediction with neural network. Comput Biol Med 2020; 119:103665. [DOI: 10.1016/j.compbiomed.2020.103665] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 02/14/2020] [Accepted: 02/14/2020] [Indexed: 12/31/2022]
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29
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Li Y, Yu Z, Chen Y, Yang C, Li Y, Allen Li X, Li B. Automatic Seizure Detection using Fully Convolutional Nested LSTM. Int J Neural Syst 2020; 30:2050019. [DOI: 10.1142/s0129065720500197] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
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.
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Affiliation(s)
- Yang Li
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, P. R. China
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, P. R. China
| | - Zuyi Yu
- School of Information Science and Engineering, Shandong University, Jinan, Shandong 250100, P. R. China
| | - Yang Chen
- Laboratory of Image Science and Technology, Southeast University, Nanjing, Jiangsu 210096, P. R. China
| | - Chunfeng Yang
- Laboratory of Image Science and Technology, Southeast University, Nanjing, Jiangsu 210096, P. R. China
| | - Yue Li
- School of Clinical Medicine, Dali University, Dali, Yunnan 671000, P. R. China
| | - X. Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Baosheng Li
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, P. R. China
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, P. R. China
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30
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A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning techniques. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101707] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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31
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Yao L, Brown P, Shoaran M. Improved detection of Parkinsonian resting tremor with feature engineering and Kalman filtering. Clin Neurophysiol 2020; 131:274-284. [PMID: 31744673 PMCID: PMC6927801 DOI: 10.1016/j.clinph.2019.09.021] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 07/25/2019] [Accepted: 09/10/2019] [Indexed: 11/23/2022]
Abstract
OBJECTIVE Accurate and reliable detection of tremor onset in Parkinson's disease (PD) is critical to the success of adaptive deep brain stimulation (aDBS) therapy. Here, we investigated the potential use of feature engineering and machine learning methods for more accurate detection of rest tremor in PD. METHODS We analyzed the local field potential (LFP) recordings from the subthalamic nucleus region in 12 patients with PD (16 recordings). To explore the optimal biomarkers and the best performing classifier, the performance of state-of-the-art machine learning (ML) algorithms and various features of the subthalamic LFPs were compared. We further used a Kalman filtering technique in feature domain to reduce the false positive rate. RESULTS The Hjorth complexity showed a higher correlation with tremor, compared to other features in our study. In addition, by optimal selection of a maximum of five features with a sequential feature selection method and using the gradient boosted decision trees as the classifier, the system could achieve an average F1 score of up to 88.7% and a detection lead of 0.52 s. The use of Kalman filtering in feature space significantly improved the specificity by 17.0% (p = 0.002), thereby potentially reducing the unnecessary power dissipation of the conventional DBS system. CONCLUSION The use of relevant features combined with Kalman filtering and machine learning improves the accuracy of tremor detection during rest. SIGNIFICANCE The proposed method offers a potential solution for efficient on-demand stimulation for PD tremor.
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Affiliation(s)
- Lin Yao
- ECE Department, Cornell University, Ithaca, NY, USA.
| | - Peter Brown
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
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Evaluation of potential auras in generalized epilepsy from EEG signals using deep convolutional neural networks and time-frequency representation. ACTA ACUST UNITED AC 2019; 65:379-391. [DOI: 10.1515/bmt-2019-0098] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Accepted: 10/07/2019] [Indexed: 01/02/2023]
Abstract
Abstract
The general uncertainty of epilepsy and its unpredictable seizures often affect badly the quality of life of people exposed to this disease. There are patients who can be considered fortunate in terms of prediction of any seizures. These are patients with epileptic auras. In this study, it was aimed to evaluate pre-seizure warning symptoms of the electroencephalography (EEG) signals by a convolutional neural network (CNN) inspired by the epileptic auras defined in the medical field. In this context, one-dimensional EEG signals were transformed into a spectrogram display form in the frequency-time domain by applying a short-time Fourier transform (STFT). Systemic changes in pre-epileptic seizure have been described by applying the CNN approach to the EEG signals represented in the image form, and the subjective EEG-Aura process has been tried to be determined for each patient. Considering all patients included in the evaluation, it was determined that the 1-min interval covering the time from the second minute to the third minute before the seizure had the highest mean and the lowest variance to determine the systematic changes before the seizure. Thus, the highest performing process is described as EEG-Aura. The average success for the EEG-Aura process was 90.38 ± 6.28%, 89.78 ± 8.34% and 90.47 ± 5.95% for accuracy, specificity and sensitivity, respectively. Through the proposed model, epilepsy patients who do not respond to medical treatment methods are expected to maintain their lives in a more comfortable and integrated way.
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Ozcan AR, Erturk S. Seizure Prediction in Scalp EEG Using 3D Convolutional Neural Networks With an Image-Based Approach. IEEE Trans Neural Syst Rehabil Eng 2019; 27:2284-2293. [DOI: 10.1109/tnsre.2019.2943707] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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34
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Ehrens D, Assaf F, Cowan NJ, Sarma SV, Schiller Y. Ultra Broad Band Neural Activity Portends Seizure Onset in a Rat Model of Epilepsy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:2276-2279. [PMID: 30440860 DOI: 10.1109/embc.2018.8512769] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Epilepsy affects over 70 million people worldwide and 30% of patients' seizures cannot be controlled with medications, motivating the development of alternative therapies such as electrical stimulation. Current stimulation strategies attempt to stop seizures after they start, but none aim to prevent seizures altogether. Preventing seizures requires knowing when the brain is entering a preictal state (i.e., approaching seizure onset). Here we show that such preictal activity can be detected by an informative neural signal that progressively and monotonically changes as the brain approaches a seizure event. Specifically, we use local field potentials (LFP) from a rat model of epilepsy to develop an innovative measure of signal novelty relative to nonseizure activity, that shows the presence of progressive neural dynamics in an ultra broad band (4 Hz - 5 kHz). The measure is extracted from functional connectivity features computed from the LFPs which are used as an input to a one-class Support Vector Machine (SVM). The SVM outputs a scalar signal which quantifies how novel the current activity looks relative to baseline (non-seizure) activity and shows a progression towards seizure onset minutes ahead of time. The use of ultra broad band multivariate features into the SVM results in a novelty signal that has a significantly higher slope in the progression to seizure onset when compared to using power in conventional frequency bands (4 - 500 Hz) on individual channels as input features to the SVM. Functional connectivity in conjunction with the SVM is a strategy that generates a new measurement of novelty that can be used by closed-loop systems for seizure forecasting and prevention.
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Usman SM, Khalid S, Akhtar R, Bortolotto Z, Bashir Z, Qiu H. Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: Review of available methodologies. Seizure 2019; 71:258-269. [DOI: 10.1016/j.seizure.2019.08.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 08/09/2019] [Accepted: 08/14/2019] [Indexed: 12/24/2022] Open
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Real-time epileptic seizure prediction based on online monitoring of pre-ictal features. Med Biol Eng Comput 2019; 57:2461-2469. [PMID: 31478133 DOI: 10.1007/s11517-019-02039-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Accepted: 08/21/2019] [Indexed: 10/26/2022]
Abstract
Reliable prediction of epileptic seizures is of prime importance as it can drastically change the quality of life for patients. This study aims to propose a real-time low computational approach for the prediction of epileptic seizures and to present an efficient hardware implementation of this approach for portable prediction systems. Three levels of feature extraction are performed to characterize the pre-ictal activities of the EEG signal. In the first-level, the line length algorithm is applied to the pre-ictal region. The features obtained in the first-level are mathematically integrated to extract the second-level features and then the line lengths of the second-level features are calculated to obtain our third-level feature. The third-level information is compared with predefined threshold levels to make a decision on whether the extracted characteristics are relevant to a seizure occurrence or not. The validity of this algorithm was tested by EEG recordings in the CHB-MIT database (97 seizures, 834.224 h) for 19 epileptic patients. The results showed that the average sensitivity was 90.62%, the specificity was 88.34%, the accuracy was 88.76% with the average false prediction rate as low as 0.0046 h-1, and the average prediction time was 23.3 min. The low computational complexity is the superiority of the proposed approach, which provides a technologically simple but accurate way of predicting epileptic seizures and enables hardware implantable devices. Graphical abstract Proposed seizure prediction algorithm and its features.
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Parhi KK, Zhang Z. Discriminative Ratio of Spectral Power and Relative Power Features Derived via Frequency-Domain Model Ratio With Application to Seizure Prediction. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2019; 13:645-657. [PMID: 31095498 DOI: 10.1109/tbcas.2019.2917184] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The ratio of spectral power in two different bands and relative band power have been shown to be sometimes more discriminative features than the spectral power in a specific band for binary classification of a time series for seizure prediction. However, why and which ratio of spectral power and relative power features are better discriminators than a band power have not been understood. While general answers to why and which are difficult, this paper partially addresses the answer to these questions. Using auto-regressive modeling, this paper, for the first time, theoretically explains that for high signal-to-noise ratio (SNR) cases, the ratio features may sometime amplify the discriminability of one of the two states in a time series, as compared with a band power. This paper, also for the first time, introduces a novel frequency-domain model ratio (FDMR) that can be used to select the two frequency bands. The FDMR computes the ratio of the frequency responses of the two auto-regressive model filters that correspond to two different states. It is shown that the ratio implicitly cancels the effect of change of variance of the white noise that is input to the auto-regressive model in a non-stationary environment for high SNR conditions. It is also shown that under certain sufficient but not necessary conditions, the ratio of the spectral power and the relative band power, i.e., the band power divided by the total power spectral density, can be better discriminators than band power. Synthesized data and scalp EEG data from the MIT Physionet for patient-specific seizure prediction are used to explain why the ratios of spectral power obtained by a ranking algorithm in the prior literature satisfy the sufficient conditions for amplification of the ratio feature derived in this paper.
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Yao L, Brown P, Shoaran M. Resting Tremor Detection in Parkinson's Disease with Machine Learning and Kalman Filtering. IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE : HEALTHCARE TECHNOLOGY : [PROCEEDINGS]. IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE 2019; 2018:BIOCAS.2018.8584721. [PMID: 31334499 PMCID: PMC6645988 DOI: 10.1109/biocas.2018.8584721] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Adaptive deep brain stimulation (aDBS) is an emerging method to alleviate the side effects and improve the efficacy of conventional open-loop stimulation for movement disorders. However, current adaptive DBS techniques are primarily based on single-feature thresholding, precluding an optimized delivery of stimulation for precise control of motor symptoms. Here, we propose to use a machine learning approach for resting-state tremor detection from local field potentials (LFPs) recorded from subthalamic nucleus (STN) in 12 Parkinson's patients. We compare the performance of state-of-the-art classifiers and LFP-based biomarkers for tremor detection, showing that the high-frequency oscillations and Hjorth parameters achieve a high discriminative performance. In addition, using Kalman filtering in the feature space, we show that the tremor detection performance significantly improves (F(1,15)=32.16, p<0.0001). The proposed method holds great promise for efficient on-demand delivery of stimulation in Parkinson's disease.
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Affiliation(s)
- Lin Yao
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
| | - Peter Brown
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Mahsa Shoaran
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
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Wang L, Long X, Aarts RM, van Dijk JP, Arends JB. EEG-based seizure detection in patients with intellectual disability: Which EEG and clinical factors are important? Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.12.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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40
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Sudalaimani C, Sivakumaran N, Elizabeth TT, Rominus VS. Automated detection of the preseizure state in EEG signal using neural networks. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2018.11.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Acharya UR, Hagiwara Y, Adeli H. Automated seizure prediction. Epilepsy Behav 2018; 88:251-261. [PMID: 30317059 DOI: 10.1016/j.yebeh.2018.09.030] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 09/16/2018] [Accepted: 09/22/2018] [Indexed: 11/16/2022]
Abstract
In the past two decades, significant advances have been made on automated electroencephalogram (EEG)-based diagnosis of epilepsy and seizure detection. A number of innovative algorithms have been introduced that can aid in epilepsy diagnosis with a high degree of accuracy. In recent years, the frontiers of computational epilepsy research have moved to seizure prediction, a more challenging problem. While antiepileptic medication can result in complete seizure freedom in many patients with epilepsy, up to one-third of patients living with epilepsy will have medically intractable epilepsy, where medications reduce seizure frequency but do not completely control seizures. If a seizure can be predicted prior to its clinical manifestation, then there is potential for abortive treatment to be given, either self-administered or via an implanted device administering medication or electrical stimulation. This will have a far-reaching impact on the treatment of epilepsy and patient's quality of life. This paper presents a state-of-the-art review of recent efforts and journal articles on seizure prediction. The technologies developed for epilepsy diagnosis and seizure detection are being adapted and extended for seizure prediction. The paper ends with some novel ideas for seizure prediction using the increasingly ubiquitous machine learning technology, particularly deep neural network machine learning.
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Affiliation(s)
- U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia
| | - Yuki Hagiwara
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Hojjat Adeli
- Department of Neuroscience, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, United States; Department of Neurology, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, United States; Department of Biomedical Informatics, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, United States.
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Detti P, de Lara GZM, Bruni R, Pranzo M, Sarnari F, Vatti G. A Patient-Specific Approach for Short-Term Epileptic Seizures Prediction Through the Analysis of EEG Synchronization. IEEE Trans Biomed Eng 2018; 66:1494-1504. [PMID: 30296211 DOI: 10.1109/tbme.2018.2874716] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Epilepsy is a neurological disorder arising from anomalies of the electrical activity in the brain, affecting about 65 millions individuals worldwide. OBJECTIVE This paper proposes a patient-specific approach for short-term prediction (i.e., within few minutes) of epileptic seizures. METHODS We use noninvasive EEG data, since the aim is exploring the possibility of developing a noninvasive monitoring/control device for the prediction of seizures. Our approach is based on finding synchronization patterns in the EEG that allow to distinguish in real time preictal from interictal states. In practice, we develop easily computable functions over a graph model to capture the variations in the synchronization, and employ a classifier for identifying the preictal state. RESULTS We compare two state-of-the-art classification algorithms and a simple and computationally inexpensive threshold-based classifier developed ad hoc. Results on publicly available scalp EEG database and on scalp data of the patients of the Unit of Neurology and Neurophysiology at University of Siena show that this simple and computationally viable processing is able to highlight the changes in synchronization when a seizure is approaching. CONCLUSION AND SIGNIFICANCE The proposed approach, characterized by low computational requirements and by the use of noninvasive techniques, is a step toward the development of portable and wearable devices for real-life use.
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Khan H, Marcuse L, Fields M, Swann K, Yener B. Focal Onset Seizure Prediction Using Convolutional Networks. IEEE Trans Biomed Eng 2018; 65:2109-2118. [DOI: 10.1109/tbme.2017.2785401] [Citation(s) in RCA: 137] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Yang Y, Zhou M, Niu Y, Li C, Cao R, Wang B, Yan P, Ma Y, Xiang J. Epileptic Seizure Prediction Based on Permutation Entropy. Front Comput Neurosci 2018; 12:55. [PMID: 30072886 PMCID: PMC6060283 DOI: 10.3389/fncom.2018.00055] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 06/28/2018] [Indexed: 11/23/2022] Open
Abstract
Epilepsy is a chronic non-communicable disorder of the brain that affects individuals of all ages. It is caused by a sudden abnormal discharge of brain neurons leading to temporary dysfunction. In this regard, if seizures could be predicted a reasonable period of time before their occurrence, epilepsy patients could take precautions against them and improve their safety and quality of life. However, the potential that permutation entropy(PE) can be applied in human epilepsy prediction from intracranial electroencephalogram (iEEG) recordings remains unclear. Here, we described the novel application of PE to track the dynamical changes of human brain activity from iEEG recordings for the epileptic seizure prediction. The iEEG signals of 19 patients were obtained from the Epilepsy Centre at the University Hospital of Freiburg. After preprocessing, PE was extracted in a sliding time window, and a support vector machine (SVM) was employed to discriminate cerebral state. Then a two-step post-processing method was applied for the purpose of prediction. The results showed that we obtained an average sensitivity (SS) of 94% and false prediction rates (FPR) with 0.111 h-1. The best results with SS of 100% and FPR of 0 h-1 were achieved for some patients. The average prediction horizon was 61.93 min, leaving sufficient treatment time before a seizure. These results indicated that applying PE as a feature to extract information and SVM for classification could predict seizures, and the presented method shows great potential in clinical seizure prediction for human.
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Affiliation(s)
- Yanli Yang
- College of Information and Computer Science, Taiyuan University of Technology, Taiyuan, China
| | - Mengni Zhou
- College of Information and Computer Science, Taiyuan University of Technology, Taiyuan, China
| | - Yan Niu
- College of Information and Computer Science, Taiyuan University of Technology, Taiyuan, China
| | - Conggai Li
- Centre for AI, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW, Australia
| | - Rui Cao
- Software College, Taiyuan University of Technology, Taiyuan, China
| | - Bin Wang
- College of Information and Computer Science, Taiyuan University of Technology, Taiyuan, China
| | - Pengfei Yan
- College of Information and Computer Science, Taiyuan University of Technology, Taiyuan, China
| | - Yao Ma
- College of Information and Computer Science, Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Information and Computer Science, Taiyuan University of Technology, Taiyuan, China
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Hosseini SA. A Hybrid Approach Based on Higher Order Spectra for Clinical Recognition of Seizure and Epilepsy Using Brain Activity. Basic Clin Neurosci 2018; 8:479-492. [PMID: 29942431 PMCID: PMC6010651 DOI: 10.29252/nirp.bcn.8.6.479] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
Introduction This paper proposes a reliable and efficient technique to recognize different epilepsy states, including healthy, interictal, and ictal states, using Electroencephalogram (EEG) signals. Methods The proposed approach consists of pre-processing, feature extraction by higher order spectra, feature normalization, feature selection by genetic algorithm and ranking method, and classification by support vector machine with Gaussian and polynomial radial basis function kernels. The proposed approach is validated on a public benchmark dataset to compare it with previous studies. Results The results indicate that the combined use of above elements can effectively decipher the cognitive process of epilepsy and seizure recognition. There are several bispectrum and bicoherence peaks at every bi-frequency plane, which reveal the location of the quadratic phase coupling. The proposed approach can reach, in almost all of the experiments, up to 100% performance in terms of sensitivity, specificity, and accuracy. Conclusion Comparing between the obtained results and previous approaches approves the effectiveness of the proposed approach for seizure and epilepsy recognition.
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Affiliation(s)
- Seyyed Abed Hosseini
- Research Center of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
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Perera H, Shiratuddin MF, Wong KW. Review of EEG-based pattern classification frameworks for dyslexia. Brain Inform 2018; 5:4. [PMID: 29904812 PMCID: PMC6094381 DOI: 10.1186/s40708-018-0079-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Accepted: 04/18/2018] [Indexed: 12/04/2022] Open
Abstract
Dyslexia is a disability that causes difficulties in reading and writing despite average intelligence. This hidden disability often goes undetected since dyslexics are normal and healthy in every other way. Electroencephalography (EEG) is one of the upcoming methods being researched for identifying unique brain activation patterns in dyslexics. The aims of this paper are to examine pros and cons of existing EEG-based pattern classification frameworks for dyslexia and recommend optimisations through the findings to assist future research. A critical analysis of the literature is conducted focusing on each framework’s (1) data collection, (2) pre-processing, (3) analysis and (4) classification methods. A wide range of inputs as well as classification approaches has been experimented for the improvement in EEG-based pattern classification frameworks. It was uncovered that incorporating reading- and writing-related tasks to experiments used in data collection may help improve these frameworks instead of using only simple tasks, and those unwanted artefacts caused by body movements in the EEG signals during reading and writing activities could be minimised using artefact subspace reconstruction. Further, support vector machine is identified as a promising classifier to be used in EEG-based pattern classification frameworks for dyslexia.
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Affiliation(s)
- Harshani Perera
- School of Engineering and Information Technology, Murdoch University, Murdoch, Australia.
| | | | - Kok Wai Wong
- School of Engineering and Information Technology, Murdoch University, Murdoch, Australia
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Wang D, Ren D, Li K, Feng Y, Ma D, Yan X, Wang G. Epileptic Seizure Detection in Long-Term EEG Recordings by Using Wavelet-Based Directed Transfer Function. IEEE Trans Biomed Eng 2018; 65:2591-2599. [PMID: 29993489 DOI: 10.1109/tbme.2018.2809798] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
GOAL The accurate automatic detection of epileptic seizures is very important in long-term electroencephalogram (EEG) recordings. In this study, the wavelet decomposition and the directed transfer function (DTF) algorithm were combined to present a novel wavelet-based directed transfer function (WDTF) method for the patient-specific seizure detection. METHODS First, five subbands were extracted from 19-channel EEG signals by using wavelet decomposition in a sliding window. Second, the information flow characteristics of five subbands and full frequency band of EEG signals were calculated by the DTF method. The intensity of the outflow information was then used to reduce the feature dimensionality. Finally, all features were combined to identify interictal and ictal EEG segments by the support vector machine classifier. RESULTS By using fivefold cross validation, the proposed method had achieved excellent performance with the average accuracy of 99.4%, the average selectivity of 91.1%, the average sensitivity of 92.1%, the average specificity of 99.5%, and the average detection rate of 95.8%. CONCLUSION The WDTF method is able to enhance seizure detection results in long-term EEG recordings of focal epilepsy patients. SIGNIFICANCE This study may lead to the development of seizure detection system with high performance, thus reducing the workload of epileptologists and facilitating to take corresponding steps promptly after the seizure onset. The high-frequency activity in the epilepsy brain may be of great importance for investigating the pathological mechanism and treatment of seizure.
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Wei CS, Wang YT, Lin CT, Jung TP. Toward Drowsiness Detection Using Non-hair-Bearing EEG-Based Brain-Computer Interfaces. IEEE Trans Neural Syst Rehabil Eng 2018; 26:400-406. [DOI: 10.1109/tnsre.2018.2790359] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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49
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Alickovic E, Kevric J, Subasi A. Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.07.022] [Citation(s) in RCA: 224] [Impact Index Per Article: 37.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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50
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Epileptic Seizure Prediction Using CSP and LDA for Scalp EEG Signals. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2017; 2017:1240323. [PMID: 29225615 PMCID: PMC5684608 DOI: 10.1155/2017/1240323] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 08/15/2017] [Accepted: 10/04/2017] [Indexed: 11/17/2022]
Abstract
This paper presents a patient-specific epileptic seizure predication method relying on the common spatial pattern- (CSP-) based feature extraction of scalp electroencephalogram (sEEG) signals. Multichannel EEG signals are traced and segmented into overlapping segments for both preictal and interictal intervals. The features extracted using CSP are used for training a linear discriminant analysis classifier, which is then employed in the testing phase. A leave-one-out cross-validation strategy is adopted in the experiments. The experimental results for seizure prediction obtained from the records of 24 patients from the CHB-MIT database reveal that the proposed predictor can achieve an average sensitivity of 0.89, an average false prediction rate of 0.39, and an average prediction time of 68.71 minutes using a 120-minute prediction horizon.
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