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Lucas A, Revell A, Davis KA. Artificial intelligence in epilepsy - applications and pathways to the clinic. Nat Rev Neurol 2024; 20:319-336. [PMID: 38720105 DOI: 10.1038/s41582-024-00965-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/16/2024] [Indexed: 06/06/2024]
Abstract
Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy have increased exponentially over the past decade. Integration of AI into epilepsy management promises to revolutionize the diagnosis and treatment of this complex disorder. However, translation of AI into neurology clinical practice has not yet been successful, emphasizing the need to consider progress to date and assess challenges and limitations of AI. In this Review, we provide an overview of AI applications that have been developed in epilepsy using a variety of data modalities: neuroimaging, electroencephalography, electronic health records, medical devices and multimodal data integration. For each, we consider potential applications, including seizure detection and prediction, seizure lateralization, localization of the seizure-onset zone and assessment for surgical or neurostimulation interventions, and review the performance of AI tools developed to date. We also discuss methodological considerations and challenges that must be addressed to successfully integrate AI into clinical practice. Our goal is to provide an overview of the current state of the field and provide guidance for leveraging AI in future to improve management of epilepsy.
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Affiliation(s)
- Alfredo Lucas
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrew Revell
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
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2
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Shafiezadeh S, Duma GM, Mento G, Danieli A, Antoniazzi L, Del Popolo Cristaldi F, Bonanni P, Testolin A. Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting. SENSORS (BASEL, SWITZERLAND) 2024; 24:2863. [PMID: 38732969 PMCID: PMC11086106 DOI: 10.3390/s24092863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 05/13/2024]
Abstract
The recent scientific literature abounds in proposals of seizure forecasting methods that exploit machine learning to automatically analyze electroencephalogram (EEG) signals. Deep learning algorithms seem to achieve a particularly remarkable performance, suggesting that the implementation of clinical devices for seizure prediction might be within reach. However, most of the research evaluated the robustness of automatic forecasting methods through randomized cross-validation techniques, while clinical applications require much more stringent validation based on patient-independent testing. In this study, we show that automatic seizure forecasting can be performed, to some extent, even on independent patients who have never been seen during the training phase, thanks to the implementation of a simple calibration pipeline that can fine-tune deep learning models, even on a single epileptic event recorded from a new patient. We evaluate our calibration procedure using two datasets containing EEG signals recorded from a large cohort of epileptic subjects, demonstrating that the forecast accuracy of deep learning methods can increase on average by more than 20%, and that performance improves systematically in all independent patients. We further show that our calibration procedure works best for deep learning models, but can also be successfully applied to machine learning algorithms based on engineered signal features. Although our method still requires at least one epileptic event per patient to calibrate the forecasting model, we conclude that focusing on realistic validation methods allows to more reliably compare different machine learning approaches for seizure prediction, enabling the implementation of robust and effective forecasting systems that can be used in daily healthcare practice.
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Affiliation(s)
- Sina Shafiezadeh
- Department of General Psychology, University of Padova, 35131 Padova, Italy; (G.M.); (F.D.P.C.)
| | - Gian Marco Duma
- Epilepsy and Clinical Neurophysiology Unit, Scientific Institute, IRCCS E. Medea, 31015 Conegliano, Italy; (G.M.D.); (A.D.); (L.A.); (P.B.)
| | - Giovanni Mento
- Department of General Psychology, University of Padova, 35131 Padova, Italy; (G.M.); (F.D.P.C.)
- Padova Neuroscience Center, University of Padova, 35131 Padova, Italy
| | - Alberto Danieli
- Epilepsy and Clinical Neurophysiology Unit, Scientific Institute, IRCCS E. Medea, 31015 Conegliano, Italy; (G.M.D.); (A.D.); (L.A.); (P.B.)
| | - Lisa Antoniazzi
- Epilepsy and Clinical Neurophysiology Unit, Scientific Institute, IRCCS E. Medea, 31015 Conegliano, Italy; (G.M.D.); (A.D.); (L.A.); (P.B.)
| | | | - Paolo Bonanni
- Epilepsy and Clinical Neurophysiology Unit, Scientific Institute, IRCCS E. Medea, 31015 Conegliano, Italy; (G.M.D.); (A.D.); (L.A.); (P.B.)
| | - Alberto Testolin
- Department of General Psychology, University of Padova, 35131 Padova, Italy; (G.M.); (F.D.P.C.)
- Department of Mathematics, University of Padova, 35131 Padova, Italy
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3
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Naghdabadi Z, Jahed M. Heterogeneous correlate and potential diagnostic biomarker of tinnitus based on nonlinear dynamics of resting-state EEG recordings. PLoS One 2024; 19:e0290563. [PMID: 38166014 PMCID: PMC10760901 DOI: 10.1371/journal.pone.0290563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 08/09/2023] [Indexed: 01/04/2024] Open
Abstract
Tinnitus is a heterogeneous condition of hearing a rattling sound when there is no auditory stimulus. This rattling sound is associated with abnormal synchronous oscillations in auditory and non-auditory cortical areas. Since tinnitus is a highly heterogeneous condition with no objective detection criteria, it is necessary to search for indicators that can be compared between and within participants for diagnostic purposes. This study introduces heterogeneous though comparable indicators of tinnitus through investigation of spontaneous fluctuations in resting-state brain dynamics. The proposed approach uses nonlinear measures of chaos theory, to detect tinnitus and cross correlation patterns to reflect many of the previously reported neural correlates of tinnitus. These indicators may serve as effective measures of tinnitus risk even at early ages before any symptom is reported. The approach quantifies differences in oscillatory brain dynamics of tinnitus and normal subjects. It demonstrates that the left temporal areas of subjects with tinnitus exhibit larger lyapunov exponent indicating irregularity of brain dynamics in these regions. More complex dynamics is further recognized in tinnitus cases through entropy. We use this evidence to distinguish tinnitus patients from normal participants. Besides, we illustrate that certain anticorrelation patterns appear in these nonlinear measures across temporal and frontal areas in the brain perhaps corresponding to increased/decreased connectivity in certain brain networks and a shift in the balance of excitation and inhibition in tinnitus. Additionally, the main correlations are lost in tinnitus participants compared to control group suggesting involvement of distinct neural mechanisms in generation and persistence of tinnitus.
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Affiliation(s)
- Zahra Naghdabadi
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Mehran Jahed
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
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4
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Baud MO, Proix T, Gregg NM, Brinkmann BH, Nurse ES, Cook MJ, Karoly PJ. Seizure forecasting: Bifurcations in the long and winding road. Epilepsia 2023; 64 Suppl 4:S78-S98. [PMID: 35604546 PMCID: PMC9681938 DOI: 10.1111/epi.17311] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/20/2022] [Accepted: 05/20/2022] [Indexed: 11/28/2022]
Abstract
To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies.
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Affiliation(s)
- Maxime O Baud
- Sleep-Wake-Epilepsy Center, Center for Experimental Neurology, NeuroTec, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
- Wyss Center for Bio- and Neuro-Engineering, Geneva, Switzerland
| | - Timothée Proix
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nicholas M Gregg
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Benjamin H Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ewan S Nurse
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Mark J Cook
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Philippa J Karoly
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
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5
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Wang R, Zhu W, Liang G, Xu J, Guo J, Wang L. Animal models for epileptic foci localization, seizure detection, and prediction by electrical impedance tomography. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2022; 13:e1619. [PMID: 36093634 DOI: 10.1002/wcs.1619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/08/2022] [Accepted: 07/14/2022] [Indexed: 06/15/2023]
Abstract
Surgical resection of lesions and closed-loop suppression are the two main treatment options for patients with refractory epilepsy whose symptoms cannot be managed with medicines. Unfortunately, failures in foci localization and seizure prediction are constraining these treatments. Electrical impedance tomography (EIT), sensitive to impedance changes caused by blood flow or cell swelling, is a potential new way to locate epileptic foci and predict seizures. Animal validation is a necessary research process before EIT can be used in clinical practice, but it is unclear which among the many animal epilepsy models is most suited to this task. The selection of an animal model of epilepsy that is similar to human seizures and can be adapted to EIT is important for the accuracy and reliability of EIT research results. This study provides an overview of the animal models of epilepsy that have been used in research on the use of EIT to locate the foci or predict seizures; discusses the advantages and disadvantages of these models regarding inducement by chemical convulsant and electrical stimulation; and finally proposes optimal animal models of epilepsy to obtain more convincing research results for foci localization and seizure prediction by EIT. The ultimate goal of this study is to facilitate the development of new treatments for patients with refractory epilepsy. This article is categorized under: Neuroscience > Clinical Neuroscience Psychology > Brain Function and Dysfunction.
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Affiliation(s)
- Rong Wang
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, China
| | - Wenjing Zhu
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, China
| | - Guohua Liang
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, China
| | - Jiaming Xu
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, China
| | - Jie Guo
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, China
| | - Lei Wang
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, China
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Granado M, Collavini S, Baravalle R, Martinez N, Montemurro MA, Rosso OA, Montani F. High-frequency oscillations in the ripple bands and amplitude information coding: Toward a biomarker of maximum entropy in the preictal signals. CHAOS (WOODBURY, N.Y.) 2022; 32:093151. [PMID: 36182366 DOI: 10.1063/5.0101220] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 08/29/2022] [Indexed: 06/16/2023]
Abstract
Intracranial electroencephalography (iEEG) can directly record local field potentials (LFPs) from a large set of neurons in the vicinity of the electrode. To search for possible epileptic biomarkers and to determine the epileptogenic zone that gives rise to seizures, we investigated the dynamics of basal and preictal signals. For this purpose, we explored the dynamics of the recorded time series for different frequency bands considering high-frequency oscillations (HFO) up to 240 Hz. We apply a Hilbert transform to study the amplitude and phase of the signals. The dynamics of the different frequency bands in the time causal entropy-complexity plane, H × C, is characterized by comparing the dynamical evolution of the basal and preictal time series. As the preictal states evolve closer to the time in which the epileptic seizure starts, the, H × C, dynamics changes for the higher frequency bands. The complexity evolves to very low values and the entropy becomes nearer to its maximal value. These quasi-stable states converge to equiprobable states when the entropy is maximal, and the complexity is zero. We could, therefore, speculate that in this case, it corresponds to the minimization of Gibbs free energy. In this case, the maximum entropy is equivalent to the principle of minimum consumption of resources in the system. We can interpret this as the nature of the system evolving temporally in the preictal state in such a way that the consumption of resources by the system is minimal for the amplitude in frequencies between 220-230 and 230-240 Hz.
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Affiliation(s)
- Mauro Granado
- Instituto de Física de La Plata (IFLP), Universidad Nacional de La Plata, CONICET CCT-La Plata, Diagonal 113 entre 63 y 64, La Plata 1900, Buenos Aires, Argentina
| | - Santiago Collavini
- Instituto de Electrónica Industrial, Control y Procesamiento de Se nales (LEICI), Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP-CONICET), La Plata 1900, Buenos Aires, Argentina
| | - Roman Baravalle
- Instituto de Física de La Plata (IFLP), Universidad Nacional de La Plata, CONICET CCT-La Plata, Diagonal 113 entre 63 y 64, La Plata 1900, Buenos Aires, Argentina
| | - Nataniel Martinez
- Instituto de Física de Mar del Plata, Universidad Nacional de Mar del Plata & CONICET, Mar del Plata 7600, Buenos Aires, Argentina
| | - Marcelo A Montemurro
- School of Mathematics & Statistics, Faculty of Science, Technology, Engineering & Mathematics, The Open University, Walton Hall, Milton Keynes MK7 6AA, United Kingdom
| | - Osvaldo A Rosso
- Instituto de Física de La Plata (IFLP), Universidad Nacional de La Plata, CONICET CCT-La Plata, Diagonal 113 entre 63 y 64, La Plata 1900, Buenos Aires, Argentina
| | - Fernando Montani
- Instituto de Física de La Plata (IFLP), Universidad Nacional de La Plata, CONICET CCT-La Plata, Diagonal 113 entre 63 y 64, La Plata 1900, Buenos Aires, Argentina
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7
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Wang X, Ling Y, Ling X, Li X, Li Z, Hu K, Dai M, Zhu J, Du Y, Yang Q. A particle swarm algorithm optimization‐based SVM–KNN algorithm for epileptic EEG recognition. INT J INTELL SYST 2022. [DOI: 10.1002/int.23040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Xiaoying Wang
- Information Center Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
| | - Yu Ling
- School of Automation Guangdong University of Technology Guangzhou China
| | - Xiang Ling
- Information Center Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
| | - Xianghuan Li
- School of Automation Guangdong University of Technology Guangzhou China
| | - Zhicheng Li
- Department of Otolaryngology Head and Neck Surgery Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
| | - Kunpeng Hu
- Department of General Surgery Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
| | - Min Dai
- Department of Traditional Chinese Medicine Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
| | - Jia Zhu
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Jinhua China
| | - Yuxiao Du
- School of Automation Guangdong University of Technology Guangzhou China
| | - Qintai Yang
- Information Center Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
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8
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Yang Y, Truong ND, Eshraghian JK, Nikpour A, Kavehei O. Weak self-supervised learning for seizure forecasting: a feasibility study. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220374. [PMID: 35950196 PMCID: PMC9346358 DOI: 10.1098/rsos.220374] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 07/12/2022] [Indexed: 05/27/2023]
Abstract
This paper proposes an artificial intelligence system that continuously improves over time at event prediction using initially unlabelled data by using self-supervised learning. Time-series data are inherently autocorrelated. By using a detection model to generate weak labels on the fly, which are concurrently used as targets to train a prediction model on a time-shifted input data stream, this autocorrelation can effectively be harnessed to reduce the burden of manual labelling. This is critical in medical patient monitoring, as it enables the development of personalized forecasting models without demanding the annotation of long sequences of physiological signal recordings. We perform a feasibility study on seizure prediction, which is identified as an ideal test case, as pre-ictal brainwaves are patient-specific, and tailoring models to individual patients is known to improve forecasting performance significantly. Our self-supervised approach is used to train individualized forecasting models for 10 patients, showing an average relative improvement in sensitivity by 14.30% and a reduction in false alarms by 19.61% in early seizure forecasting. This proof-of-concept on the feasibility of using a continuous stream of time-series neurophysiological data paves the way towards a low-power neuromorphic neuromodulation system.
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Affiliation(s)
- Yikai Yang
- School of Biomedical Engineering, and the Australian Research Council Training Centre for Innovative BioEngineering, Faculty of EngineeringThe University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
| | - Nhan Duy Truong
- School of Biomedical Engineering, and the Australian Research Council Training Centre for Innovative BioEngineering, Faculty of EngineeringThe University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
- The University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
| | - Jason K. Eshraghian
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Armin Nikpour
- Faculty of Medicine and Health, Central Clinical School, The University of Sydney, Sydney, New South Wales 2006, Australia
- Comprehensive Epilepsy Service and Department of Neurology, Royal Prince Alfred Hospital, Camperdown, New South Wales 2050, Australia
| | - Omid Kavehei
- School of Biomedical Engineering, and the Australian Research Council Training Centre for Innovative BioEngineering, Faculty of EngineeringThe University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
- The University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
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9
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Choi W, Kim MJ, Yum MS, Jeong DH. Deep Convolutional Gated Recurrent Unit Combined with Attention Mechanism to Classify Pre-Ictal from Interictal EEG with Minimized Number of Channels. J Pers Med 2022; 12:jpm12050763. [PMID: 35629185 PMCID: PMC9147609 DOI: 10.3390/jpm12050763] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/05/2022] [Accepted: 05/06/2022] [Indexed: 02/05/2023] Open
Abstract
The early prediction of epileptic seizures is important to provide appropriate treatment because it can notify clinicians in advance. Various EEG-based machine learning techniques have been used for automatic seizure classification based on subject-specific paradigms. However, because subject-specific models tend to perform poorly on new patient data, a generalized model with a cross-patient paradigm is necessary for building a robust seizure diagnosis system. In this study, we proposed a generalized model that combines one-dimensional convolutional layers (1D CNN), gated recurrent unit (GRU) layers, and attention mechanisms to classify preictal and interictal phases. When we trained this model with ten minutes of preictal data, the average accuracy over eight patients was 82.86%, with 80% sensitivity and 85.5% precision, outperforming other state-of-the-art models. In addition, we proposed a novel application of attention mechanisms for channel selection. The personalized model using three channels with the highest attention score from the generalized model performed better than when using the smallest attention score. Based on these results, we proposed a model for generalized seizure predictors and a seizure-monitoring system with a minimized number of EEG channels.
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Affiliation(s)
- WooHyeok Choi
- School of Computer Science and Information Engineering, The Catholic University of Korea, Seoul 14662, Korea;
| | - Min-Jee Kim
- Department of Pediatrics, Asan Medical Center Children’s Hospital, Ulsan University College of Medicine, Seoul 05505, Korea; (M.-J.K.); (M.-S.Y.)
| | - Mi-Sun Yum
- Department of Pediatrics, Asan Medical Center Children’s Hospital, Ulsan University College of Medicine, Seoul 05505, Korea; (M.-J.K.); (M.-S.Y.)
| | - Dong-Hwa Jeong
- Department of Artificial Intelligence, The Catholic University of Korea, Seoul 14662, Korea
- Correspondence:
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10
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Zhou J, Liu L, Leng Y, Yang Y, Gao B, Jiang Z, Nie W, Yuan Q. Both Cross-Patient and Patient-Specific Seizure Detection Based on Self-Organizing Fuzzy Logic. Int J Neural Syst 2022; 32:2250017. [DOI: 10.1142/s0129065722500174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Automatic epilepsy detection is of great significance for the diagnosis and treatment of patients. Most detection methods are based on patient-specific models and have achieved good results. However, in practice, new patients do not have their own previous EEG data and therefore cannot be initially diagnosed. If the EEG data of other patients can be used to achieve cross-patient detection, and cross-patient and patient-specific experiments can be combined at the same time, this method will be more widely used. In this work, an EEG classification model based on a self-organizing fuzzy logic (SOF) classifier is proposed for both cross-patient and patient-specific seizure detection. After preprocessing, the features of the original EEG signal are extracted and sent to the SOF classifier. This classification model is free from predefined parameters or a prior assumption regarding the EEG data generation model and only stores the key meta-parameters in memory. Therefore, it is very suitable for large-scale EEG signals in cross-patient detection. Selecting different granularity and classification distance in two different experiments after post-processing will achieve the best results. Experiments were conducted using a long-term continuous scalp EEG database and the [Formula: see text]-mean of cross-patient and patient-specific detection reached 83.35% and 92.04%, respectively. A comparison with other methods shows that there is greater performance and generalizability with this method.
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Affiliation(s)
- Jiazheng Zhou
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Li Liu
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Yan Leng
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Yuying Yang
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Bin Gao
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Zonghong Jiang
- College of Resources and Environment Engineering, Guizhou University, Guiyang 550025, P. R. China
| | - Weiwei Nie
- The First Affiliated Hospital of Shandong, First Medical University, Jinan 250014, P. R. China
| | - Qi Yuan
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
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11
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Rout SK, Sahani M, Dora C, Biswal PK, Biswal B. An efficient epileptic seizure classification system using empirical wavelet transform and multi-fuse reduced deep convolutional neural network with digital implementation. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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12
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Automatic Diagnosis of Epileptic Seizures in EEG Signals Using Fractal Dimension Features and Convolutional Autoencoder Method. BIG DATA AND COGNITIVE COMPUTING 2021. [DOI: 10.3390/bdcc5040078] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper proposes a new method for epileptic seizure detection in electroencephalography (EEG) signals using nonlinear features based on fractal dimension (FD) and a deep learning (DL) model. Firstly, Bonn and Freiburg datasets were used to perform experiments. The Bonn dataset consists of binary and multi-class classification problems, and the Freiburg dataset consists of two-class EEG classification problems. In the preprocessing step, all datasets were prepossessed using a Butterworth band pass filter with 0.5–60 Hz cut-off frequency. Then, the EEG signals of the datasets were segmented into different time windows. In this section, dual-tree complex wavelet transform (DT-CWT) was used to decompose the EEG signals into the different sub-bands. In the following section, in order to feature extraction, various FD techniques were used, including Higuchi (HFD), Katz (KFD), Petrosian (PFD), Hurst exponent (HE), detrended fluctuation analysis (DFA), Sevcik, box counting (BC), multiresolution box-counting (MBC), Margaos-Sun (MSFD), multifractal DFA (MF-DFA), and recurrence quantification analysis (RQA). In the next step, the minimum redundancy maximum relevance (mRMR) technique was used for feature selection. Finally, the k-nearest neighbors (KNN), support vector machine (SVM), and convolutional autoencoder (CNN-AE) were used for the classification step. In the classification step, the K-fold cross-validation with k = 10 was employed to demonstrate the effectiveness of the classifier methods. The experiment results show that the proposed CNN-AE method achieved an accuracy of 99.736% and 99.176% for the Bonn and Freiburg datasets, respectively.
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13
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Chen HH, Shiao HT, Cherkassky V. Online Prediction of Lead Seizures from iEEG Data. Brain Sci 2021; 11:brainsci11121554. [PMID: 34942859 PMCID: PMC8699082 DOI: 10.3390/brainsci11121554] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 11/06/2021] [Accepted: 11/23/2021] [Indexed: 11/17/2022] Open
Abstract
We describe a novel system for online prediction of lead seizures from long-term intracranial electroencephalogram (iEEG) recordings for canines with naturally occurring epilepsy. This study adopts new specification of lead seizures, reflecting strong clustering of seizures in observed data. This clustering results in fewer lead seizures (~7 lead seizures per dog), and hence new challenges for online seizure prediction, that are addressed in the proposed system. In particular, the machine learning part of the system is implemented using the group learning method suitable for modeling sparse and noisy seizure data. In addition, several modifications for the proposed system are introduced to cope with the non-stationarity of a noisy iEEG signal. They include: (1) periodic retraining of the SVM classifier using most recent training data; (2) removing samples with noisy labels from training data; and (3) introducing a new adaptive post-processing technique for combining many predictions made for 20 s windows into a single prediction for a 4 h segment. Application of the proposed system requires only two lead seizures for training the initial model, and results in high prediction performance for all four dogs (with mean 0.84 sensitivity, 0.27 time-in-warning, and 0.78 false-positive rate per day). The proposed system achieves accurate prediction of lead seizures during long-term test periods, 3–16 lead seizures during a 169–364 day test period, whereas earlier studies did not differentiate between lead vs. non-lead seizures and used much shorter test periods (~few days long).
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Affiliation(s)
- Hsiang-Han Chen
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455, USA;
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA;
- Correspondence:
| | - Han-Tai Shiao
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA;
| | - Vladimir Cherkassky
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455, USA;
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA;
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14
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Maesawa S, Bagarinao E, Nakatsubo D, Ishizaki T, Takai S, Torii J, Kato S, Shibata M, Wakabayashi T, Saito R. Multitier Network Analysis Using Resting-State Functional MRI for Epilepsy Surgery. Neurol Med Chir (Tokyo) 2021; 62:45-55. [PMID: 34759070 PMCID: PMC8754678 DOI: 10.2176/nmc.oa.2021-0173] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
Resting-state functional MRI (rs-fMRI) has been utilized to visualize large-scale brain networks. We evaluated the usefulness of multitier network analysis using rs-fMRI in patients with focal epilepsy. Structural and rs-fMRI data were retrospectively evaluated in 20 cases with medically refractory focal epilepsy, who subsequently underwent surgery. First, structural changes were examined using voxel-based morphometry analysis. Second, alterations in large-scale networks were evaluated using dual-regression analysis. Third, changes in cortical hubs were analyzed and the relationship between aberrant hubs and the epileptogenic zone (EZ) was evaluated. Finally, the relationship between the hubs and the default mode network (DMN) was examined using spectral dynamic causal modeling (spDCM). Dual-regression analysis revealed significant decrease in functional connectivity in several networks including DMN in patients, although no structural difference was seen between groups. Aberrant cortical hubs were observed in and around the EZ (EZ hubs) in 85% of the patients, and a strong degree of EZ hubs correlated to good seizure outcomes postoperatively. In spDCM analysis, facilitation was often seen from the EZ hub to the contralateral side, while inhibition was seen from the EZ hub to nodes of the DMN. Some cognition-related networks were impaired in patients with focal epilepsy. The EZ hub appeared in the vicinity of EZ facilitating connections to distant regions in the early phase, which may eventually generate secondary focus, while inhibiting connections to the DMN, which may cause cognitive deterioration. Our results demonstrate pathological network alterations in epilepsy and suggest that earlier surgical intervention may be more effective.
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Affiliation(s)
- Satoshi Maesawa
- Brain & Mind Research Center, Nagoya University Graduate School of Medicine.,Department of Neurosurgery, Nagoya University Graduate School of Medicine
| | - Epifanio Bagarinao
- Brain & Mind Research Center, Nagoya University Graduate School of Medicine.,Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine
| | - Daisuke Nakatsubo
- Department of Neurosurgery, Nagoya University Graduate School of Medicine.,Radiosurgery and Focused Ultrasound Surgery Center, Nagoya Kyoritsu Hospital
| | - Tomotaka Ishizaki
- Department of Neurosurgery, Nagoya University Graduate School of Medicine.,Department of Neurosurgery, Kainan Hospital
| | - Sou Takai
- Department of Neurosurgery, Nagoya University Graduate School of Medicine
| | - Jun Torii
- Department of Neurosurgery, Nagoya University Graduate School of Medicine
| | - Sachiko Kato
- Department of Neurosurgery, Nagoya University Graduate School of Medicine.,Radiosurgery and Focused Ultrasound Surgery Center, Nagoya Kyoritsu Hospital
| | - Masashi Shibata
- Department of Neurosurgery, Nagoya University Graduate School of Medicine.,Radiosurgery and Focused Ultrasound Surgery Center, Nagoya Kyoritsu Hospital
| | - Toshihiko Wakabayashi
- Department of Neurosurgery, Nagoya University Graduate School of Medicine.,Radiosurgery and Focused Ultrasound Surgery Center, Nagoya Kyoritsu Hospital
| | - Ryuta Saito
- Department of Neurosurgery, Nagoya University Graduate School of Medicine
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15
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Opie NL, O'Brien TJ. The potential of closed-loop endovascular neurostimulation as a viable therapeutic approach for drug-resistant epilepsy: A critical review. Artif Organs 2021; 46:337-348. [PMID: 34101849 DOI: 10.1111/aor.14007] [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: 03/11/2021] [Revised: 05/23/2021] [Accepted: 05/27/2021] [Indexed: 11/30/2022]
Abstract
Over the last few decades, biomedical implants have successfully delivered therapeutic electrical stimulation to reduce the frequency and severity of seizures in people with drug-resistant epilepsy. However, neurostimulation approaches require invasive surgery to implant stimulating electrodes, and surgical, medical, and hardware complications are not uncommon. An endovascular approach provides a potentially safer and less invasive surgical alternative. This article critically evaluates the feasibility of endovascular closed-loop neuromodulation for the treatment of epilepsy. By reviewing literature that reported the impact of direct electrical stimulation to reduce the frequency of epileptic seizures, we identified clinically validated extracranial, cortical, and deep cortical neural targets. We identified veins in close proximity to these targets and evaluated the potential of delivering an endovascular implant to these veins based on their diameter. We then compared the risks and benefits of existing technology to describe a benchmark of clinical safety and efficacy that would need to be achieved for endovascular neuromodulation to provide therapeutic benefit. For the majority of brain regions that have been clinically demonstrated to reduce seizure occurrence in response to delivered electrical stimulation, vessels of appropriate diameter for delivery of an endovascular electrode to these regions could be achieved. This includes delivery to the vagus nerve via the 13.2 ± 0.9 mm diameter internal jugular vein, the motor cortex via the 6.5 ± 1.7 mm diameter superior sagittal sinus, and the cerebellum via the 7.7 ± 1.4 mm diameter sigmoid sinus or 6.2 ± 1.4 mm diameter transverse sinus. Deep cerebral targets can also be accessed with an endovascular approach, with the 1.9 ± 0.5 mm diameter internal cerebral vein and 1.2-mm-diameter thalamostriate vein lying in close proximity to the anterior and centromedian nuclei of the thalamus, respectively. This work identified numerous veins that are in close proximity to conventional stimulation targets that are of a diameter large enough for delivery and deployment of an endovascular electrode array, supporting future work to assess clinical efficacy and chronic safety of an endovascular approach to deliver therapeutic neurostimulation.
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Affiliation(s)
- Nicholas L Opie
- Vascular Bionics Laboratory, Department of Medicine, The University of Melbourne, Parkville, VIC, Australia.,Synchron Inc., San Francisco, CA, USA
| | - Terence J O'Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia.,Department of Neurology, Alfred Health, Melbourne, VIC, Australia
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16
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Sahani M, Rout SK, Dash PK. Epileptic Seizure Recognition Using Reduced Deep Convolutional Stack Autoencoder and Improved Kernel RVFLN From EEG Signals. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:595-605. [PMID: 34156948 DOI: 10.1109/tbcas.2021.3090995] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
In this paper, reduced deep convolutional stack autoencoder (RDCSAE) and improved kernel random vector functional link network (IKRVFLN) are combined to recognize the epileptic seizure using both the multichannel scalp and single-channel electroencephalogram (EEG) signals. The novel RDCSAE structure is designed to extract the most discriminative unsupervised features from EEG signals and fed into the proposed supervised IKRVFLN classifier to train efficiently by reducing the mean-square error cost function for recognizing the epileptic seizure activity with promising accuracy. The proposed RDCSAE-IKRVFLN algorithm is tested over the benchmark Boston Children's Hospital multichannel scalp EEG (sEEG) and Boon University, Germany single-channel EEG databases. The less computational complexity, higher learning speed, better model generalization, accurate epileptic seizure recognition, remarkable classification accuracy, negligible false positive rate per hour (FPR/h) and short event recognition time are the main advantages of the proposed RDCSAE-IKRVFLN method over reduced deep convolutional neural network (RDCNN), RDCSAE and RDCSAE-KRVFLN methods. The proposed RDCSAE-IKRVFLN method is implemented in a high-speed reconfigurable field-programmable gate array (FPGA) hardware environment to design a computer-aided-diagnosis (CAD) system for automatic epileptic seizure diagnosis. The simplicity, feasibility, and practicability of the proposed method validate the stable and reliable performances of epilepsy detection and recognition.
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17
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Liu X, Richardson AG. Edge deep learning for neural implants: a case study of seizure detection and prediction. J Neural Eng 2021; 18. [PMID: 33794507 DOI: 10.1088/1741-2552/abf473] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 04/01/2021] [Indexed: 11/12/2022]
Abstract
Objective.Implanted devices providing real-time neural activity classification and control are increasingly used to treat neurological disorders, such as epilepsy and Parkinson's disease. Classification performance is critical to identifying brain states appropriate for the therapeutic action (e.g. neural stimulation). However, advanced algorithms that have shown promise in offline studies, in particular deep learning (DL) methods, have not been deployed on resource-restrained neural implants. Here, we designed and optimized three DL models or edge deployment and evaluated their inference performance in a case study of seizure detection.Approach.A deep neural network (DNN), a convolutional neural network (CNN), and a long short-term memory (LSTM) network were designed and trained with TensorFlow to classify ictal, preictal, and interictal phases from the CHB-MIT scalp EEG database. A sliding window based weighted majority voting algorithm was developed to detect seizure events based on each DL model's classification results. After iterative model compression and coefficient quantization, the algorithms were deployed on a general-purpose, off-the-shelf microcontroller for real-time testing. Inference sensitivity, false positive rate (FPR), execution time, memory size, and power consumption were quantified.Main results.For seizure event detection, the sensitivity and FPR for the DNN, CNN, and LSTM models were 87.36%/0.169 h-1, 96.70%/0.102 h-1, and 97.61%/0.071 h-1, respectively. Predicting seizures for early warnings was also feasible. The LSTM model achieved the best overall performance at the expense of the highest power. The DNN model achieved the shortest execution time. The CNN model showed advantages in balanced performance and power with minimum memory requirement. The implemented model compression and quantization achieved a significant saving of power and memory with an accuracy degradation of less than 0.5%.Significance.Inference with embedded DL models achieved performance comparable to many prior implementations that had no time or computational resource limitations. Generic microcontrollers can provide the required memory and computational resources, while model designs can be migrated to application-specific integrated circuits for further optimization and power saving. The results suggest that edge DL inference is a feasible option for future neural implants to improve classification performance and therapeutic outcomes.
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Affiliation(s)
- Xilin Liu
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Andrew G Richardson
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, United States of America
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18
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Rout SK, Sahani M, Dash PK, Biswal PK. Multifuse multilayer multikernel RVFLN+ of process modes decomposition and approximate entropy data from iEEG/sEEG signals for epileptic seizure recognition. Comput Biol Med 2021; 132:104299. [PMID: 33711557 DOI: 10.1016/j.compbiomed.2021.104299] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 02/23/2021] [Accepted: 02/24/2021] [Indexed: 12/01/2022]
Abstract
In this paper, the extracted features using variational mode decomposition (VMD) and approximate entropy (ApEn) privileged information of the input EEG signals are combined with multilayer multikernel random vector functional link network plus (MMRVFLN+) classifier to recognize the epileptic seizure epochs efficaciously. In our experiment Bonn University single-channel intracranial electroencephalogram (iEEG) and Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) multichannel scalp EEG (sEEG) recordings are considered to evaluate the efficacy of the proposed method. The VMD is applied on chaotic, non-stationary, nonlinear, and complex EEG signal to decompose it into three band-limited intrinsic mode functions (BLIMFs). The Hilbert transform (HT) is applied on BLIMFs to extract informative spectral and temporal features. The ApEn is computed from the raw EEG signals as the privileged information and given to the multi-hidden layer structure to obtain the most discriminative compressed form. The scatter plots show the distinct nature of compressed privileged ApEn information among the seizure pattern classes. The linear as well as nonlinear mapping, local and global kernel function, high-learning speed, less computationally complex MMRVFLN+ classifier is proposed to recognize the seizure events accurately by importing the efficacious features with ApEn as the input. The advanced signal processing algorithm i.e., Hilbert Huang transform (HHT) with ApEn and MMRVFLN+ are combined to compare the performance with the proposed VMDHTApEn-MMRVFLN+ method. The proposed method has remarkable recognition ability, superior classification accuracy, and excellent overall performance as compared to other methods. The digital architecture of the multifuse MMRVFLN+ is developed and implemented on a high-speed reconfigurable FPGA hardware platform to validate the effectiveness of the proposed method. The superior classification accuracy, the negligible false positive rate per hour (FPR/h), simplicity, feasibility, robustness, and practicability of the proposed method validate its ability to recognize the epileptic seizure epochs automatically.
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Affiliation(s)
- Susanta Kumar Rout
- Siksha 'O' Anusandhan Deemed to Be University, Bhubaneswar, Odisha, India; International Institute of Information Technology, Bhubaneswar, Odisha, India
| | - Mrutyunjaya Sahani
- Siksha 'O' Anusandhan Deemed to Be University, Bhubaneswar, Odisha, India.
| | - P K Dash
- Siksha 'O' Anusandhan Deemed to Be University, Bhubaneswar, Odisha, India
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19
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Ruiz Marín M, Villegas Martínez I, Rodríguez Bermúdez G, Porfiri M. Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings. iScience 2021; 24:101997. [PMID: 33490905 PMCID: PMC7811137 DOI: 10.1016/j.isci.2020.101997] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 10/23/2020] [Accepted: 12/23/2020] [Indexed: 11/23/2022] Open
Abstract
Automated seizure detection in long-term video-EEG recordings is far from being integrated into common clinical practice. Here, we leverage classical and state-of-the-art complexity measures to robustly and automatically detect seizures from scalp recordings. Brain activity is scored through eight features, encompassing traditional time domain and novel measures of recurrence. A binary classification algorithm tailored to treat unbalanced dataset is used to determine whether a time window is ictal or non-ictal from its features. The application of the algorithm on a cohort of ten adult patients with focal refractory epilepsy indicates sensitivity, specificity, and accuracy of 90%, along with a true alarm rate of 95% and less than four false alarms per day. The proposed approach emphasizes ictal patterns against noisy background without the need of data preprocessing. Finally, we benchmark our approach against previous studies on two publicly available datasets, demonstrating the good performance of our algorithm. Complexity measures are formulated to enhance classical time-domain statistics of EEG The detection algorithm does not need ad-hoc data preprocessing to address artifacts Focal seizures are detected 95% of the time with less than four false alarms per day The approach offers a visual representation of a seizure as a time-evolving network
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Affiliation(s)
- Manuel Ruiz Marín
- Department of Quantitative Methods, Law and Modern Languages, Technical University of Cartagena (UPCT), Cartagena, Murcia 30201, Spain
- Bio-Health Institute (IMIB-Arrixaca), Health Science Campus, Murcia, CP 30120, Spain
- Corresponding author
| | - Irene Villegas Martínez
- Department of Projects and Innovation, Health Service of Murcia (SMS), Murcia, Spain
- Bio-Health Institute (IMIB-Arrixaca), Health Science Campus, Murcia, CP 30120, Spain
- Corresponding author
| | | | - Maurizio Porfiri
- Department of Quantitative Methods, Law and Modern Languages, Technical University of Cartagena (UPCT), Cartagena, Murcia 30201, Spain
- Department of Mechanical and Aerospace Engineering, and Department of Biomedical Engineering New York University Tandon School of Engineering (NYU), Brooklyn, NY, USA
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20
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Nakai Y, Nishibayashi H, Donishi T, Terada M, Nakao N, Kaneoke Y. Regional abnormality of functional connectivity is associated with clinical manifestations in individuals with intractable focal epilepsy. Sci Rep 2021; 11:1545. [PMID: 33452388 PMCID: PMC7810833 DOI: 10.1038/s41598-021-81207-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 01/04/2021] [Indexed: 01/29/2023] Open
Abstract
We explored regional functional connectivity alterations in intractable focal epilepsy brains using resting-state functional MRI. Distributions of the network parameters (corresponding to degree and eigenvector centrality) measured at each brain region for all 25 patients were significantly different from age- and sex-matched control data that were estimated by a healthy control dataset (n = 582, 18-84 years old). The number of abnormal regions whose parameters exceeded the mean + 2 SD of age- and sex-matched data for each patient were associated with various clinical parameters such as the duration of illness and seizure severity. Furthermore, abnormal regions for each patient tended to have functional connections with each other (mean ± SD = 58.6 ± 20.2%), the magnitude of which was negatively related to the quality of life. The abnormal regions distributed within the default mode network with significantly higher probability (p < 0.05) in 7 of 25 patients. We consider that the detection of abnormal regions by functional connectivity analysis using a large number of control datasets is useful for the numerical assessment of each patient's clinical conditions, although further study is necessary to elucidate etiology-specific abnormalities.
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Affiliation(s)
- Yasuo Nakai
- Department of Neurological Surgery, Wakayama Medical University, 811-1 Kimiidera, Wakayama, 641-8509, Japan.
| | - Hiroki Nishibayashi
- Department of Neurological Surgery, Wakayama Medical University, 811-1 Kimiidera, Wakayama, 641-8509, Japan
| | - Tomohiro Donishi
- Department of System Neurophysiology, Wakayama Medical University, 811-1 Kimiidera, Wakayama, 641-8509, Japan
| | - Masaki Terada
- Wakayama-Minami Radiology Clinic, 870-2 Kimiidera, Wakayama, 641-0012, Japan
| | - Naoyuki Nakao
- Department of Neurological Surgery, Wakayama Medical University, 811-1 Kimiidera, Wakayama, 641-8509, Japan
| | - Yoshiki Kaneoke
- Department of System Neurophysiology, Wakayama Medical University, 811-1 Kimiidera, Wakayama, 641-8509, Japan
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21
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Carvalho VR, Moraes MFD, Cash SS, Mendes EMAM. Active probing to highlight approaching transitions to ictal states in coupled neural mass models. PLoS Comput Biol 2021; 17:e1008377. [PMID: 33493165 PMCID: PMC7861539 DOI: 10.1371/journal.pcbi.1008377] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 02/04/2021] [Accepted: 12/02/2020] [Indexed: 01/07/2023] Open
Abstract
The extraction of electrophysiological features that reliably forecast the occurrence of seizures is one of the most challenging goals in epilepsy research. Among possible approaches to tackle this problem is the use of active probing paradigms in which responses to stimuli are used to detect underlying system changes leading up to seizures. This work evaluates the theoretical and mechanistic underpinnings of this strategy using two coupled populations of the well-studied Wendling neural mass model. Different model settings are evaluated, shifting parameters (excitability, slow inhibition, or inter-population coupling gains) from normal towards ictal states while probing stimuli are applied every 2 seconds to the input of either one or both populations. The correlation between the extracted features and the ictogenic parameter shifting indicates if the impending transition to the ictal state may be identified in advance. Results show that not only can the response to the probing stimuli forecast seizures but this is true regardless of the altered ictogenic parameter. That is, similar feature changes are highlighted by probing stimuli responses in advance of the seizure including: increased response variance and lag-1 autocorrelation, decreased skewness, and increased mutual information between the outputs of both model subsets. These changes were mostly restricted to the stimulated population, showing a local effect of this perturbational approach. The transition latencies from normal activity to sustained discharges of spikes were not affected, suggesting that stimuli had no pro-ictal effects. However, stimuli were found to elicit interictal-like spikes just before the transition to the ictal state. Furthermore, the observed feature changes highlighted by probing the neuronal populations may reflect the phenomenon of critical slowing down, where increased recovery times from perturbations may signal the loss of a systems' resilience and are common hallmarks of an impending critical transition. These results provide more evidence that active probing approaches highlight information about underlying system changes involved in ictogenesis and may be able to play a role in assisting seizure forecasting methods which can be incorporated into early-warning systems that ultimately enable closing the loop for targeted seizure-controlling interventions.
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Affiliation(s)
- Vinícius Rezende Carvalho
- Programa de Pós-Graduação em Engenharia Elétrica, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Núcleo de Neurociências, Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Márcio Flávio Dutra Moraes
- Núcleo de Neurociências, Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Centro de Tecnologia e Pesquisa em Magneto-Ressonância, Escola de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Sydney S. Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Eduardo Mazoni Andrade Marçal Mendes
- Programa de Pós-Graduação em Engenharia Elétrica, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Núcleo de Neurociências, Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Centro de Tecnologia e Pesquisa em Magneto-Ressonância, Escola de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
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22
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Höller Y, Nardone R. Quantitative EEG biomarkers for epilepsy and their relation to chemical biomarkers. Adv Clin Chem 2020; 102:271-336. [PMID: 34044912 DOI: 10.1016/bs.acc.2020.08.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The electroencephalogram (EEG) is the most important method to diagnose epilepsy. In clinical settings, it is evaluated by experts who identify patterns visually. Quantitative EEG is the application of digital signal processing to clinical recordings in order to automatize diagnostic procedures, and to make patterns visible that are hidden to the human eye. The EEG is related to chemical biomarkers, as electrical activity is based on chemical signals. The most well-known chemical biomarkers are blood laboratory tests to identify seizures after they have happened. However, research on chemical biomarkers is much less extensive than research on quantitative EEG, and combined studies are rarely published, but highly warranted. Quantitative EEG is as old as the EEG itself, but still, the methods are not yet standard in clinical practice. The most evident application is an automation of manual work, but also a quantitative description and localization of interictal epileptiform events as well as seizures can reveal important hints for diagnosis and contribute to presurgical evaluation. In addition, the assessment of network characteristics and entropy measures were found to reveal important insights into epileptic brain activity. Application scenarios of quantitative EEG in epilepsy include seizure prediction, pharmaco-EEG, treatment monitoring, evaluation of cognition, and neurofeedback. The main challenges to quantitative EEG are poor reliability and poor generalizability of measures, as well as the need for individualization of procedures. A main hindrance for quantitative EEG to enter clinical routine is also that training is not yet part of standard curricula for clinical neurophysiologists.
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Affiliation(s)
- Yvonne Höller
- Faculty of Psychology, University of Akureyri, Akureyri, Iceland.
| | - Raffaele Nardone
- Department of Neurology, Franz Tappeiner Hospital, Merano, Italy; Spinal Cord Injury and Tissue Regeneration Center, Salzburg, Austria; Department of Neurology, Christian Doppler Klinik, Paracelsus Medical University, Salzburg, Austria
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23
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Stirling RE, Cook MJ, Grayden DB, Karoly PJ. Seizure forecasting and cyclic control of seizures. Epilepsia 2020; 62 Suppl 1:S2-S14. [PMID: 32712968 DOI: 10.1111/epi.16541] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 04/23/2020] [Accepted: 04/27/2020] [Indexed: 02/02/2023]
Abstract
Epilepsy is a unique neurologic condition characterized by recurrent seizures, where causes, underlying biomarkers, triggers, and patterns differ across individuals. The unpredictability of seizures can heighten fear and anxiety in people with epilepsy, making it difficult to take part in day-to-day activities. Epilepsy researchers have prioritized developing seizure prediction algorithms to combat episodic seizures for decades, but the utility and effectiveness of prediction algorithms has not been investigated thoroughly in clinical settings. In contrast, seizure forecasts, which theoretically provide the probability of a seizure at any time (as opposed to predicting the next seizure occurrence), may be more feasible. Many advances have been made over the past decade in the field of seizure forecasting, including improvements in algorithms as a result of machine learning and exploration of non-EEG-based measures of seizure susceptibility, such as physiological biomarkers, behavioral changes, environmental drivers, and cyclic seizure patterns. For example, recent work investigating periodicities in individual seizure patterns has determined that more than 90% of people have circadian rhythms in their seizures, and many also experience multiday, weekly, or longer cycles. Other potential indicators of seizure susceptibility include stress levels, heart rate, and sleep quality, all of which have the potential to be captured noninvasively over long time scales. There are many possible applications of a seizure-forecasting device, including improving quality of life for people with epilepsy, guiding treatment plans and medication titration, optimizing presurgical monitoring, and focusing scientific research. To realize this potential, it is vital to better understand the user requirements of a seizure-forecasting device, continue to advance forecasting algorithms, and design clear guidelines for prospective clinical trials of seizure forecasting.
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Affiliation(s)
- Rachel E Stirling
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Vic., Australia
| | - Mark J Cook
- Graeme Clark Institute & St Vincent's Hospital, The University of Melbourne, Melbourne, Vic., Australia
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Vic., Australia
| | - Philippa J Karoly
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Vic., Australia.,Graeme Clark Institute & St Vincent's Hospital, The University of Melbourne, Melbourne, Vic., Australia
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24
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Vieluf S, Reinsberger C, El Atrache R, Jackson M, Schubach S, Ufongene C, Loddenkemper T, Meisel C. Autonomic nervous system changes detected with peripheral sensors in the setting of epileptic seizures. Sci Rep 2020; 10:11560. [PMID: 32665704 PMCID: PMC7360606 DOI: 10.1038/s41598-020-68434-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 06/15/2020] [Indexed: 11/09/2022] Open
Abstract
A better understanding of the early detection of seizures is highly desirable as identification of an impending seizure may afford improved treatments, such as antiepileptic drug chronotherapy, or timely warning to patients. While epileptic seizures are known to often manifest also with autonomic nervous system (ANS) changes, it is not clear whether ANS markers, if recorded from a wearable device, are also informative about an impending seizure with statistically significant sensitivity and specificity. Using statistical testing with seizure surrogate data and a unique dataset of continuously recorded multi-day wristband data including electrodermal activity (EDA), temperature (TEMP) and heart rate (HR) from 66 people with epilepsy (9.9 ± 5.8 years; 27 females; 161 seizures) we investigated differences between inter- and preictal periods in terms of mean, variance, and entropy of these signals. We found that signal mean and variance do not differentiate between inter- and preictal periods in a statistically meaningful way. EDA signal entropy was found to be increased prior to seizures in a small subset of patients. Findings may provide novel insights into the pathophysiology of epileptic seizures with respect to ANS function, and, while further validation and investigation of potential causes of the observed changes are needed, indicate that epilepsy-related state changes may be detectable using peripheral wearable devices. Detection of such changes with wearable devices may be more feasible for everyday monitoring than utilizing an electroencephalogram.
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Affiliation(s)
- Solveig Vieluf
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA.,Institute of Sports Medicine, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany
| | - Claus Reinsberger
- Institute of Sports Medicine, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany.,Edward E. Bromfield Epilepsy Center, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Rima El Atrache
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA
| | - Michele Jackson
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA
| | - Sarah Schubach
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA
| | - Claire Ufongene
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA
| | - Christian Meisel
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA. .,Department of Neurology, University Clinic Carl Gustav Carus, Fetscherstraße 74, Dresden, 01307, Germany.
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25
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DAS A, Cash SS, Sejnowski TJ. Heterogeneity of Preictal Dynamics in Human Epileptic Seizures. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:52738-52748. [PMID: 32411567 PMCID: PMC7224217 DOI: 10.1109/access.2020.2981017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
It is generally understood that there is a preictal phase in the development of a seizure and this precictal period is the basis for seizure prediction attempts. The focus of this study is the preictal global spatiotemporal dynamics and its intra-patient variability. We analyzed preictal broadband brain connectivity from human electrocorticography (ECoG) recordings of 185 seizures (which included 116 clinical seizures) collected from 12 patients. ECoG electrodes record from only a part of the cortex, leaving large regions of the brain unobserved. Brain connectivity was therefore estimated using the sparse-plus-latent-regularized precision matrix (SLRPM) method, which calculates connectivity from partial correlations of the conditional statistics of the observed regions given the unobserved latent regions. Brain connectivity was quantified using eigenvector centrality (EC), from which a degree of heterogeneity was calculated for the preictal periods of all seizures in each patient. Results from the SLRPM method are compared to those from the sparse-regularized precision matrix (SRPM) and correlation methods, which do not account for the unobserved inputs when estimating brain connectivity. The degree of heterogeneity estimated by the SLRPM method is higher than those estimated by the SRPM and correlation methods for the preictal periods in most patients. These results reveal substantial heterogeneity or desynchronization among brain areas in the preictal period of human epileptic seizures. Furthermore, the SLRPM method identifies more onset channels from the preictal active electrodes compared to the SRPM and correlation methods. Finally, the correlation between the degree of heterogeneity and seizure severity of patients for SLRPM and SRPM methods were lower than that obtained from the correlation method. These results support recent findings suggesting that inhibitory neurons can have anti-seizure effects by inducing variability or heterogeneity across seizures. Understanding how this variability is linked to seizure initiation may lead to better predictions and controlling therapies.
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Affiliation(s)
- Anup DAS
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA 94305 USA
| | - Sydney S Cash
- Massachusetts General Hospital, Boston, MA 02114 USA
| | - Terrence J Sejnowski
- Division of Biological Sciences and Institute of Neural Computation, University of California, San Diego, La Jolla, CA 92093 USA
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26
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An efficient error-minimized random vector functional link network for epileptic seizure classification using VMD. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101787] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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27
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Abbasi H, Unsworth CP. Applications of advanced signal processing and machine learning in the neonatal hypoxic-ischemic electroencephalogram. Neural Regen Res 2020; 15:222-231. [PMID: 31552887 PMCID: PMC6905345 DOI: 10.4103/1673-5374.265542] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 05/24/2019] [Indexed: 01/15/2023] Open
Abstract
Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.
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Affiliation(s)
- Hamid Abbasi
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
| | - Charles P. Unsworth
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
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28
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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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29
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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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30
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Schevon CA, Tobochnik S, Eissa T, Merricks E, Gill B, Parrish RR, Bateman LM, McKhann GM, Emerson RG, Trevelyan AJ. Multiscale recordings reveal the dynamic spatial structure of human seizures. Neurobiol Dis 2019; 127:303-311. [PMID: 30898669 PMCID: PMC6588430 DOI: 10.1016/j.nbd.2019.03.015] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 03/11/2019] [Accepted: 03/15/2019] [Indexed: 02/07/2023] Open
Abstract
The cellular activity underlying human focal seizures, and its relationship to key signatures in the EEG recordings used for therapeutic purposes, has not been well characterized despite many years of investigation both in laboratory and clinical settings. The increasing use of microelectrodes in epilepsy surgery patients has made it possible to apply principles derived from laboratory research to the problem of mapping the spatiotemporal structure of human focal seizures, and characterizing the corresponding EEG signatures. In this review, we describe results from human microelectrode studies, discuss some data interpretation pitfalls, and explain the current understanding of the key mechanisms of ictogenesis and seizure spread.
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Affiliation(s)
- Catherine A Schevon
- Department of Neurology, Columbia University Medical Center, New York, NY, USA.
| | - Steven Tobochnik
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Tahra Eissa
- Department of Applied Mathematics, University of Colorado at Boulder, Boulder, CO, USA
| | - Edward Merricks
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Brian Gill
- Department of Neurological Surgery, Columbia University Medical Center, New York, NY, USA
| | - R Ryley Parrish
- Institute for Aging, Newcastle University, Newcastle-Upon-Tyne, UK
| | - Lisa M Bateman
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Guy M McKhann
- Department of Neurological Surgery, Columbia University Medical Center, New York, NY, USA
| | - Ronald G Emerson
- Department of Neurology, Weill Cornell Medical Center, New York, NY, USA
| | - Andrew J Trevelyan
- Department of Neurology, Columbia University Medical Center, New York, NY, USA; Institute for Aging, Newcastle University, Newcastle-Upon-Tyne, UK
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31
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Amengual-Gual M, Ulate-Campos A, Loddenkemper T. Status epilepticus prevention, ambulatory monitoring, early seizure detection and prediction in at-risk patients. Seizure 2019; 68:31-37. [DOI: 10.1016/j.seizure.2018.09.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 08/16/2018] [Accepted: 09/15/2018] [Indexed: 02/08/2023] Open
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32
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Shirzhiyan Z, Keihani A, Farahi M, Shamsi E, GolMohammadi M, Mahnam A, Haidari MR, Jafari AH. Introducing chaotic codes for the modulation of code modulated visual evoked potentials (c-VEP) in normal adults for visual fatigue reduction. PLoS One 2019; 14:e0213197. [PMID: 30840671 PMCID: PMC6402685 DOI: 10.1371/journal.pone.0213197] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 02/16/2019] [Indexed: 11/19/2022] Open
Abstract
Code modulated Visual Evoked Potentials (c-VEP) based BCI studies usually employ m-sequences as a modulating codes for their broadband spectrum and correlation property. However, subjective fatigue of the presented codes has been a problem. In this study, we introduce chaotic codes containing broadband spectrum and similar correlation property. We examined whether the introduced chaotic codes could be decoded from EEG signals and also compared the subjective fatigue level with m-sequence codes in normal subjects. We generated chaotic code from one-dimensional logistic map and used it with conventional 31-bit m-sequence code. In a c-VEP based study in normal subjects (n = 44, 21 females) we presented these codes visually and recorded EEG signals from the corresponding codes for their four lagged versions. Canonical correlation analysis (CCA) and spatiotemporal beamforming (STB) methods were used for target identification and comparison of responses. Additionally, we compared the subjective self-declared fatigue using VAS caused by presented m-sequence and chaotic codes. The introduced chaotic code was decoded from EEG responses with CCA and STB methods. The maximum total accuracy values of 93.6 ± 11.9% and 94 ± 14.4% were achieved with STB method for chaotic and m-sequence codes for all subjects respectively. The achieved accuracies in all subjects were not significantly different in m-sequence and chaotic codes. There was significant reduction in subjective fatigue caused by chaotic codes compared to the m-sequence codes. Both m-sequence and chaotic codes were similar in their accuracies as evaluated by CCA and STB methods. The chaotic codes significantly reduced subjective fatigue compared to the m-sequence codes.
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Affiliation(s)
- Zahra Shirzhiyan
- Medical Physics & Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
| | - Ahmadreza Keihani
- Medical Physics & Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
| | - Morteza Farahi
- Medical Physics & Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
| | - Elham Shamsi
- Medical Physics & Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
| | - Mina GolMohammadi
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
| | - Amin Mahnam
- Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
| | - Mohsen Reza Haidari
- Section of Neuroscience, Department of Neurology, Faculty of Medicine, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Amir Homayoun Jafari
- Medical Physics & Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
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33
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Hata K, Fujiwara K, Inoue T, Abe T, Kubo T, Yamakawa T, Nomura S, Imoto H, Suzuki M, Kano M. Epileptic Seizure Suppression by Focal Brain Cooling With Recirculating Coolant Cooling System: Modeling and Simulation. IEEE Trans Neural Syst Rehabil Eng 2019; 27:162-171. [PMID: 30624219 DOI: 10.1109/tnsre.2019.2891090] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
A focal brain cooling system for treatment of refractory epilepsy that is implantable and wearable may permit patients with this condition to lead normal daily lives. We have developed such a system for cooling of the epileptic focus by delivery of cold saline to a cooling device that is implanted cranially. The outflow is pumped for circulation and cooled by a Peltier device. Here, we describe the design of the system and evaluate its feasibility by simulation. Mathematical models were constructed based on equations of fluid dynamics and data from a cat model. Computational fluid dynamics simulations gave the following results: 1) a cooling device with a complex channel structure gives a more uniform temperature in the brain; 2) a cooling period of <10 min is required to reach an average temperature of 25.0°Cat 2 mm below the brain surface, which is the target temperature for seizure suppression. This time is short enough for cooling of the brain before seizure onset after seizure prediction by an intracranial electroencephalogram-based algorithm; and 3) battery charging would be required once every several days for most patients. These results suggest that the focal brain cooling system may be clinically applicable.
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34
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Amengual-Gual M, Sánchez Fernández I, Loddenkemper T. Patterns of epileptic seizure occurrence. Brain Res 2019; 1703:3-12. [DOI: 10.1016/j.brainres.2018.02.032] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2017] [Revised: 12/03/2017] [Accepted: 02/20/2018] [Indexed: 01/03/2023]
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35
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Wu M, Wan T, Ding M, Wan X, Du Y, She J. A New Unsupervised Detector of High-Frequency Oscillations in Accurate Localization of Epileptic Seizure Onset Zones. IEEE Trans Neural Syst Rehabil Eng 2018; 26:2280-2289. [DOI: 10.1109/tnsre.2018.2877820] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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36
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Kueh SM, Kazmierski TJ. Low-Power and Low-Cost Dedicated Bit-Serial Hardware Neural Network for Epileptic Seizure Prediction System. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2018; 6:2000209. [PMID: 30324037 PMCID: PMC6175036 DOI: 10.1109/jtehm.2018.2867864] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Revised: 07/31/2018] [Accepted: 08/13/2018] [Indexed: 11/07/2022]
Abstract
This paper presents results of using a simple bit-serial architecture as a method of designing an extremely low-power and low-cost neural network processor for epilepsy seizure prediction. The proposed concept is based on a novel bit-serial data processing unit (DPU) which implements the functionality of a complete neuron and uses bit-serial arithmetic. Arrays of DPUs are controlled by simple finite state machines. We show that epilepsy detection through such dedicated neural hardware is feasible and may facilitate development of wearable, low-cost and low-energy personalized seizure prediction equipment. The proposed processor extracts epileptic seizure characteristics from electroencephalogram (EEG) waveforms. In order to facilitate the classification of EEG waveforms, we develop a dedicated feature extraction hardware that provides inputs to the neural network. This approach has been tested using various network configurations and has been compared with related work. A complete system which can predict epileptic seizures with high accuracy has been implemented on an ALTERA Cyclone V FPGA using 3931 ALMs which constitutes about 7% of the Cyclone V A7 capacity. The design has a prediction accuracy of 90%.
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Affiliation(s)
- Si Mon Kueh
- Faculty of Physical Science and EngineeringUniversity of SouthamptonSouthamptonSO17 1BJU.K
| | - Tom J Kazmierski
- Faculty of Physical Science and EngineeringUniversity of SouthamptonSouthamptonSO17 1BJU.K
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37
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Manzouri F, Heller S, Dümpelmann M, Woias P, Schulze-Bonhage A. A Comparison of Machine Learning Classifiers for Energy-Efficient Implementation of Seizure Detection. Front Syst Neurosci 2018; 12:43. [PMID: 30294263 PMCID: PMC6158331 DOI: 10.3389/fnsys.2018.00043] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2016] [Accepted: 08/27/2018] [Indexed: 11/29/2022] Open
Abstract
The closed-loop application of electrical stimulation via chronically implanted electrodes is a novel approach to stop seizures in patients with focal-onset epilepsy. To this end, an energy efficient seizure detector that can be implemented in an implantable device is of crucial importance. In this study, we first evaluated the performance of two machine learning algorithms (Random Forest classifier and support vector machine (SVM)) by using selected time and frequency domain features with a limited need of computational resources. Performance of the algorithms was further compared to a detection strategy implemented in an existing closed loop neurostimulation device for the treatment of epilepsy. The results show a superior performance of the Random Forest classifier compared to the SVM classifier and the reference approach. Next, we implemented the feature extraction and classification process of the Random Forest classifier on a microcontroller to evaluate the energy efficiency of this seizure detector. In conclusion, the feature set in combination with Random Forest classifier is an energy efficient hardware implementation that shows an improvement of detection sensitivity and specificity compared to the presently available closed-loop intervention in epilepsy while preserving a low detection delay.
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Affiliation(s)
- Farrokh Manzouri
- Epilepsy Center, Faculty of Medicine, University of Freiburg Medical Center, Freiburg, Germany.,BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany
| | - Simon Heller
- BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany.,Department of Microsystems Engineering, Faculty of Engineering, University of Freiburg, Freiburg, Germany
| | - Matthias Dümpelmann
- Epilepsy Center, Faculty of Medicine, University of Freiburg Medical Center, Freiburg, Germany.,BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany
| | - Peter Woias
- BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany.,Department of Microsystems Engineering, Faculty of Engineering, University of Freiburg, Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Faculty of Medicine, University of Freiburg Medical Center, Freiburg, Germany.,BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany
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38
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Al-Kaysi AM, Al-Ani A, Galvez V, Colleen Loo K, Ling S, Tjeerd Boonstra W. Estimating The Quality of Electroconvulsive Therapy Induced Seizures Using Decision Tree and Fuzzy Inference System Classifiers. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:3677-3680. [PMID: 30441170 DOI: 10.1109/embc.2018.8513334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Electroconvulsive therapy (ECT) is an effective and widely used treatment for major depressive disorder, in which a brief electric current is passed through the brain to trigger a brief seizure. This study aims to identify seizure quality rating by utilizing a set of seizure parameters. We used 750 ECT EEG recordings in this experiment. Four seizure related parameters, (time of slowing, regularity, stereotypy and post-ictal suppression) are used as inputs to two classifiers, decision tree and fuzzy inference system (FIS), to predict seizure quality ratings. The two classifiers produced encouraging results with error rate of 0.31 and 0.25 for FIS and decision tree, respectively. The classification results show that the four seizure parameters provide relevant information about the rating of seizure quality. Automatic scoring of seizure quality may be beneficial to clinicians working in this field.
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39
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Li Hegner Y, Marquetand J, Elshahabi A, Klamer S, Lerche H, Braun C, Focke NK. Increased Functional MEG Connectivity as a Hallmark of MRI-Negative Focal and Generalized Epilepsy. Brain Topogr 2018; 31:863-874. [PMID: 29766384 DOI: 10.1007/s10548-018-0649-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Accepted: 05/08/2018] [Indexed: 01/13/2023]
Abstract
Epilepsy is one of the most prevalent neurological diseases with a high morbidity. Accumulating evidence has shown that epilepsy is an archetypical neural network disorder. Here we developed a non-invasive cortical functional connectivity analysis based on magnetoencephalography (MEG) to assess commonalities and differences in the network phenotype in different epilepsy syndromes (non-lesional/cryptogenic focal and idiopathic/genetic generalized epilepsy). Thirty-seven epilepsy patients with normal structural brain anatomy underwent a 30-min resting state MEG measurement with eyes closed. We only analyzed interictal epochs without epileptiform discharges. The imaginary part of coherency was calculated as an indicator of cortical functional connectivity in five classical frequency bands. This connectivity measure was computed between all sources on individually reconstructed cortical surfaces that were surface-aligned to a common template. In comparison to healthy controls, both focal and generalized epilepsy patients showed widespread increased functional connectivity in several frequency bands, demonstrating the potential of elevated functional connectivity as a common pathophysiological hallmark in different epilepsy types. Furthermore, the comparison between focal and generalized epilepsies revealed increased network connectivity in bilateral mesio-frontal and motor regions specifically for the generalized epilepsy patients. Our study indicated that the surface-based normalization of MEG sources of individual brains enables the comparison of imaging findings across subjects and groups on a united platform, which leads to a straightforward and effective disclosure of pathological network characteristics in epilepsy. This approach may allow for the definition of more specific markers of different epilepsy syndromes, and increased MEG-based resting-state functional connectivity seems to be a common feature in MRI-negative epilepsy syndromes.
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Affiliation(s)
- Yiwen Li Hegner
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Hope-Seyler-Straße 3, 72076, Tübingen, Germany.
| | - Justus Marquetand
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Hope-Seyler-Straße 3, 72076, Tübingen, Germany
| | - Adham Elshahabi
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Hope-Seyler-Straße 3, 72076, Tübingen, Germany.,Werner Reichardt Centre for Integrative Neuroscience, Tübingen, Germany
| | - Silke Klamer
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Hope-Seyler-Straße 3, 72076, Tübingen, Germany
| | - Holger Lerche
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Hope-Seyler-Straße 3, 72076, Tübingen, Germany.,Werner Reichardt Centre for Integrative Neuroscience, Tübingen, Germany
| | - Christoph Braun
- Werner Reichardt Centre for Integrative Neuroscience, Tübingen, Germany.,MEG Center, University of Tübingen, Tübingen, Germany.,CIMeC, Center for Mind/Brain Sciences, University of Trento, Trento, Italy
| | - Niels K Focke
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Hope-Seyler-Straße 3, 72076, Tübingen, Germany.,Werner Reichardt Centre for Integrative Neuroscience, Tübingen, Germany.,Clinical Neurophysiology, University of Göttingen, Göttingen, Germany
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40
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Li Y, Wang XD, Luo ML, Li K, Yang XF, Guo Q. Epileptic Seizure Classification of EEGs Using Time–Frequency Analysis Based Multiscale Radial Basis Functions. IEEE J Biomed Health Inform 2018; 22:386-397. [DOI: 10.1109/jbhi.2017.2654479] [Citation(s) in RCA: 82] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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41
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Yuan S, Zhou W, Chen L. Epileptic Seizure Prediction Using Diffusion Distance and Bayesian Linear Discriminate Analysis on Intracranial EEG. Int J Neural Syst 2017; 28:1750043. [DOI: 10.1142/s0129065717500435] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Epilepsy is a chronic neurological disorder characterized by sudden and apparently unpredictable seizures. A system capable of forecasting the occurrence of seizures is crucial and could open new therapeutic possibilities for human health. This paper addresses an algorithm for seizure prediction using a novel feature — diffusion distance (DD) in intracranial Electroencephalograph (iEEG) recordings. Wavelet decomposition is conducted on segmented electroencephalograph (EEG) epochs and subband signals at scales 3, 4 and 5 are utilized to extract the diffusion distance. The features of all channels composing a feature vector are then fed into a Bayesian Linear Discriminant Analysis (BLDA) classifier. Finally, postprocessing procedure is applied to reduce false prediction alarms. The prediction method is evaluated on the public intracranial EEG dataset, which consists of 577.67[Formula: see text]h of intracranial EEG recordings from 21 patients with 87 seizures. We achieved a sensitivity of 85.11% for a seizure occurrence period of 30[Formula: see text]min and a sensitivity of 93.62% for a seizure occurrence period of 50[Formula: see text]min, both with the seizure prediction horizon of 10[Formula: see text]s. Our false prediction rate was 0.08/h. The proposed method yields a high sensitivity as well as a low false prediction rate, which demonstrates its potential for real-time prediction of seizures.
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Affiliation(s)
- Shasha Yuan
- School of Microelectronics, Shandong University, Jinan 250100, P. R. China
- School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, P. R. China
| | - Weidong Zhou
- School of Microelectronics, Shandong University, Jinan 250100, P. R.China
| | - Liyan Chen
- School of Microelectronics, Shandong University, Jinan 250100, P. R.China
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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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Hata K, Fujiwara K, Kano M, Inoue T, Nomura S, Imoto H, Suzuki M. Design of focal brain cooling system for suppressing epileptic seizures. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:283-286. [PMID: 29059865 DOI: 10.1109/embc.2017.8036817] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Epilepsy is a group of diseases caused by excessive neuronal activities, and one-quarter of the patients do not become seizure-free by the existing treatments. The potential treatments include focal brain cooling, which aims to cool the region where the excessive neuronal activities begin. We are developing a focal brain cooling system. The system delivers cold saline to a cranially implanted cooling device. The outflow is cooled by a Peltier device and pumped for circulation. The Peltier device and the pump are activated only when a seizure is predicted. In this research, the length of time for cooling the brain was calculated with a computational fluid dynamics (CFD)-based model of the focal brain cooling system. As a result, it takes less than 10 minutes for the average temperature 2 mm below the cooling device to reach 25.0 °C. It is much shorter than the time from seizure prediction to seizure onset when an existing algorithm for prediction is used.
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Karoly PJ, Ung H, Grayden DB, Kuhlmann L, Leyde K, Cook MJ, Freestone DR. The circadian profile of epilepsy improves seizure forecasting. Brain 2017; 140:2169-2182. [PMID: 28899023 DOI: 10.1093/brain/awx173] [Citation(s) in RCA: 121] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Accepted: 05/20/2017] [Indexed: 01/20/2023] Open
Abstract
It is now established that epilepsy is characterized by periodic dynamics that increase seizure likelihood at certain times of day, and which are highly patient-specific. However, these dynamics are not typically incorporated into seizure prediction algorithms due to the difficulty of estimating patient-specific rhythms from relatively short-term or unreliable data sources. This work outlines a novel framework to develop and assess seizure forecasts, and demonstrates that the predictive power of forecasting models is improved by circadian information. The analyses used long-term, continuous electrocorticography from nine subjects, recorded for an average of 320 days each. We used a large amount of out-of-sample data (a total of 900 days for algorithm training, and 2879 days for testing), enabling the most extensive post hoc investigation into seizure forecasting. We compared the results of an electrocorticography-based logistic regression model, a circadian probability, and a combined electrocorticography and circadian model. For all subjects, clinically relevant seizure prediction results were significant, and the addition of circadian information (combined model) maximized performance across a range of outcome measures. These results represent a proof-of-concept for implementing a circadian forecasting framework, and provide insight into new approaches for improving seizure prediction algorithms. The circadian framework adds very little computational complexity to existing prediction algorithms, and can be implemented using current-generation implant devices, or even non-invasively via surface electrodes using a wearable application. The ability to improve seizure prediction algorithms through straightforward, patient-specific modifications provides promise for increased quality of life and improved safety for patients with epilepsy.
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Affiliation(s)
- Philippa J Karoly
- Department of Medicine, The University of Melbourne, St. Vincent's Hospital, Fitzroy VIC 3065, Australia.,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.,NeuroEngineering Research Laboratory, Department of Biomedical Engineering, The University of Melbourne, Parkville VIC 3010, Australia
| | - Hoameng Ung
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - David B Grayden
- NeuroEngineering Research Laboratory, Department of Biomedical Engineering, The University of Melbourne, Parkville VIC 3010, Australia
| | - Levin Kuhlmann
- Department of Medicine, The University of Melbourne, St. Vincent's Hospital, Fitzroy VIC 3065, Australia.,Brain Dynamics Lab, Centre for Human Psychopharmacology, Swinburne University of Technology, Hawthorne VIC 3122, Australia
| | - Kent Leyde
- Cascade Neuroscience, Seattle, WA 98109, USA
| | - Mark J Cook
- Department of Medicine, The University of Melbourne, St. Vincent's Hospital, Fitzroy VIC 3065, Australia.,Graeme Clark Institute for Biomedical Engineering, The University of Melbourne
| | - Dean R Freestone
- Department of Medicine, The University of Melbourne, St. Vincent's Hospital, Fitzroy VIC 3065, Australia
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45
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Electrophysiological investigation of human embryonic stem cell derived neurospheres using a novel spike detection algorithm. Biosens Bioelectron 2017; 100:462-468. [PMID: 28963963 DOI: 10.1016/j.bios.2017.09.034] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 08/10/2017] [Accepted: 09/18/2017] [Indexed: 02/07/2023]
Abstract
Microelectrode array (MEA) technology in combination with three-dimensional (3D) neuronal cell models derived from human embryonic stem cells (hESC) provide an excellent tool for neurotoxicity screening. Yet, there are significant challenges in terms of data processing and analysis, since neuronal signals have very small amplitudes and the 3D structure enhances the level of background noise. Thus, neuronal signal analysis requires the application of highly sophisticated algorithms. In this study, we present a new approach optimized for the detection of spikes recorded from 3D neurospheres (NS) with a very low signal-to-noise ratio. This was achieved by extending simple threshold-based spike detection utilizing a highly sensitive algorithm named SWTTEO. This analysis procedure was applied to data obtained from hESC-derived NS grown on MEA chips. Specifically, we examined changes in the activity pattern occurring within the first ten days of electrical activity. We further analyzed the response of NS to the GABA receptor antagonist bicuculline. With this new algorithm method we obtained more reliable results compared to the simple threshold-based spike detection.
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46
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Analysis of variations of correlation dimension and nonlinear interdependence for the prediction of pediatric myoclonic seizures – A preliminary study. Epilepsy Res 2017; 135:102-114. [DOI: 10.1016/j.eplepsyres.2017.06.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Revised: 05/20/2017] [Accepted: 06/16/2017] [Indexed: 01/23/2023]
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47
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Wen T, Kramer DR, Sirot S, Ho L, Moalem AS, Cen SY, Millett D, Heck C, Robison RA, Mack WJ, Liu CY. The Weekend Effect on Morbidity and Mortality Among Pediatric Epilepsy Admissions. Pediatr Neurol 2017; 74:24-31.e1. [PMID: 28676248 DOI: 10.1016/j.pediatrneurol.2017.05.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2017] [Revised: 04/19/2017] [Accepted: 05/28/2017] [Indexed: 11/16/2022]
Abstract
BACKGROUND Pediatric epilepsy is one of the most common neurological disorders with low mortality and high morbidity, often requiring hospitalization. Weekend admissions have been shown to be associated with worse outcomes compared with their weekday counterparts. To date, no study has assessed the impact of weekend admission on clinical and quality outcomes in the pediatric epilepsy population. METHODS Children with epilepsy were identified from the 2000, 2003, 2006, and 2009 Kids Inpatient Database. Quality outcomes were identified using the Centers of Medicare and Medicaid Services' hospital acquired conditions International Classification of Diseases, Ninth Edition; Clinical Modification (ICD-9CM) codes. Multivariable analyses were conducted to assess the association between weekend admission and inpatient mortality and hospital acquired condition occurrence. RESULTS A total of 526,765 pediatric epilepsy discharges were identified, with 80% occurring on weekdays and 20% on weekends. Overall, the hospital acquired condition rate was 3.6% (3.2% vs 5.2% for weekday versus weekend) and inpatient mortality was 1.5% (1.2% vs 1.7%). Patients admitted on the weekend had 28% higher rates of hospital acquired conditions and 21% higher inpatient mortality rates compared with their weekday counterparts. Patients seen at nonpediatric centers had 10% to 28% lower rates of mortality, but 5% to 13% higher hospital acquired condition rates than those at pediatric centers. CONCLUSIONS Weekend admission is significantly associated with worse clinical and quality outcomes compared with weekday admissions among pediatric epilepsy inpatients. Weekend admissions likely represent unplanned, at risk admissions, coupled with less staffing. Further study is needed to isolate clinical and systemic factors to decrease this disparity in this highly comorbid pediatric subgroup.
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Affiliation(s)
- Timothy Wen
- Keck School of Medicine of USC, University of Southern California, Los Angeles, California
| | - Daniel R Kramer
- Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, California
| | - Steve Sirot
- Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, California
| | - Lianne Ho
- Keck School of Medicine of USC, University of Southern California, Los Angeles, California
| | - Alimohammad S Moalem
- Keck School of Medicine of USC, University of Southern California, Los Angeles, California
| | - Steven Y Cen
- Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, California; Department of Neurology, Keck School of Medicine of USC, Los Angeles, California
| | - David Millett
- Department of Neurology, Keck School of Medicine of USC, Los Angeles, California
| | - Christianne Heck
- Department of Neurology, Keck School of Medicine of USC, Los Angeles, California
| | - R Aaron Robison
- Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, California; Division of Neurosurgery, Children's Hospital Los Angeles, Los Angeles, California
| | - William J Mack
- Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, California.
| | - Charles Y Liu
- Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, California
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Aarabi A, He B. Seizure prediction in patients with focal hippocampal epilepsy. Clin Neurophysiol 2017; 128:1299-1307. [PMID: 28554147 DOI: 10.1016/j.clinph.2017.04.026] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2017] [Revised: 04/24/2017] [Accepted: 04/26/2017] [Indexed: 11/25/2022]
Abstract
OBJECTIVE We evaluated the performance of our previously developed seizure prediction approach on thirty eight seizures from ten patients with focal hippocampal epilepsy. METHODS The seizure prediction system was developed based on the extraction of correlation dimension, correlation entropy, noise level, Lempel-Ziv complexity, largest Lyapunov exponent, and nonlinear interdependence from segments of intracranial EEG. RESULTS Our results showed an average sensitivity of 86.7% and 92.9%, an average false prediction rate of 0.126 and 0.096/h, and an average minimum prediction time of 14.3 and 33.3min, respectively, using seizure occurrence periods of 30 and 50min and a seizure prediction horizon of 10s. Two-third of the analyzed seizures showed significantly increased complexity in periods prior to the seizures in comparison with baseline. In four patients, strong bidirectional connectivities between epileptic contacts and the surrounding areas were observed. However, in five patients, unidirectional functional connectivities in preictal periods were observed from remote areas to epileptogenic zones. CONCLUSIONS Overall, preictal periods in patients with focal hippocampal epilepsy were characterized with patient-specific changes in univariate and bivariate nonlinear measures. SIGNIFICANCE The spatio-temporal characterization of preictal periods may help to better understand the mechanism underlying seizure generation in patients with focal hippocampal epilepsy.
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Affiliation(s)
- Ardalan Aarabi
- GRAMFC Inserm U1105, University Research Center, University of Picardie-Jules Verne, CHU AMIENS - SITE SUD, Avenue Laennec, 80054 Amiens, France; Faculty of Medicine, University of Picardie Jules Verne, Amiens 80036, France.
| | - Bin He
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA; Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN 55455, USA
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Potentially preventable complications in epilepsy admissions: The "weekend effect". Epilepsy Behav 2017; 70:50-56. [PMID: 28410465 DOI: 10.1016/j.yebeh.2017.03.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Revised: 02/26/2017] [Accepted: 03/04/2017] [Indexed: 11/20/2022]
Abstract
INTRODUCTION Epilepsy affects approximately 1% of the population in the United States with frequent hospital admissions accounting for a significant burden on patients and society as a whole. Weekend admissions have generally been found to have poorer outcomes compared to weekday admissions with increased rates of preventable complications, such as nationally identified "hospital-acquired conditions" (HAC). OBJECTIVE This study aimed to assess the impact of weekend admission on HACs and mortality in the adult epilepsy population. PARTICIPANTS All adult patients with epilepsy hospitalized in the U.S. from 2000 to 2010 in the Nationwide Inpatient Sample. RESULTS There were 12,997,181 admissions for epilepsy with 10,106,152 (78%) weekday, 2,891,019 (22%) weekend, and 10 (<0.1%) missing admissions. Weekend admissions saw a 10% increased likelihood of both HACs (RR=1.10, 95% CI:1.09, 1.11, p<0.01) and mortality (RR=1.10, 95% CI: 1.09, 1.11, p<0.01) compared to weekday admissions. The occurrence of HAC was associated with higher inpatient charges (RR=1.36, 95% CI: 1.35, 1.36, p<0.01), pLOS (RR=1.21, 95% CI: 1.21, 1.22, p<0.01), and higher mortality (RR=1.13, 95% CI: 1.12, 1.14, p<0.01). CONCLUSION Prior studies have shown weekend admissions are usually associated with higher rates of complications leading to higher costs and a longer hospital stay. Likewise, weekend admissions for epilepsy were associated with increased rates of HACs and mortality; however, they were also negatively associated with LOS and total charge. Thus, weekend admissions for epilepsy should be considered high risk with greater effort made to mitigate these risks.
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50
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Lin LC, Chen SCJ, Chiang CT, Wu HC, Yang RC, Ouyang CS. Classification Preictal and Interictal Stages via Integrating Interchannel and Time-Domain Analysis of EEG Features. Clin EEG Neurosci 2017; 48:139-145. [PMID: 27177554 DOI: 10.1177/1550059416649076] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The life quality of patients with refractory epilepsy is extremely affected by abrupt and unpredictable seizures. A reliable method for predicting seizures is important in the management of refractory epilepsy. A critical factor in seizure prediction involves the classification of the preictal and interictal stages. This study aimed to develop an efficient, automatic, quantitative, and individualized approach for preictal/interictal stage identification. Five epileptic children, who had experienced at least 2 episodes of seizures during a 24-hour video EEG recording, were included. Artifact-free preictal and interictal EEG epochs were acquired, respectively, and characterized with 216 global feature descriptors. The best subset of 5 discriminative descriptors was identified. The best subsets showed differences among the patients. Statistical analysis revealed most of the 5 descriptors in each subset were significantly different between the preictal and interictal stages for each patient. The proposed approach yielded weighted averages of 97.50% correctness, 96.92% sensitivity, 97.78% specificity, and 95.45% precision on classifying test epochs. Although the case number was limited, this study successfully integrated a new EEG analytical method to classify preictal and interictal EEG segments and might be used further in predicting the occurrence of seizures.
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Affiliation(s)
- Lung-Chang Lin
- 1 Department of Pediatrics, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.,2 Department of Pediatrics, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Sharon Chia-Ju Chen
- 3 Department of Medical Imaging and Radiological Sciences, College of Health Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Ching-Tai Chiang
- 4 Department of Computer and Communication, National Pingtung University, Pingtung, Taiwan
| | - Hui-Chuan Wu
- 2 Department of Pediatrics, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Rei-Cheng Yang
- 2 Department of Pediatrics, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.,5 Department of Pediatrics, Changhua Christian Hospital, Changhua, Taiwan
| | - Chen-Sen Ouyang
- 6 Department of Information Engineering, I-Shou University, Kaohsiung, Taiwan
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