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Wei Z, Wang X, Liu C, Feng Y, Gan Y, Shi Y, Wang X, Liu Y, Deng Y. Microstate-based brain network dynamics distinguishing temporal lobe epilepsy patients: A machine learning approach. Neuroimage 2024; 296:120683. [PMID: 38880308 DOI: 10.1016/j.neuroimage.2024.120683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 06/02/2024] [Accepted: 06/10/2024] [Indexed: 06/18/2024] Open
Abstract
Temporal lobe epilepsy (TLE) stands as the predominant adult focal epilepsy syndrome, characterized by dysfunctional intrinsic brain dynamics. However, the precise mechanisms underlying seizures in these patients remain elusive. Our study encompassed 116 TLE patients compared with 51 healthy controls. Employing microstate analysis, we assessed brain dynamic disparities between TLE patients and healthy controls, as well as between drug-resistant epilepsy (DRE) and drug-sensitive epilepsy (DSE) patients. We constructed dynamic functional connectivity networks based on microstates and quantified their spatial and temporal variability. Utilizing these brain network features, we developed machine learning models to discriminate between TLE patients and healthy controls, and between DRE and DSE patients. Temporal dynamics in TLE patients exhibited significant acceleration compared to healthy controls, along with heightened synchronization and instability in brain networks. Moreover, DRE patients displayed notably lower spatial variability in certain parts of microstate B, E and F dynamic functional connectivity networks, while temporal variability in certain parts of microstate E and G dynamic functional connectivity networks was markedly higher in DRE patients compared to DSE patients. The machine learning model based on these spatiotemporal metrics effectively differentiated TLE patients from healthy controls and discerned DRE from DSE patients. The accelerated microstate dynamics and disrupted microstate sequences observed in TLE patients mirror highly unstable intrinsic brain dynamics, potentially underlying abnormal discharges. Additionally, the presence of highly synchronized and unstable activities in brain networks of DRE patients signifies the establishment of stable epileptogenic networks, contributing to the poor responsiveness to antiseizure medications. The model based on spatiotemporal metrics demonstrated robust predictive performance, accurately distinguishing both TLE patients from healthy controls and DRE patients from DSE patients.
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Affiliation(s)
- Zihan Wei
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China
| | - Xinpei Wang
- School of Aerospace Medicine, Fourth Military Medical University, Xi'an, China
| | - Chao Liu
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China
| | - Yan Feng
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China; Xi'an Medical University, Xi'an 710021, PR China
| | - Yajing Gan
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China
| | - Yuqing Shi
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China; Xi'an Medical University, Xi'an 710021, PR China
| | - Xiaoli Wang
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China
| | - Yonghong Liu
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China
| | - Yanchun Deng
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, 127 West Changle Road, Xi'an 710032, PR China.
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2
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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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3
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Geng C, Wang Z, Tang Y. Machine learning in Alzheimer's disease drug discovery and target identification. Ageing Res Rev 2024; 93:102172. [PMID: 38104638 DOI: 10.1016/j.arr.2023.102172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/28/2023] [Accepted: 12/13/2023] [Indexed: 12/19/2023]
Abstract
Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.
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Affiliation(s)
- Chaofan Geng
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - ZhiBin Wang
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Yi Tang
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China; Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China.
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4
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AlDajani BA, Uzair M, Qaiser H, Mir A, Mohammad Saleh N, Al Baradie R, Tahseen S, Bashir S. Evaluating the Potential of Light Exposure on Reducing the Frequency of Epileptic Seizures. CNS & NEUROLOGICAL DISORDERS DRUG TARGETS 2024; 23:463-467. [PMID: 37026496 DOI: 10.2174/1871527322666230407104706] [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: 09/16/2022] [Revised: 01/16/2023] [Accepted: 01/31/2023] [Indexed: 04/08/2023]
Abstract
Epilepsy is one of the most common and devastating neurological disorders that causes unprovoked, recurrent seizures arising from excessive synchronized neuronal discharging. Although antiepileptic drugs (AEDs) reduce the frequency of epilepsy seizures, drug-refractory epileptic patients exert resistance to AEDs, resulting in treatment difficulty. Moreover, pharmacological treatments do not show satisfactory results in response to photosensitive epilepsy. In the recent era, light therapy emerged as a potential non-pharmacological approach for treating various diseases, including depression, seasonal affective disorders, migraine, pain, and others. Several studies have also shown the potential of light therapy in treating epilepsy. In addition, Red light evokes epilepsy seizures. Blue lenses filter the red light and significantly suppress the frequency of epilepsy seizures. However, the effects of green light on the frequency of epileptic seizures are not studied yet. In addition, light-activated gene therapy or optogenetics also emerged as a possible option for epilepsy treatment. Animal models have shown the therapeutic possibilities of optogenetics and light therapy; however, human studies addressing this possibility are still vague. This review provides the beneficial effects of light in reducing seizure frequency in epilepsy patients. A limited number of studies have been reported so far; therefore, light therapy for treating epilepsy requires more studies on animal models to provide precise results of light effects on seizures.
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Affiliation(s)
| | - Mohammad Uzair
- Department of Biological Sciences, Faculty of Basic & Applied Sciences, International Islamic University Islamabad, Islamabad, Pakistan
| | - Hammad Qaiser
- Department of Biological Sciences, Faculty of Basic & Applied Sciences, International Islamic University Islamabad, Islamabad, Pakistan
| | - Ali Mir
- Neuroscience Center, King Fahad Specialist Hospital Dammam, Dammam, Saudi Arabia
| | - Nojoud Mohammad Saleh
- Department of Neurology, Neuroscience Center, King Fahad Specialist Hospital Dammam, Dammam, Saudi Arabia
| | - Raidah Al Baradie
- Neuroscience Center, King Fahad Specialist Hospital Dammam, Dammam, Saudi Arabia
| | - Saneela Tahseen
- Department of Family Medicine, District Headquarter Hospital, Mandi Bahauddin, Pakistan
| | - Shahid Bashir
- Neuroscience Center, King Fahad Specialist Hospital Dammam, Dammam, Saudi Arabia
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Palanisamy P, Urooj S, Arunachalam R, Lay-Ekuakille A. A Novel Prognostic Model Using Chaotic CNN with Hybridized Spoofing for Enhancing Diagnostic Accuracy in Epileptic Seizure Prediction. Diagnostics (Basel) 2023; 13:3382. [PMID: 37958278 PMCID: PMC10650532 DOI: 10.3390/diagnostics13213382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 08/30/2023] [Accepted: 11/01/2023] [Indexed: 11/15/2023] Open
Abstract
Epileptic seizure detection has undergone progressive advancements since its conception in the 1970s. From proof-of-concept experiments in the latter part of that decade, it has now become a vibrant area of clinical and laboratory research. In an effort to bring this technology closer to practical application in human patients, this study introduces a customized approach to selecting electroencephalogram (EEG) features and electrode positions for seizure prediction. The focus is on identifying precursors that occur within 10 min of the onset of abnormal electrical activity during a seizure. However, there are security concerns related to safeguarding patient EEG recordings against unauthorized access and network-based attacks. Therefore, there is an urgent need for an efficient prediction and classification method for encrypted EEG data. This paper presents an effective system for analyzing and recognizing encrypted EEG information using Arnold transform algorithms, chaotic mapping, and convolutional neural networks (CNNs). In this system, the EEG time series from each channel is converted into a 2D spectrogram image, which is then encrypted using chaotic algorithms. The encrypted data is subsequently processed by CNNs coupled with transfer learning (TL) frameworks. To optimize the fusion parameters of the ensemble learning classifiers, a hybridized spoofing optimization method is developed by combining the characteristics of corvid and gregarious-seeking agents. The evaluation of the model's effectiveness yielded the following results: 98.9 ± 0.3% accuracy, 98.2 ± 0.7% sensitivity, 98.6 ± 0.6% specificity, 98.6 ± 0.6% precision, and an F1 measure of 98.9 ± 0.6%. When compared with other state-of-the-art techniques applied to the same dataset, this novel strategy demonstrated one of the most effective seizure detection systems, as evidenced by these results.
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Affiliation(s)
- Preethi Palanisamy
- Department of Computer Science and Engineering, Kongunadu College of Engineering and Technology, Trichy 621215, India
| | - Shabana Urooj
- Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Rajesh Arunachalam
- Department of Electronics and Communication Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India;
| | - Aime Lay-Ekuakille
- Dipartimento d’Ingegneria dell’Innovazione (DII) (Department of Innovation Engineering), Universita del Salento (University of Salento), Via Monteroni, Ed. “Corpo O”, 73100 Leece, Italy
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6
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Patient-specific method for predicting epileptic seizures based on DRSN-GRU. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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7
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Stredny C, Rotenberg A, Leviton A, Loddenkemper T. Systemic inflammation as a biomarker of seizure propensity and a target for treatment to reduce seizure propensity. Epilepsia Open 2023; 8:221-234. [PMID: 36524286 PMCID: PMC9978091 DOI: 10.1002/epi4.12684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
People with diabetes can wear a device that measures blood glucose and delivers just the amount of insulin needed to return the glucose level to within bounds. Currently, people with epilepsy do not have access to an equivalent wearable device that measures a systemic indicator of an impending seizure and delivers a rapidly acting medication or other intervention (e.g., an electrical stimulus) to terminate or prevent a seizure. Given that seizure susceptibility is reliably increased in systemic inflammatory states, we propose a novel closed-loop device where release of a fast-acting therapy is governed by sensors that quantify the magnitude of systemic inflammation. Here, we review the evidence that patients with epilepsy have raised levels of systemic indicators of inflammation than controls, and that some anti-inflammatory drugs have reduced seizure occurrence in animals and humans. We then consider the options of what might be incorporated into a responsive anti-seizure system.
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Affiliation(s)
- Coral Stredny
- Division of Epilepsy and Clinical Neurophysiology, Department of NeurologyBoston Children's HospitalBostonMassachusettsUSA
- Department of NeurologyHarvard Medical SchoolBostonMassachusettsUSA
| | - Alexander Rotenberg
- Division of Epilepsy and Clinical Neurophysiology, Department of NeurologyBoston Children's HospitalBostonMassachusettsUSA
- Department of NeurologyHarvard Medical SchoolBostonMassachusettsUSA
| | - Alan Leviton
- Division of Epilepsy and Clinical Neurophysiology, Department of NeurologyBoston Children's HospitalBostonMassachusettsUSA
- Department of NeurologyHarvard Medical SchoolBostonMassachusettsUSA
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology, Department of NeurologyBoston Children's HospitalBostonMassachusettsUSA
- Department of NeurologyHarvard Medical SchoolBostonMassachusettsUSA
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Ren Z, Han X, Wang B. The performance evaluation of the state-of-the-art EEG-based seizure prediction models. Front Neurol 2022; 13:1016224. [PMID: 36504642 PMCID: PMC9732735 DOI: 10.3389/fneur.2022.1016224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 11/09/2022] [Indexed: 11/26/2022] Open
Abstract
The recurrent and unpredictable nature of seizures can lead to unintentional injuries and even death. The rapid development of electroencephalogram (EEG) and Artificial Intelligence (AI) technologies has made it possible to predict seizures in real-time through brain-machine interfaces (BCI), allowing advanced intervention. To date, there is still much room for improvement in predictive seizure models constructed by EEG using machine learning (ML) and deep learning (DL). But, the most critical issue is how to improve the performance and generalization of the model, which involves some confusing conceptual and methodological issues. This review focuses on analyzing several factors affecting the performance of seizure prediction models, focusing on the aspects of post-processing, seizure occurrence period (SOP), seizure prediction horizon (SPH), and algorithms. Furthermore, this study presents some new directions and suggestions for building high-performance prediction models in the future. We aimed to clarify the concept for future research in related fields and improve the performance of prediction models to provide a theoretical basis for future applications of wearable seizure detection devices.
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Affiliation(s)
- Zhe Ren
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, China,Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Xiong Han
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, China,Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, China,*Correspondence: Xiong Han
| | - Bin Wang
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, China,Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, China
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9
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Shoeibi A, Moridian P, Khodatars M, Ghassemi N, Jafari M, Alizadehsani R, Kong Y, Gorriz JM, Ramírez J, Khosravi A, Nahavandi S, Acharya UR. An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works. Comput Biol Med 2022; 149:106053. [DOI: 10.1016/j.compbiomed.2022.106053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 08/17/2022] [Accepted: 08/17/2022] [Indexed: 02/01/2023]
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10
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Forster RJ, Henshall DC, El Naggar H, Pellegrin Y, Delanty N. Electrochemiluminescent Detection of Epilepsy Biomarker miR-134 using a Metal Complex Light Switch. Bioelectrochemistry 2022; 146:108150. [DOI: 10.1016/j.bioelechem.2022.108150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 04/27/2022] [Accepted: 04/28/2022] [Indexed: 11/02/2022]
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11
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Wu J, Wu J, Guo R, Chu L, Li J, Zhang S, Ren H. The decreased connectivity in middle temporal gyrus can be used as a potential neuroimaging biomarker for left temporal lobe epilepsy. Front Psychiatry 2022; 13:972939. [PMID: 36032260 PMCID: PMC9399621 DOI: 10.3389/fpsyt.2022.972939] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 07/08/2022] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE We aimed to explore voxel-mirrored homotopic connectivity (VMHC) abnormalities between the two brain hemispheres in left temporal lobe epilepsy (lTLE) patients and to determine whether these alterations could be leveraged to guide lTLE diagnosis. MATERIALS AND METHODS Fifty-eight lTLE patients and sixty healthy controls (HCs) matched in age, sex, and education level were recruited to receive resting state functional magnetic resonance imaging (rs-fMRI) scan. Then VHMC analyses of bilateral brain regions were conducted based on the results of these rs-fMRI scans. The resultant imaging data were further analyzed using support vector machine (SVM) methods. RESULTS Compared to HCs, patients with lTLE exhibited decreased VMHC values in the bilateral middle temporal gyrus (MTG) and middle cingulum gyrus (MCG), while no brain regions in these patients exhibited increased VMHC values. SVM analyses revealed the diagnostic accuracy of reduced bilateral MTG VMHC values to be 75.42% (89/118) when differentiating between lTLE patients and HCs, with respective sensitivity and specificity values of 74.14% (43/58) and 76.67% (46/60). CONCLUSION Patients with lTLE exhibit abnormal VMHC values corresponding to the impairment of functional coordination between homotopic regions of the brain. These altered MTG VMHC values may also offer value as a robust neuroimaging biomarker that can guide lTLE patient diagnosis.
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Affiliation(s)
- Jinlong Wu
- Department of Imaging Center, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China.,Key Laboratory of Occupational Hazards and Identification, Wuhan University of Science and Technology, Wuhan, China
| | - Jun Wu
- Department of Neurosurgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ruimin Guo
- Department of Imaging Center, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Linkang Chu
- Department of Imaging Center, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Jun Li
- Department of Neurosurgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Sheng Zhang
- Liyuan Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongwei Ren
- Department of Imaging Center, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
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12
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Obukhov YV, Kershner IA, Tolmacheva RA, Sinkin MV, Zhavoronkova LA. Wavelet Ridges in EEG Diagnostic Features Extraction: Epilepsy Long-Time Monitoring and Rehabilitation after Traumatic Brain Injury. SENSORS (BASEL, SWITZERLAND) 2021; 21:5989. [PMID: 34577198 PMCID: PMC8468146 DOI: 10.3390/s21185989] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 08/29/2021] [Accepted: 09/03/2021] [Indexed: 01/06/2023]
Abstract
Interchannel EEG synchronization, as well as its violation, is an important diagnostic sign of a number of diseases. In particular, during an epileptic seizure, such synchronization occurs starting from some pairs of channels up to many pairs in a generalized seizure. Additionally, for example, after traumatic brain injury, the destruction of interneuronal connections occurs, which leads to a violation of interchannel synchronization when performing motor or cognitive tests. Within the framework of a unified approach to the analysis of interchannel EEG synchronization using the ridges of wavelet spectra, two problems were solved. First, the segmentation of the initial data of long-term monitoring of scalp EEG with various artifacts into fragments suspicious of epileptic seizures in order to reduce the total duration of the fragments analyzed by the doctor. Second, assessments of recovery after rehabilitation of cognitive functions in patients with moderate traumatic brain injury. In the first task, the initial EEG was segmented into fragments in which at least two channels were synchronized, and by the adaptive threshold method into fragments with a high value of the EEG power spectral density. Overlapping in time synchronized fragments with fragments of high spectral power density was determined. As a result, the total duration of the fragments for analysis by the doctor was reduced by more than 60 times. In the second task, the network of phase-related EEG channels was determined during the cognitive test before and after rehabilitation. Calculation-logical and spatial-pattern cognitive tests were used. The positive dynamics of rehabilitation was determined during the initialization of interhemispheric connections and connections in the frontal cortex of the brain.
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Affiliation(s)
- Yury Vladimirovich Obukhov
- Kotelnikov Institute of Radio Engineering and Electronics of RAS, Mokhovaya St. 11-7, 125009 Moscow, Russia; (Y.V.O.); (R.A.T.)
| | - Ivan Andreevich Kershner
- Kotelnikov Institute of Radio Engineering and Electronics of RAS, Mokhovaya St. 11-7, 125009 Moscow, Russia; (Y.V.O.); (R.A.T.)
| | - Renata Alekseevna Tolmacheva
- Kotelnikov Institute of Radio Engineering and Electronics of RAS, Mokhovaya St. 11-7, 125009 Moscow, Russia; (Y.V.O.); (R.A.T.)
| | - Mikhail Vladimirovich Sinkin
- Department of Neurosurgery of the Sklifosovsky Research Institute for Emergency Medicine of Moscow Healthcare Department, Bolshaya Sukharevskaya Square 3, 129090 Moscow, Russia; or
- Laboratory of Invasive Neurointerfaces of the Research Institute TechnoBioMed, A.I. Yevdokimov Moscow State University of Medicine and Dentistry, Delegatskaya St. 20 p.1, 127473 Moscow, Russia
| | - Ludmila Alekseevna Zhavoronkova
- Laboratory of General and Clinical Neurophysiology of the Institute of Higher Nervous Activity and Neurophysiology of RAS, Butlerova St. 5a, 117485 Moscow, Russia; or
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