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Wu M, Peng H, Liu Z, Wang J. Seizure Detection of EEG Signals Based on Multi-Channel Long- and Short-Term Memory-Like Spiking Neural Model. Int J Neural Syst 2024:2450051. [PMID: 39004932 DOI: 10.1142/s0129065724500515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Seizure is a common neurological disorder that usually manifests itself in recurring seizure, and these seizures can have a serious impact on a person's life and health. Therefore, early detection and diagnosis of seizure is crucial. In order to improve the efficiency of early detection and diagnosis of seizure, this paper proposes a new seizure detection method, which is based on discrete wavelet transform (DWT) and multi-channel long- and short-term memory-like spiking neural P (LSTM-SNP) model. First, the signal is decomposed into 5 levels by using DWT transform to obtain the features of the components at different frequencies, and a series of time-frequency features in wavelet coefficients are extracted. Then, these different features are used to train a multi-channel LSTM-SNP model and perform seizure detection. The proposed method achieves a high seizure detection accuracy on the CHB-MIT dataset: 98.25% accuracy, 98.22% specificity and 97.59% sensitivity. This indicates that the proposed epilepsy detection method can show competitive detection performance.
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Affiliation(s)
- Min Wu
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Hong Peng
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Zhicai Liu
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Jun Wang
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, P. R. China
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2
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Anwar MS, Frolov N, Hramov AE, Ghosh D. Self-organized bistability on globally coupled higher-order networks. Phys Rev E 2024; 109:014225. [PMID: 38366474 DOI: 10.1103/physreve.109.014225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 01/04/2024] [Indexed: 02/18/2024]
Abstract
Self-organized bistability (SOB) stands as a critical behavior for the systems delicately adjusting themselves to the brink of bistability, characterized by a first-order transition. Its essence lies in the inherent ability of the system to undergo enduring shifts between the coexisting states, achieved through the self-regulation of a controlling parameter. Recently, SOB has been established in a scale-free network as a recurrent transition to a short-living state of global synchronization. Here, we embark on a theoretical exploration that extends the boundaries of the SOB concept on a higher-order network (implicitly embedded microscopically within a simplicial complex) while considering the limitations imposed by coupling constraints. By applying Ott-Antonsen dimensionality reduction in the thermodynamic limit to the higher-order network, we derive SOB requirements under coupling limits that are in good agreement with numerical simulations on systems of finite size. We use continuous synchronization diagrams and statistical data from spontaneous synchronized events to demonstrate the crucial role SOB plays in initiating and terminating temporary synchronized events. We show that under weak-coupling consumption, these spontaneous occurrences closely resemble the statistical traits of the epileptic brain functioning.
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Affiliation(s)
- Md Sayeed Anwar
- Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India
| | - Nikita Frolov
- Laboratory of Dynamics in Biological Systems, Department of Cellular and Molecular Medicine, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Alexander E Hramov
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 14, A. Nevskogo str., Kaliningrad 236016, Russia
| | - Dibakar Ghosh
- Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India
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3
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Gerasimova S, Lebedeva A, Gromov N, Malkov A, Fedulina А, Levanova T, Pisarchik A. Memristive Neural Networks for Predicting Seizure Activity. Sovrem Tekhnologii Med 2023; 15:30-38. [PMID: 38434190 PMCID: PMC10902902 DOI: 10.17691/stm2023.15.4.03] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Indexed: 03/05/2024] Open
Abstract
The aim of the study is to assess the possibilities of predicting epileptiform activity using the neuronal activity data recorded from the hippocampus and medial entorhinal cortex of mice with chronic epileptiform activity. To reach this goal, a deep artificial neural network (ANN) has been developed and its implementation based on memristive devices has been demonstrated. Materials and Methods The biological part of the investigation. Young healthy outbred CD1 mice were used in our study. They were divided into two groups: control (n=6) and the group with induced chronic epileptiform activity (n=6). Local field potentials (LFP) were recorded from the hippocampus and medial entorhinal cortex of the mice of both groups to register neuronal activity. The LFP recordings were used for deep ANN training. Epileptiform activity in mice was modeled by intraperitoneal injection of pilocarpine (280 mg/kg). LFP were recorded in the awake mice a month after the induction of epileptiform activity.Mathematical part of the investigation. A deep long short-term memory (LSTM) ANN capable of predicting biological signals of neuronal activity in mice has been developed. The ANN implementation is based on memristive devices, which are described by the equations of the redox processes running in the memristive thin metal-oxide-metal films, e.g., Au/ZrO2(Y)/TiN/Ti and Au/SiO2(Y)/TiN/Ti. In order to train the developed ANN to predict epileptiform activity, a supervised learning algorithm was used, which allowed us to adjust the network parameters and train LSTM on the described recordings of neuronal activity. Results After training on the LFP recordings from the hippocampus and medial entorhinal cortex of the mice with chronic epileptiform activity, the proposed deep ANN has demonstrated high values of evaluation metric (root-mean-square error, RMSE) and successfully predicted epileptiform activity shortly before its occurrence (40 ms). The results of the numerical experiments have shown that the RMSE value of 0.019 was reached, which indicates the efficacy of proposed approach. The accuracy of epileptiform activity prediction 40 ms before its occurrence is a significant result and shows the potential of the developed neural network architecture. Conclusion The proposed deep ANN can be used to predict pathological neuronal activity including epileptic seizure (focal) activity in mice before its actual occurrence. Besides, it can be applied for building a long-term prognosis of the disease course based on the LFP data. Thus, the proposed ANN based on memristive devices represents a novel approach to the prediction and analysis of pathological neuronal activity possessing a potential for improving the diagnosis and prognostication of epileptic seizures and other diseases associated with neuronal activity.
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Affiliation(s)
- S.A. Gerasimova
- Researcher, Research Laboratory of Perspective Methods of Multidimensional Data Analysis, Institute of Information Technologies, Mathematics, and Mechanics; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia
| | - A.V. Lebedeva
- Associate Professor, Department of Neurotechnologies, Institute of Biology and Biomedicine; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia
| | - N.V. Gromov
- Laboratory Research Assistant, Research Laboratory of Perspective Methods of Multidimensional Data Analysis, Institute of Information Technologies, Mathematics, and Mechanics; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia
| | - A.E. Malkov
- Senior Researcher, Laboratory of Systemic Organization of Neurons; Institute of Theoretical and Experimental Biophysics of Russian Academy of Sciences, 3 Institutskaya St., Puschino, Moscow Region, 142290, Russia
| | - А.А. Fedulina
- Junior Researcher, Laboratory of Brain Development Genetics, Research Institute of Neurosciences; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia
| | - T.A. Levanova
- Associate Professor, Department of System Dynamics and Control Theory, Institute of Information Technologies, Mathematics, and Mechanics; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia
| | - A.N. Pisarchik
- Head of the Laboratory of Computational Biology, Center for Biomedical Technology; Universidad Politécnica de Madrid, Madrid, 28223, Spain
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4
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Characterization of Anesthesia in Rats from EEG in Terms of Long-Range Correlations. Diagnostics (Basel) 2023; 13:diagnostics13030426. [PMID: 36766531 PMCID: PMC9914327 DOI: 10.3390/diagnostics13030426] [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: 12/25/2022] [Revised: 01/15/2023] [Accepted: 01/20/2023] [Indexed: 01/26/2023] Open
Abstract
Long-range correlations are often used as diagnostic markers in physiological research. Due to the limitations of conventional techniques, their characterizations are typically carried out with alternative approaches, such as the detrended fluctuation analysis (DFA). In our previous works, we found EEG-related markers of the blood-brain barrier (BBB), which limits the penetration of major drugs into the brain. However, anesthetics can penetrate the BBB, affecting its function in a dose-related manner. Here, we study two types of anesthesia widely used in experiments on animals, including zoletil/xylazine and isoflurane in optimal doses not associated with changes in the BBB. Based on DFA, we reveal informative characteristics of the electrical activity of the brain during such doses that are important for controlling the depth of anesthesia in long-term experiments using magnetic resonance imaging, multiphoton microscopy, etc., which are crucial for the interpretation of experimental results. These findings provide an important informative platform for the enhancement and refinement of surgery, since the EEG-based DFA analysis of BBB can easily be used during surgery as a tool for characterizing normal BBB functions under anesthesia.
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5
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Koronovskii AA, Blokhina IA, Dmitrenko AV, Tuzhilkin MA, Moiseikina TV, Elizarova IV, Semyachkina-Glushkovskaya OV, Pavlov AN. Extended Detrended Fluctuation Analysis of Coarse-Grained Time Series. Diagnostics (Basel) 2022; 13:diagnostics13010093. [PMID: 36611385 PMCID: PMC9818195 DOI: 10.3390/diagnostics13010093] [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: 12/08/2022] [Revised: 12/21/2022] [Accepted: 12/24/2022] [Indexed: 12/30/2022] Open
Abstract
A coarse-graining procedure, which involves averaging time series in non-overlapping windows followed by processing of the obtained multiple data sets, is the initial step in the multiscale entropy computation method. In this paper, we discuss how this procedure can be applied with other methods of time series analysis. Based on extended detrended fluctuation analysis (EDFA), we compare signal processing results for data sets with and without coarse-graining. Using the simulated data provided by the interacting nephrons model, we show how this procedure increases, up to 48%, the distinctions between local scaling exponents quantifying synchronous and asynchronous chaotic oscillations. Based on the experimental data of electrocorticograms (ECoG) of mice, an improvement in differences in local scaling exponents up to 41% and Student's t-values up to 34% was revealed.
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Affiliation(s)
- Alexander A. Koronovskii
- Physics of Open Systems Department, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, Russia
| | - Inna A. Blokhina
- Department of Human and Animal Physiology, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, Russia
| | - Alexander V. Dmitrenko
- Department of Human and Animal Physiology, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, Russia
| | - Matvey A. Tuzhilkin
- Department of Human and Animal Physiology, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, Russia
| | - Tatyana V. Moiseikina
- Department of Human and Animal Physiology, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, Russia
| | - Inna V. Elizarova
- Department of Human and Animal Physiology, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, Russia
| | | | - Alexey N. Pavlov
- Physics of Open Systems Department, Saratov State University, Astrakhanskaya Str. 83, Saratov 410012, Russia
- Correspondence:
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6
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Andreev AV, Badarin AA, Maximenko VA, Hramov AE. Forecasting macroscopic dynamics in adaptive Kuramoto network using reservoir computing. CHAOS (WOODBURY, N.Y.) 2022; 32:103126. [PMID: 36319291 DOI: 10.1063/5.0114127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 09/30/2022] [Indexed: 06/16/2023]
Abstract
Forecasting a system's behavior is an essential task encountering the complex systems theory. Machine learning offers supervised algorithms, e.g., recurrent neural networks and reservoir computers that predict the behavior of model systems whose states consist of multidimensional time series. In real life, we often have limited information about the behavior of complex systems. The brightest example is the brain neural network described by the electroencephalogram. Forecasting the behavior of these systems is a more challenging task but provides a potential for real-life application. Here, we trained reservoir computer to predict the macroscopic signal produced by the network of phase oscillators. The Lyapunov analysis revealed the chaotic nature of the signal and reservoir computer failed to forecast it. Augmenting the feature space using Takkens' theorem improved the quality of forecasting. RC achieved the best prediction score when the number of signals coincided with the embedding dimension estimated via the nearest false neighbors method. We found that short-time prediction required a large number of features, while long-time prediction utilizes a limited number of features. These results refer to the bias-variance trade-off, an important concept in machine learning.
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Affiliation(s)
- Andrey V Andreev
- Engineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University named after the first President of Russia B.N.Yeltsin, 19 Mira str., 620002 Ekaterinburg, Russia
| | - Artem A Badarin
- Engineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University named after the first President of Russia B.N.Yeltsin, 19 Mira str., 620002 Ekaterinburg, Russia
| | - Vladimir A Maximenko
- Engineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University named after the first President of Russia B.N.Yeltsin, 19 Mira str., 620002 Ekaterinburg, Russia
| | - Alexander E Hramov
- Engineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University named after the first President of Russia B.N.Yeltsin, 19 Mira str., 620002 Ekaterinburg, Russia
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7
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Frolov N, Hramov A. Self-organized bistability on scale-free networks. Phys Rev E 2022; 106:044301. [PMID: 36397487 DOI: 10.1103/physreve.106.044301] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 08/15/2022] [Indexed: 06/16/2023]
Abstract
A dynamical system approaching the first-order transition can exhibit a specific type of critical behavior known as self-organized bistability (SOB). It lies in the fact that the system can permanently switch between the coexisting states under the self-tuning of a control parameter. Many of these systems have a network organization that should be taken into account to understand the underlying processes in detail. In the present paper, we theoretically explore an extension of the SOB concept on the scale-free network under coupling constraints. As provided by the numerical simulations and mean-field approximation in the thermodynamic limit, SOB on scale-free networks originates from facilitated criticality reflected on both macro- and mesoscopic network scales. We establish that the appearance of switches is rooted in spatial self-organization and temporal self-similarity of the network's critical dynamics and replicates extreme properties of epileptic seizure recurrences. Our results, thus, indicate that the proposed conceptual model is suitable to deepen the understanding of emergent collective behavior behind neurological diseases.
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Affiliation(s)
- Nikita Frolov
- Center for Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad 236041, Russia and Institute of Information Technology, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603022, Russia
| | - Alexander Hramov
- Center for Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad 236041, Russia and Institute of Information Technology, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603022, Russia
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8
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Skiba I, Kopanitsa G, Metsker O, Yanishevskiy S, Polushin A. Application of Machine Learning Methods for Epilepsy Risk Ranking in Patients with Hematopoietic Malignancies Using. J Pers Med 2022; 12:jpm12081306. [PMID: 36013255 PMCID: PMC9410112 DOI: 10.3390/jpm12081306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/31/2022] [Accepted: 08/08/2022] [Indexed: 12/02/2022] Open
Abstract
Machine learning methods to predict the risk of epilepsy, including vascular epilepsy, in oncohematological patients are currently considered promising. These methods are used in research to predict pharmacoresistant epilepsy and surgical treatment outcomes in order to determine the epileptogenic zone and functional neural systems in patients with epilepsy, as well as to develop new approaches to classification and perform other tasks. This paper presents the results of applying machine learning to analyzing data and developing diagnostic models of epilepsy in oncohematological and cardiovascular patients. This study contributes to solving the problem of often unjustified diagnosis of primary epilepsy in patients with oncohematological or cardiovascular pathology, prescribing antiseizure drugs to patients with single seizure syndromes without finding a disease associated with these cases. We analyzed the hospital database of the V.A. Almazov Scientific Research Center of the Ministry of Health of Russia. The study included 66,723 treatment episodes of patients with vascular diseases (I10–I15, I61–I69, I20–I25) and 16,383 episodes with malignant neoplasms of lymphoid, hematopoietic, and related tissues (C81–C96 according to ICD-10) for the period from 2010 to 2020. Data analysis and model calculations indicate that the best result was shown by gradient boosting with mean accuracy cross-validation score = 0.96. f1-score = 98, weighted avg precision = 93, recall = 96, f1-score = 94. The highest correlation coefficient for G40 and different clinical conditions was achieved with fibrillation, hypertension, stenosis or occlusion of the precerebral arteries (0.16), cerebral sinus thrombosis (0.089), arterial hypertension (0.17), age (0.03), non-traumatic intracranial hemorrhage (0.07), atrial fibrillation (0.05), delta absolute neutrophil count (0.05), platelet count at discharge (0.04), transfusion volume for stem cell transplantation (0.023). From the clinical point of view, the identified differences in the importance of predictors in a broader patient model are consistent with a practical algorithm for organic brain damage. Atrial fibrillation is one of the leading factors in the development of both ischemic and hemorrhagic strokes. At the same time, brain infarction can be accompanied both by the development of epileptic seizures in the acute period and by unprovoked epileptic seizures and development of epilepsy in the early recovery and in a longer period. In addition, a microembolism of the left heart chambers can lead to multiple microfocal lesions of the brain, which is one of the pathogenetic aspects of epilepsy in elderly patients. The presence of precordial fibrillation requires anticoagulant therapy, the use of which increases the risk of both spontaneous and traumatic intracranial hemorrhage.
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Affiliation(s)
- Iaroslav Skiba
- Department of Chemotherapy and Stem Cell Transplantation for Cancer and Autoimmune Diseases, First Pavlov State Medical University of St. Peterburg, 197022 Saint Petersburg, Russia
| | - Georgy Kopanitsa
- Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia
- National Center for Cognitive Research, ITMO University, 49 Kronverskiy Prospect, 197101 Saint Petersburg, Russia
- Correspondence:
| | - Oleg Metsker
- Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia
| | | | - Alexey Polushin
- Department of Chemotherapy and Stem Cell Transplantation for Cancer and Autoimmune Diseases, First Pavlov State Medical University of St. Peterburg, 197022 Saint Petersburg, Russia
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9
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Karpov OE, Grubov VV, Maksimenko VA, Kurkin SA, Smirnov NM, Utyashev NP, Andrikov DA, Shusharina NN, Hramov AE. Extreme value theory inspires explainable machine learning approach for seizure detection. Sci Rep 2022; 12:11474. [PMID: 35794223 PMCID: PMC9259747 DOI: 10.1038/s41598-022-15675-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 06/28/2022] [Indexed: 11/09/2022] Open
Abstract
Epilepsy is one of the brightest manifestations of extreme behavior in living systems. Extreme epileptic events are seizures, that arise suddenly and unpredictably. Usually, treatment strategies start by analyzing brain activity during the seizures revealing their type and onset mechanisms. This approach requires collecting data for a representative number of events which is only possible during the continuous EEG monitoring over several days. A big part of the further analysis is searching for seizures on these recordings. An experienced medical specialist spends hours checking the data of a single patient and needs assistance from the automative systems for seizure detection. Machine learning methods typically address this issue in a supervised fashion and exhibit a lack of generalization. The extreme value theory allows addressing this issue with the unsupervised machine learning methods of outlier detection. Here, we make the first step toward using this approach for the seizure detection. Based on our recent work, we specified the EEG features showing extreme behavior during seizures and loaded them to the one-class SVM, a popular outlier detection algorithm. Testing the proposed approach on 83 patients, we reported 77% sensitivity and 12% precision. In 60 patients, sensitivity was 100%. In the rest 23 subjects, we observed deviations from the extreme behavior. The one-class SVM used a single subject's data for training; therefore, it was stable against between-subject variability. Our results demonstrate an effective convergence between the extreme value theory, a physical concept, and the outlier detection algorithms, a machine learning concept, toward solving the meaningful task of medicine.
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Affiliation(s)
- Oleg E Karpov
- National Medical and Surgical Center named after N. I. Pirogov, Ministry of Healthcare of the Russian Federation, Moscow, Russia
| | - Vadim V Grubov
- Neuroscience and Cognitive Technology Laboratory, Innopolis University, Kazan, Russia.,Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Vladimir A Maksimenko
- Neuroscience and Cognitive Technology Laboratory, Innopolis University, Kazan, Russia.,Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Semen A Kurkin
- Neuroscience and Cognitive Technology Laboratory, Innopolis University, Kazan, Russia.,Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Nikita M Smirnov
- Neuroscience and Cognitive Technology Laboratory, Innopolis University, Kazan, Russia
| | - Nikita P Utyashev
- National Medical and Surgical Center named after N. I. Pirogov, Ministry of Healthcare of the Russian Federation, Moscow, Russia
| | | | - Natalia N Shusharina
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Alexander E Hramov
- Neuroscience and Cognitive Technology Laboratory, Innopolis University, Kazan, Russia. .,Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad, Russia. .,Department of Theoretical Cybernetics, Saint Petersburg State University, St. Petersburg, Russia.
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10
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Frolov N, Hramov A. Extreme synchronization events in a Kuramoto model: The interplay between resource constraints and explosive transitions. CHAOS (WOODBURY, N.Y.) 2021; 31:063103. [PMID: 34241300 DOI: 10.1063/5.0055156] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 05/17/2021] [Indexed: 06/13/2023]
Abstract
Many living and artificial systems possess structural and dynamical properties of complex networks. One of the most exciting living networked systems is the brain, in which synchronization is an essential mechanism of its normal functioning. On the other hand, excessive synchronization in neural networks reflects undesired pathological activity, including various forms of epilepsy. In this context, network-theoretical approach and dynamical modeling may uncover deep insight into the origins of synchronization-related brain disorders. However, many models do not account for the resource consumption needed for the neural networks to synchronize. To fill this gap, we introduce a phenomenological Kuramoto model evolving under the excitability resource constraints. We demonstrate that the interplay between increased excitability and explosive synchronization induced by the hierarchical organization of the network forces the system to generate short-living extreme synchronization events, which are well-known signs of epileptic brain activity. Finally, we establish that the network units occupying the medium levels of hierarchy most strongly contribute to the birth of extreme events emphasizing the focal nature of their origin.
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Affiliation(s)
- Nikita Frolov
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Innopolis, The Republic of Tatarstan, Russia
| | - Alexander Hramov
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Innopolis, The Republic of Tatarstan, Russia
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11
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Bosl WJ, Leviton A, Loddenkemper T. Prediction of Seizure Recurrence. A Note of Caution. Front Neurol 2021; 12:675728. [PMID: 34054713 PMCID: PMC8155381 DOI: 10.3389/fneur.2021.675728] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 04/20/2021] [Indexed: 12/31/2022] Open
Abstract
Great strides have been made recently in documenting that machine-learning programs can predict seizure occurrence in people who have epilepsy. Along with this progress have come claims that appear to us to be a bit premature. We anticipate that many people will benefit from seizure prediction. We also doubt that all will benefit. Although machine learning is a useful tool for aiding discovery, we believe that the greatest progress will come from deeper understanding of seizures, epilepsy, and the EEG features that enable seizure prediction. In this essay, we lay out reasons for optimism and skepticism.
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Affiliation(s)
- William J Bosl
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States.,Health Informatics Program, University of San Francisco, San Francisco, CA, United States
| | - Alan Leviton
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Tobias Loddenkemper
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
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12
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Pavlov AN, Pavlova ON, Semyachkina-Glushkovskaya OV, Kurths J. Enhanced multiresolution wavelet analysis of complex dynamics in nonlinear systems. CHAOS (WOODBURY, N.Y.) 2021; 31:043110. [PMID: 34251250 DOI: 10.1063/5.0045859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/23/2021] [Indexed: 06/13/2023]
Abstract
Multiresolution wavelet analysis (MWA) is a powerful data processing tool that provides a characterization of complex signals over multiple time scales. Typically, the standard deviations of wavelet coefficients are computed depending on the resolution level and such quantities are used as measures for diagnosing different types of system behavior. To enhance the capabilities of this tool, we propose a combination of MWA with detrended fluctuation analysis (DFA) of detail wavelet coefficients. We find that such an MWA&DFA approach is capable of revealing the correlation features of wavelet coefficients in independent ranges of scales, which provide more information about the complex organization of datasets compared to variances or similar statistical measures of the standard MWA. Using this approach, we consider changes in the dynamics of coupled chaotic systems caused by transitions between different types of complex oscillations. We also demonstrate the potential of the MWA&DFA method for characterizing different physiological conditions by analyzing the electrical brain activity in mice.
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Affiliation(s)
- A N Pavlov
- Institute of Physics, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia
| | - O N Pavlova
- Institute of Physics, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia
| | | | - J Kurths
- Biology Department, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia
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Karpov OE, Grubov VV, Maksimenko VA, Utaschev N, Semerikov VE, Andrikov DA, Hramov AE. Noise amplification precedes extreme epileptic events on human EEG. Phys Rev E 2021; 103:022310. [PMID: 33735967 DOI: 10.1103/physreve.103.022310] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Accepted: 01/19/2021] [Indexed: 06/12/2023]
Abstract
Extreme events are rare and sudden abnormal deviations of the system's behavior from a typical state. Statistical analysis reveals that if the time series contains extreme events, its distribution has a heavy tail. In dynamical systems, extreme events often occur due to developing instability preceded by noise amplification. Here, we apply this theory to analyze generalized epileptic seizures in the human brain. First, we demonstrate that the time series of electroencephalogram (EEG) spectral power in a frequency band of 1-5 Hz obeys a heavy-tailed distribution, confirming the presence of extreme events. Second, we report that noise on EEG signals gradually increases before the seizure onset. Thus, we hypothesize that generalized epileptic seizures in humans are the extreme events emerging from instability accompanied by preictal noise amplification similar to other dynamical systems.
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Affiliation(s)
- Oleg E Karpov
- National Medical and Surgical Center named after N. I. Pirogov, Ministry of Healthcare of the Russian Federation, 105203 Moscow, Russia
| | - Vadim V Grubov
- Research and Production Company "Immersmed", Moscow 105203, Russia
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Kazan, Russia
| | - Vladimir A Maksimenko
- Research and Production Company "Immersmed", Moscow 105203, Russia
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Kazan, Russia
| | - Nikita Utaschev
- National Medical and Surgical Center named after N. I. Pirogov, Ministry of Healthcare of the Russian Federation, 105203 Moscow, Russia
| | | | - Denis A Andrikov
- Research and Production Company "Immersmed", Moscow 105203, Russia
| | - Alexander E Hramov
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Kazan, Russia
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14
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Pavlov AN, Pitsik EN, Frolov NS, Badarin A, Pavlova ON, Hramov AE. Age-Related Distinctions in EEG Signals during Execution of Motor Tasks Characterized in Terms of Long-Range Correlations. SENSORS 2020; 20:s20205843. [PMID: 33076556 PMCID: PMC7602706 DOI: 10.3390/s20205843] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 09/23/2020] [Accepted: 10/12/2020] [Indexed: 12/20/2022]
Abstract
The problem of revealing age-related distinctions in multichannel electroencephalograms (EEGs) during the execution of motor tasks in young and elderly adults is addressed herein. Based on the detrended fluctuation analysis (DFA), differences in long-range correlations are considered, emphasizing changes in the scaling exponent α. Stronger responses in elderly subjects are confirmed, including the range and rate of increase in α. Unlike elderly subjects, young adults demonstrated about 2.5 times more pronounced differences between motor task responses with the dominant and non-dominant hand. Knowledge of age-related changes in brain electrical activity is important for understanding consequences of healthy aging and distinguishing them from pathological changes associated with brain diseases. Besides diagnosing age-related effects, the potential of DFA can also be used in the field of brain–computer interfaces.
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Affiliation(s)
- Alexey N. Pavlov
- Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (A.N.P.); (O.N.P.)
| | - Elena N. Pitsik
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Universitetskaya Str. 1, 420500 Innopolis, Russia; (E.N.P.); (N.S.F.); (A.B.)
| | - Nikita S. Frolov
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Universitetskaya Str. 1, 420500 Innopolis, Russia; (E.N.P.); (N.S.F.); (A.B.)
| | - Artem Badarin
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Universitetskaya Str. 1, 420500 Innopolis, Russia; (E.N.P.); (N.S.F.); (A.B.)
| | - Olga N. Pavlova
- Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (A.N.P.); (O.N.P.)
| | - Alexander E. Hramov
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Universitetskaya Str. 1, 420500 Innopolis, Russia; (E.N.P.); (N.S.F.); (A.B.)
- Lobachevsky University, 23 Gagarina Avenue, 603950 Nizhny Novgorod, Russia
- Saratov State Medical University, Bolshaya Kazachya Str. 112, 410012 Saratov, Russia
- Correspondence:
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15
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Orlando G, Zimatore G. Business cycle modeling between financial crises and black swans: Ornstein-Uhlenbeck stochastic process vs Kaldor deterministic chaotic model. CHAOS (WOODBURY, N.Y.) 2020; 30:083129. [PMID: 32872798 DOI: 10.1063/5.0015916] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 07/30/2020] [Indexed: 06/11/2023]
Abstract
Business cycles are oscillations in the economy because of recessions and expansions. In this paper we investigate the oscillation of the gross domestic product as a result of its relations with the other main macroeconomic variables such as capital, consumption, and investment. There is a long-standing debate about chaos and non-linear dynamics in economy and even the usefulness of those concepts has been questioned. Stochastic modeling has proven to be able to simulate reality fairly well. However, a stochastic behavior implies that reality is about exogenous randomness, while a chaotic behavior means that reality is deterministic and non-linearities are endogenous. Here we compare an Ornstein-Uhlenbeck stochastic process with a Kaldor-Kalecki deterministic chaotic model to understand which one fits better real data. We show that our chaotic model is able to represent reality as well as the stochastic model taken into consideration. Furthermore, our model may reproduce an extreme event (black swans).
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Affiliation(s)
- Giuseppe Orlando
- Department of Economics and Finance, Università degli Studi di Bari "Aldo Moro," Via C. Rosalba 53, Bari I-70124, Italy
| | - Giovanna Zimatore
- Department of Theoretical and Applied Sciences, eCampus University, Via Matera 18, Rome I-00182, Italy
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16
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Pavlov AN, Dubrovsky AI, Koronovskii AA, Pavlova ON, Semyachkina-Glushkovskaya OV, Kurths J. Extended detrended fluctuation analysis of electroencephalograms signals during sleep and the opening of the blood-brain barrier. CHAOS (WOODBURY, N.Y.) 2020; 30:073138. [PMID: 32752608 DOI: 10.1063/5.0011823] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 07/07/2020] [Indexed: 06/11/2023]
Abstract
Detrended fluctuation analysis (DFA) is widely used to characterize long-range power-law correlations in complex signals. However, it has restrictions when nonstationarity is not limited only to slow variations in the mean value. To improve the characterization of inhomogeneous datasets, we have proposed the extended DFA (EDFA), which is a modification of the conventional method that evaluates an additional scaling exponent to take into account the features of time-varying nonstationary behavior. Based on EDFA, here, we analyze rat electroencephalograms to identify specific changes in the slow-wave dynamics of brain electrical activity associated with two different conditions, such as the opening of the blood-brain barrier and sleep, which are both characterized by the activation of the brain drainage function. We show that these conditions cause a similar reduction in the scaling exponents of EDFA. Such a similarity may represent an informative marker of fluid homeostasis of the central nervous system.
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Affiliation(s)
- A N Pavlov
- Department of Nonlinear Processes, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia
| | - A I Dubrovsky
- Department of Physics, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia
| | - A A Koronovskii
- Department of Nonlinear Processes, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia
| | - O N Pavlova
- Department of Physics, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia
| | | | - J Kurths
- Deparatment of Biology, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia
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Yang S, Li B, Zhang Y, Duan M, Liu S, Zhang Y, Feng X, Tan R, Huang L, Zhou F. Selection of features for patient-independent detection of seizure events using scalp EEG signals. Comput Biol Med 2020; 119:103671. [DOI: 10.1016/j.compbiomed.2020.103671] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 02/20/2020] [Accepted: 02/20/2020] [Indexed: 11/16/2022]
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Cozzolino O, Sicca F, Paoli E, Trovato F, Santorelli FM, Ratto GM, Marchese M. Evolution of Epileptiform Activity in Zebrafish by Statistical-Based Integration of Electrophysiology and 2-Photon Ca 2+ Imaging. Cells 2020; 9:cells9030769. [PMID: 32245158 PMCID: PMC7140665 DOI: 10.3390/cells9030769] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 03/14/2020] [Accepted: 03/18/2020] [Indexed: 12/20/2022] Open
Abstract
The study of sources and spatiotemporal evolution of ictal bursts is critical for the mechanistic understanding of epilepsy and for the validation of anti-epileptic drugs. Zebrafish is a powerful vertebrate model representing an excellent compromise between system complexity and experimental accessibility. We performed the quantitative evaluation of the spatial recruitment of neuronal populations during physiological and pathological activity by combining local field potential (LFP) recordings with simultaneous 2-photon Ca2+ imaging. We developed a method to extract and quantify electrophysiological transients coupled with Ca2+ events and we applied this tool to analyze two different epilepsy models and to assess the efficacy of the anti-epileptic drug valproate. Finally, by cross correlating the imaging data with the LFP, we demonstrated that the cerebellum is the main source of epileptiform transients. We have also shown that each transient was preceded by the activation of a sparse subset of neurons mostly located in the optic tectum.
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Affiliation(s)
- Olga Cozzolino
- National Enterprise for Nanoscience and Nanotechnology (NEST), Istituto Nanoscienze Consiglio Nazionale delle Ricerche (CNR) and Scuola Normale Superiore Pisa, 56127 Pisa, Italy; (O.C.); (E.P.); (F.T.)
| | - Federico Sicca
- Molecular Medicine, IRCCS Fondazione Stella Maris, Via dei Giacinti 2, 56028 Pisa, Italy; (F.S.); (F.M.S.)
| | - Emanuele Paoli
- National Enterprise for Nanoscience and Nanotechnology (NEST), Istituto Nanoscienze Consiglio Nazionale delle Ricerche (CNR) and Scuola Normale Superiore Pisa, 56127 Pisa, Italy; (O.C.); (E.P.); (F.T.)
| | - Francesco Trovato
- National Enterprise for Nanoscience and Nanotechnology (NEST), Istituto Nanoscienze Consiglio Nazionale delle Ricerche (CNR) and Scuola Normale Superiore Pisa, 56127 Pisa, Italy; (O.C.); (E.P.); (F.T.)
| | - Filippo M. Santorelli
- Molecular Medicine, IRCCS Fondazione Stella Maris, Via dei Giacinti 2, 56028 Pisa, Italy; (F.S.); (F.M.S.)
| | - Gian Michele Ratto
- National Enterprise for Nanoscience and Nanotechnology (NEST), Istituto Nanoscienze Consiglio Nazionale delle Ricerche (CNR) and Scuola Normale Superiore Pisa, 56127 Pisa, Italy; (O.C.); (E.P.); (F.T.)
- Correspondence: (G.M.R.); (M.M.); Tel.: +39-050-3153168 (G.M.R.); +39-050-886332 (M.M.)
| | - Maria Marchese
- Molecular Medicine, IRCCS Fondazione Stella Maris, Via dei Giacinti 2, 56028 Pisa, Italy; (F.S.); (F.M.S.)
- Correspondence: (G.M.R.); (M.M.); Tel.: +39-050-3153168 (G.M.R.); +39-050-886332 (M.M.)
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19
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Li X, Tao S, Jamal-Omidi S, Huang Y, Lhatoo SD, Zhang GQ, Cui L. Detection of Postictal Generalized Electroencephalogram Suppression: Random Forest Approach. JMIR Med Inform 2020; 8:e17061. [PMID: 32130173 PMCID: PMC7055778 DOI: 10.2196/17061] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 12/20/2019] [Accepted: 12/29/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Sudden unexpected death in epilepsy (SUDEP) is second only to stroke in neurological events resulting in years of potential life lost. Postictal generalized electroencephalogram (EEG) suppression (PGES) is a period of suppressed brain activity often occurring after generalized tonic-clonic seizure, a most significant risk factor for SUDEP. Therefore, PGES has been considered as a potential biomarker for SUDEP risk. Automatic PGES detection tools can address the limitations of labor-intensive, and sometimes inconsistent, visual analysis. A successful approach to automatic PGES detection must overcome computational challenges involved in the detection of subtle amplitude changes in EEG recordings, which may contain physiological and acquisition artifacts. OBJECTIVE This study aimed to present a random forest approach for automatic PGES detection using multichannel human EEG recordings acquired in epilepsy monitoring units. METHODS We used a combination of temporal, frequency, wavelet, and interchannel correlation features derived from EEG signals to train a random forest classifier. We also constructed and applied confidence-based correction rules based on PGES state changes. Motivated by practical utility, we introduced a new, time distance-based evaluation method for assessing the performance of PGES detection algorithms. RESULTS The time distance-based evaluation showed that our approach achieved a 5-second tolerance-based positive prediction rate of 0.95 for artifact-free signals. For signals with different artifact levels, our prediction rates varied from 0.68 to 0.81. CONCLUSIONS We introduced a feature-based, random forest approach for automatic PGES detection using multichannel EEG recordings. Our approach achieved increasingly better time distance-based performance with reduced signal artifact levels. Further study is needed for PGES detection algorithms to perform well irrespective of the levels of signal artifacts.
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Affiliation(s)
- Xiaojin Li
- Department of Neurology, University of Texas Health Science Center, Houston, TX, United States
| | - Shiqiang Tao
- Department of Neurology, University of Texas Health Science Center, Houston, TX, United States
| | - Shirin Jamal-Omidi
- Department of Neurology, University of Texas Health Science Center, Houston, TX, United States
| | - Yan Huang
- Department of Computer Science, University of Kentucky, Lexington, KY, United States
| | - Samden D Lhatoo
- Department of Neurology, University of Texas Health Science Center, Houston, TX, United States
| | - Guo-Qiang Zhang
- Department of Neurology, University of Texas Health Science Center, Houston, TX, United States
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States
| | - Licong Cui
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States
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20
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Pisarchik AN, Maksimenko VA, Hramov AE. From Novel Technology to Novel Applications: Comment on "An Integrated Brain-Machine Interface Platform With Thousands of Channels" by Elon Musk and Neuralink. J Med Internet Res 2019; 21:e16356. [PMID: 31674923 PMCID: PMC6914250 DOI: 10.2196/16356] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Revised: 10/16/2019] [Accepted: 10/20/2019] [Indexed: 01/20/2023] Open
Affiliation(s)
- Alexander N Pisarchik
- Center for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcón, Madrid, Spain.,Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russian Federation
| | - Vladimir A Maksimenko
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russian Federation
| | - Alexander E Hramov
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russian Federation
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