101
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Zhang Y, Yang R, Zhou W. Roughness-Length-Based Characteristic Analysis of Intracranial EEG and Epileptic Seizure Prediction. Int J Neural Syst 2020; 30:2050072. [DOI: 10.1142/s0129065720500720] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
To identify precursors of epileptic seizures, an EEG characteristic analysis is carried out based on a roughness-length method, where fractal dimensions and intercept values are extracted to measure the structure complexity and the amplitude roughness of EEG signals in different phases. Using the significant changes of the fractal dimension and intercept in the preictal phase with respect to those in the interictal phase, a patient-specific seizure prediction algorithm is then proposed by combining with a gradient boosting classifier. The probabilistic outputs of the trained gradient boosting classifier are further processed by threshold comparison and rule-based judgment to distinguish preictal EEG from interictal EEG and to generate seizure alerts. The prediction algorithm was evaluated on 20 patients’ intracranial EEG recordings from the Freiburg EEG database, which contains the preictal periods of 65 seizures and 499[Formula: see text]h interictal EEG. Setting the seizure prediction horizon as 2[Formula: see text]min, averaged sensitivity values of 90.42% and 91.67% with averaged false prediction rates of 0.12/h and 0.10/h were achieved for seizure occurrence periods of 30 and 50[Formula: see text]min, respectively. These results demonstrate the ability of fractal dimension and intercept metrics in predicting the occurrence of seizures.
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Affiliation(s)
- Yanli Zhang
- School of Information and Electronic Engineering, Shandong Technology and Business University, 191 Binhai Middle Road, Yantai 264005, P. R. China
| | - Rendi Yang
- School of Electromechanical and Automotive Engineering, Yantai University, Yantai 264005, P. R. China
| | - Weidong Zhou
- School of Microelectronics, Shandong University, Jinan 250100, P. R. China
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102
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Time-frequency analysis and fuzzy-based detection of heat-stressed sleep EEG spectra. Med Biol Eng Comput 2020; 59:23-39. [PMID: 33188622 DOI: 10.1007/s11517-020-02278-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 10/20/2020] [Indexed: 11/27/2022]
Abstract
Nowadays, sleep disorders are contemplated as the major issue in the human lives. The current work aims at extraction of time-frequency information from recorded dataset and provides an efficient sleep stage detection method. Recordings of brain signal namely electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) were carried out under defined clinical condition for the classification of sleep EEG. Subsequent upon the extraction of various features from the raw EEG data, neuro-fuzzy system is trained to classify the sleep stages into three major classes namely awake, slow wave sleep (SWS), and rapid eye movement sleep (REM). This classification would enable medical professionals to diagnose sleep related disorders accurately. The results obtained clearly indicate that the mean performance for SWS stage is profound as compared to REM and awake stage. Specificity and sensitivity of the proposed method are obtained as 95.4% and 80%, respectively. The average accuracy of the system employing neuro-fuzzy approach is found to be 90.6% in which SWS stage was best detected among the other stages of sleep EEG.Graphical abstract.
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103
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An Investigation of Insider Threat Mitigation Based on EEG Signal Classification. SENSORS 2020; 20:s20216365. [PMID: 33171609 PMCID: PMC7664688 DOI: 10.3390/s20216365] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 11/02/2020] [Accepted: 11/06/2020] [Indexed: 12/04/2022]
Abstract
This study proposes a scheme to identify insider threats in nuclear facilities through the detection of malicious intentions of potential insiders using subject-wise classification. Based on electroencephalography (EEG) signals, a classification model was developed to identify whether a subject has a malicious intention under scenarios of being forced to become an insider threat. The model also distinguishes insider threat scenarios from everyday conflict scenarios. To support model development, 21-channel EEG signals were measured on 25 healthy subjects, and sets of features were extracted from the time, time–frequency, frequency and nonlinear domains. To select the best use of the available features, automatic selection was performed by random-forest-based algorithms. The k-nearest neighbor, support vector machine with radial kernel, naïve Bayes, and multilayer perceptron algorithms were applied for the classification. By using EEG signals obtained while contemplating becoming an insider threat, the subject-wise model identified malicious intentions with 78.57% accuracy. The model also distinguished insider threat scenarios from everyday conflict scenarios with 93.47% accuracy. These findings could be utilized to support the development of insider threat mitigation systems along with existing trustworthiness assessments in the nuclear industry.
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104
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Narin A. Detection of Focal and Non-focal Epileptic Seizure Using Continuous Wavelet Transform-Based Scalogram Images and Pre-trained Deep Neural Networks. Ing Rech Biomed 2020. [DOI: 10.1016/j.irbm.2020.11.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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105
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Moradi F, Mohammadi H, Rezaei M, Sariaslani P, Razazian N, Khazaie H, Adeli H. A Novel Method for Sleep-Stage Classification Based on Sonification of Sleep Electroencephalogram Signals Using Wavelet Transform and Recurrent Neural Network. Eur Neurol 2020; 83:468-486. [PMID: 33120386 DOI: 10.1159/000511306] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Accepted: 09/02/2020] [Indexed: 11/19/2022]
Abstract
INTRODUCTION Visual sleep-stage scoring is a time-consuming technique that cannot extract the nonlinear characteristics of electroencephalogram (EEG). This article presents a novel method for sleep-stage differentiation based on sonification of sleep-EEG signals using wavelet transform and recurrent neural network (RNN). METHODS Two RNNs were designed and trained separately based on a database of classical guitar pieces and Kurdish tanbur Makams using a long short-term memory model. Moreover, discrete wavelet transform and wavelet packet decomposition were used to determine the association between the EEG signals and musical pitches. Continuous wavelet transform was applied to extract musical beat-based features from the EEG. Then, the pretrained RNN was used to generate music. To test the proposed model, 11 sleep EEGs were mapped onto the guitar and tanbur frequency intervals and presented to the pretrained RNN. Next, the generated music was randomly presented to 2 neurologists. RESULTS The proposed model classified the sleep stages with an accuracy of >81% for tanbur and more than 93% for guitar musical pieces. The inter-rater reliability measured by Cohen's kappa coefficient (κ) revealed good reliability for both tanbur (κ = 0.64, p < 0.001) and guitar musical pieces (κ = 0.85, p < 0.001). CONCLUSION The present EEG sonification method leads to valid sleep staging by clinicians. The method could be used on various EEG databases for classification, differentiation, diagnosis, and treatment purposes. Real-time EEG sonification can be used as a feedback tool for replanning of neurophysiological functions for the management of many neurological and psychiatric disorders in the future.
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Affiliation(s)
- Foad Moradi
- Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran.,Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran.,Department of Neurology, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Hiwa Mohammadi
- Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran, .,Department of Neurology, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran, .,Clinical Research Development Center, Imam Reza Hospital, Kermanshah University of Medical Sciences, Kermanshah, Iran,
| | - Mohammad Rezaei
- Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran.,Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Payam Sariaslani
- Department of Neurology, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran.,Clinical Research Development Center, Imam Reza Hospital, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Nazanin Razazian
- Department of Neurology, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran.,Clinical Research Development Center, Imam Reza Hospital, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Habibolah Khazaie
- Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Hojjat Adeli
- Department of Biomedical Informatics and Department of Neuroscience, The Ohio State University, Columbus, Ohio, USA
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106
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Liu Y, Lin Y, Jia Z, Ma Y, Wang J. Representation based on ordinal patterns for seizure detection in EEG signals. Comput Biol Med 2020; 126:104033. [PMID: 33091826 DOI: 10.1016/j.compbiomed.2020.104033] [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] [Received: 03/10/2020] [Revised: 09/25/2020] [Accepted: 10/01/2020] [Indexed: 11/26/2022]
Abstract
EEG signals carry rich information about brain activity and play an important role in the diagnosis and recognition of epilepsy. Numerous algorithms using EEG signals to detect seizures have been developed in recent decades. However, most of them require well-designed features that highly depend on domain-specific knowledge and algorithm expertise. In this study, we introduce the unigram ordinal pattern (UniOP) and bigram ordinal pattern (BiOP) representations to capture the different underlying dynamics of time series, which only assumes that time series derived from different dynamics can be characterized by repeated ordinal patterns. Specifically, we first transform each subsequence in a time series into the corresponding ordinal pattern in terms of the ranking of values and consider the distribution of ordinal patterns of all subsequences as the UniOP representation. Furthermore, we consider the distribution of the cooccurrence of ordinal patterns as the BiOP representation to characterize the contextual information for each ordinal pattern. We then combine the proposed representations with the nearest neighbor algorithm to evaluate its effectiveness on three publicly available seizure datasets. The results on the Bonn EEG dataset demonstrate that this method provides more than 90% accuracy, sensitivity, and specificity in most cases and outperforms several state-of-the-art methods, which proves its ability to capture the key information of the underlying dynamics of EEG time series at healthy, seizure-free, and seizure states. The results on the second dataset are comparable with the state-of-the-art method, showing the good generalization ability of the proposed method. All performance metrics on the third dataset are approximately 89%, which demonstrates that the proposed representations are suitable for large-scale datasets.
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Affiliation(s)
- Yunxiao Liu
- Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, PR China; CAAC Key Laboratory of Intelligent Passenger Service of Civil Aviation, Beijing, China
| | - Youfang Lin
- Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, PR China; CAAC Key Laboratory of Intelligent Passenger Service of Civil Aviation, Beijing, China
| | - Ziyu Jia
- Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, PR China; CAAC Key Laboratory of Intelligent Passenger Service of Civil Aviation, Beijing, China
| | - Yan Ma
- Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Jing Wang
- Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, PR China; CAAC Key Laboratory of Intelligent Passenger Service of Civil Aviation, Beijing, China.
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107
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Four-dimensional computed tomography angiography analysis of internal carotid arteries opacification at the skull base to detect delayed cerebral ischemia: a feasibility study. Int J Comput Assist Radiol Surg 2020; 15:2005-2015. [PMID: 33026600 DOI: 10.1007/s11548-020-02268-y] [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: 04/29/2020] [Accepted: 09/22/2020] [Indexed: 10/23/2022]
Abstract
PURPOSE Delayed cerebral ischemia represents a significant cause of poor functional outcome for patients with vasospasm after subarachnoid hemorrhage. We investigated whether delayed cerebral ischemia could be detected by the arterial opacification of internal carotid artery at the level of the skull base. METHODS In this exploratory, nested retrospective cohort diagnostic accuracy study, patients with clinical and/or transcranial Doppler suspicion of vasospasm who underwent four-dimensional computed tomography angiography were included. They were split into two groups for the main endpoint analysis, according to the actually adopted morphological (cerebral infarction) and clinical criteria (neurologic deterioration) of delayed cerebral ischemia. Opacification with a temporal resolution of 0.15 s of both internal carotid arteries at the skull base level was obtained through a semi-automated segmentation method based on skeletonization, and analyzed by a wavelet transform (rbio2.2, level 1). The results obtained by k-means clustering were analyzed with regard to the state of delayed cerebral infarction. RESULTS Over ten patients included and analyzed, five patients presented a delayed cerebral ischemia, two of them in both side. The semi-automated processing and analysis clustered two different types of opacification curves. The obtaining of a nonlinear opacification pattern was associated (p < 0.001) with delayed cerebral ischemia. CONCLUSIONS The analysis of arterial opacification of internal carotid arteries at skull base by the proposed processing is feasible and leads to cluster two types of opacification that may help to early detect and prevent delayed cerebral ischemia, in particularly when examinations are artifacted by aneurysm treatment materials.
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108
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Song X, Aguilar L, Yoon SC. AA Comparison of Dynamic Modeling Approaches for Epileptic EEG Detection and Classification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:523-527. [PMID: 33018042 DOI: 10.1109/embc44109.2020.9175979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Electroencephalogram (EEG) has been intensively used as a diagnosis tool for epilepsy. The traditional diagnostic procedure relies on a recording of EEG from several days up to a few weeks, and the recordings are visually inspected by trained medical professionals. This procedure is time consuming with a high misdiagnosis rate. In recent years, computer-aided techniques have been proposed to automate the epilepsy diagnosis by using machine learning methods to analyze EEG data. Considering the time-varying nature of EEG, the goal of this work is to characterize dynamic changes of EEG patterns for the detection and classification of epilepsy. Four different dynamic Bayesian modeling methods were evaluated using multi-subject epileptic EEG data. Experimental results show that an accuracy of 98.0% can be achieved by one of the four methods. The same method also provides an overall accuracy of 87.7% for the classification of seven different seizure types.
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109
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Li Q, Gao J, Huang Q, Wu Y, Xu B. Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Scale-Dependent Lyapunov Exponent. Front Bioeng Biotechnol 2020; 8:1006. [PMID: 33015003 PMCID: PMC7506120 DOI: 10.3389/fbioe.2020.01006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 07/31/2020] [Indexed: 11/23/2022] Open
Abstract
Epileptiform discharges are of fundamental importance in understanding the physiology of epilepsy. To aid in the clinical diagnosis, classification, prognosis, and treatment of epilepsy, it is important to develop automated computer programs to distinguish epileptiform discharges from normal electroencephalogram (EEG). This is a challenging task as clinically used scalp EEG often contains a lot of noise and motion artifacts. The challenge is even greater if one wishes to develop explainable rather than black-box based approaches. To take on this challenge, we propose to use a multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE). We analyzed 640 multi-channel EEG segments, each 4 s long. Among these segments, 540 are short epileptiform discharges, and 100 are from healthy controls. We found that features from SDLE were very effective in distinguishing epileptiform discharges from normal EEG. Using Random Forest Classifier (RF) and Support Vector Machines (SVM), the proposed approach with different features from SDLE robustly achieves an accuracy exceeding 99% in distinguishing epileptiform discharges from normal control ones. A single parameter, which is the ratio of the spectral energy of EEG signals and the SDLE and quantifies the regularity or predictability of the EEG signals, is introduced to better understand the high accuracy in the classification. It is found that this regularity is considerably greater for epileptiform discharges than for normal controls. Robustly having high accuracy in distinguishing epileptiform discharges from normal controls irrespective of which classification scheme being used, the proposed approach has the potential to be used widely in a clinical setting.
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Affiliation(s)
- Qiong Li
- School of Computer, Electronics and Information, Guangxi University, Nanning, China
| | - Jianbo Gao
- Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing, China.,Institute of Automation, Chinese Academy of Sciences, Beijing, China.,International College, Guangxi University, Nanning, China
| | - Qi Huang
- The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yuan Wu
- The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Bo Xu
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
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110
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Tang FG, Liu Y, Li Y, Peng ZW. A unified multi-level spectral–temporal feature learning framework for patient-specific seizure onset detection in EEG signals. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.106152] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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111
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Anwar T. A Machine Learning approach for Recognizing Intellectual Development Disorder using EEG. 2020 INTERNATIONAL CONFERENCE ON BIOMEDICAL INNOVATIONS AND APPLICATIONS (BIA) 2020. [DOI: 10.1109/bia50171.2020.9244283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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112
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Brogin JAF, Faber J, Bueno DD. An Efficient Approach to Define the Input Stimuli to Suppress Epileptic Seizures Described by the Epileptor Model. Int J Neural Syst 2020; 30:2050062. [PMID: 32938259 DOI: 10.1142/s0129065720500628] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Epilepsy affects about 70 million people in the world. Every year, approximately 2.4 million people are diagnosed with epilepsy, two-thirds of them will not know the etiology of their disease, and 1% of these individuals will decease as a consequence of it. Due to the inherent complexity of predicting and explaining it, the mathematical model Epileptor was recently developed to reproduce seizure-like events, also providing insights to improve the understanding of the neural dynamics in the interictal and ictal periods, although the physics behind each parameter and variable of the model is not fully established in the literature. This paper introduces an approach to design a feedback-based controller for suppressing epileptic seizures described by Epileptor. Our work establishes how the nonlinear dynamics of this disorder can be written in terms of a combination of linear sub-models employing an exact solution. Additionally, we show how a feedback control gain can be computed to suppress seizures, as well as how specific shapes applied as input stimuli for this purpose can be obtained. The practical application of the approach is discussed and the results show that the proposed technique is promising for developing controllers in this field.
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Affiliation(s)
- João Angelo Ferres Brogin
- Department of Mechanical Engineering, São Paulo State University (UNESP), School of Engineering of Ilha Solteira, 56 Brasil Avenue, Ilha Solteira, São Paulo 15385-000, Brazil
| | - Jean Faber
- Department of Neurology and Neurosurgery, Federal University of São Paulo (UNIFESP), 667 Pedro de Toledo Street, São Paulo 04039-032, Brazil
| | - Douglas Domingues Bueno
- Department of Mathematics, São Paulo State University (UNESP), School of Engineering of Ilha Solteira, 56 Brasil Avenue, Ilha Solteira, São Paulo 15385-000, Brazil
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113
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Follis JL, Lai D. Variability analysis of epileptic EEG using the maximal overlap discrete wavelet transform. Health Inf Sci Syst 2020; 8:26. [PMID: 32999715 DOI: 10.1007/s13755-020-00118-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 09/02/2020] [Indexed: 11/29/2022] Open
Abstract
Purpose To determine if there is a difference in the wavelet variances of seizure and non-seizure channels in the EEG of an epileptic subject. Methods A six-level decomposition was applied using the Maximal Overlap Discrete Wavelet Transform (MODWT). The wavelet variance and 95% CIs were calculated for each level of the decomposition. The number of changes in variance for each level were found using a change-point detection method of Whitcher. The Kruskal-Wallis test was used to determine if there were differences in the median number of change points within channels and across frequency bands (levels). Results No distinctive pattern was found for the wavelet variances to differentiate the seizure and non-seizure channels. The seizure channels tended to have lower variances for each level and overall, but this pattern only held for one of the three seizure channels (RAST4). The median number of change points did not differ between the seizure and non-seizure channels either within each channel or across the frequency bands. Conclusion The use of the MODWT in examining the variances and changes in variance did not show specific patterns which differentiate between seizure and non-seizure channels.
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Affiliation(s)
- Jack L Follis
- Department of Mathematics and Computer Science, University of St. Thomas, 3800 Montrose Boulevard, Houston, TX 77006 USA
| | - Dejian Lai
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, School of Public Health, 1200 Herman Pressler Drive, W-1008, Houston, TX 77030 USA
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114
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San-Juan D, Rodríguez-Méndez DA. Epilepsy as a disease affecting neural networks: A neurophysiological perspective. Neurologia 2020; 38:S0213-4853(20)30213-9. [PMID: 32912747 DOI: 10.1016/j.nrl.2020.06.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 05/09/2020] [Accepted: 06/12/2020] [Indexed: 10/23/2022] Open
Abstract
INTRODUCTION The brain is a series of networks of functionally and anatomically connected, bilaterally represented structures; in epilepsy, activity of any part of the brain affects activity in the other parts. This is relevant for understanding the pathophysiology, diagnosis, and prognosis of the disease. OBJECTIVE In this study, we present a state-of-the-art review of the neurophysiological view of epilepsy as a disease affecting neural networks. RESULTS We describe the basic and advanced principles of epilepsy as a disease affecting neural networks, based on the use of different clinical and mathematical techniques from a neurophysiological perspective, and signal the limitations of these findings in the clinical context. CONCLUSIONS Epilepsy is a disease affecting complex neural networks. Understanding these will enable better management and prognostic confidence.
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Affiliation(s)
- D San-Juan
- Departamento de Investigación Clínica, Instituto Nacional de Neurología y Neurocirugía, Ciudad de México, México.
| | - D A Rodríguez-Méndez
- Facultad de Ciencias, Universidad Autónoma del Estado de México, Toluca de Lerdo, México
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115
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Adaptive median feature baseline correction for improving recognition of epileptic seizures in ICU EEG. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.04.144] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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116
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Ouyang CS, Yang RC, Wu RC, Chiang CT, Lin LC. Determination of Antiepileptic Drugs Withdrawal Through EEG Hjorth Parameter Analysis. Int J Neural Syst 2020; 30:2050036. [PMID: 32812470 DOI: 10.1142/s0129065720500367] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The decision to continue or to stop antiepileptic drug (AED) treatment in patients with prolonged seizure remission is a critical issue. Previous studies have used certain risk factors or electroencephalogram (EEG) findings to predict seizure recurrence after the withdrawal of AEDs. However, validated biomarkers to guide the withdrawal of AEDs are lacking. In this study, we used quantitative EEG analysis to establish a method for predicting seizure recurrence after the withdrawal of AEDs. A total of 34 patients with epilepsy were divided into two groups, 17 patients in the recurrence group and the other 17 patients in the nonrecurrence group. All patients were seizure free for at least two years. Before AED withdrawal, an EEG was performed for each patient that showed no epileptiform discharges. These EEG recordings were classified using Hjorth parameter-based EEG features. We found that the Hjorth complexity values were higher in patients in the recurrence group than in the nonrecurrence group. The extreme gradient boosting classification method achieved the highest performance in terms of accuracy, area under the curve, sensitivity, and specificity (84.76%, 88.77%, 89.67%, and 80.47%, respectively). Our proposed method is a promising tool to help physicians determine AED withdrawal for seizure-free patients.
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Affiliation(s)
- Chen-Sen Ouyang
- Department of Information Engineering, I-Shou University, No. 1, Section 1, Syuecheng Road, Dashu District, Kaohsiung 84001, Taiwan
| | - Rei-Cheng Yang
- Departments of Pediatrics, Kaohsiung Medical University Hospital, Kaohsiung Medical University, No. 100, Shin-Chuan 1st Road, Kaohsiung 80708, Taiwan
| | - Rong-Ching Wu
- Department of Electrical Engineering, I-Shou University, No. 1, Section 1, Syuecheng Road, Dashu District, Kaohsiung 84001, Taiwan
| | - Ching-Tai Chiang
- Department of Computer and Communication, National Pingtung University, No. 51, Minsheng Road, Pingtung 900392, Taiwan
| | - Lung-Chang Lin
- Department of Pediatrics, School of Medicine, College of Medicine, Departments of Pediatrics, Kaohsiung Medical University Hospital, Kaohsiung Medical University, No. 100, Shin-Chuan 1st Road, Kaohsiung 80708, Taiwan
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117
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Li Q, Gao J, Zhang Z, Huang Q, Wu Y, Xu B. Distinguishing Epileptiform Discharges From Normal Electroencephalograms Using Adaptive Fractal and Network Analysis: A Clinical Perspective. Front Physiol 2020; 11:828. [PMID: 32903770 PMCID: PMC7438848 DOI: 10.3389/fphys.2020.00828] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 06/22/2020] [Indexed: 01/03/2023] Open
Abstract
Epilepsy is one of the most common disorders of the brain. Clinically, to corroborate an epileptic seizure-like symptom and to find the seizure localization, electroencephalogram (EEG) data are often visually examined by a clinical doctor to detect the presence of epileptiform discharges. Epileptiform discharges are transient waveforms lasting for several tens to hundreds of milliseconds and are mainly divided into seven types. It is important to develop systematic approaches to accurately distinguish these waveforms from normal control ones. This is a difficult task if one wishes to develop first principle rather than black-box based approaches, since clinically used scalp EEGs usually contain a lot of noise and artifacts. To solve this problem, we analyzed 640 multi-channel EEG segments, each 4s long. Among these segments, 540 are short epileptiform discharges, and 100 are from healthy controls. We have proposed two approaches for distinguishing epileptiform discharges from normal EEGs. The first method is based on Signal Range and EEGs' long range correlation properties characterized by the Hurst parameter H extracted by applying adaptive fractal analysis (AFA), which can also maximally suppress the effects of noise and various kinds of artifacts. Our second method is based on networks constructed from three aspects of the scalp EEG signals, the Signal Range, the energy of the alpha wave component, and EEG's long range correlation properties. The networks are further analyzed using singular value decomposition (SVD). The square of the first singular value from SVD is used to construct features to distinguish epileptiform discharges from normal controls. Using Random Forest Classifier (RF), our approaches can achieve very high accuracy in distinguishing epileptiform discharges from normal control ones, and thus are very promising to be used clinically. The network-based approach is also used to infer the localizations of each type of epileptiform discharges, and it is found that the sub-networks representing the most likely location of each type of epileptiform discharges are different among the seven types of epileptiform discharges.
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Affiliation(s)
- Qiong Li
- School of Computer, Electronics and Information, Guangxi University, Nanning, China
| | - Jianbo Gao
- Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing, China
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- International College, Guangxi University, Nanning, Guangxi, China
| | - Ziwen Zhang
- School of Computer, Electronics and Information, Guangxi University, Nanning, China
| | - Qi Huang
- The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yuan Wu
- The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Bo Xu
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
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Kim T, Nguyen P, Pham N, Bui N, Truong H, Ha S, Vu T. Epileptic Seizure Detection and Experimental Treatment: A Review. Front Neurol 2020; 11:701. [PMID: 32849189 PMCID: PMC7396638 DOI: 10.3389/fneur.2020.00701] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 07/09/2020] [Indexed: 01/18/2023] Open
Abstract
One-fourths of the patients have medication-resistant seizures and require seizure detection and treatment continuously to cope with sudden seizures. Seizures can be detected by monitoring the brain and muscle activities, heart rate, oxygen level, artificial sounds, or visual signatures through EEG, EMG, ECG, motion, or audio/video recording on the human head and body. In this article, we first discuss recent advances in seizure sensing, signal processing, time- or frequency-domain analysis, and classification algorithms to detect and classify seizure stages. Then, we show a strong potential of applying recent advancements in non-invasive brain stimulation technology to treat seizures. In particular, we explain the fundamentals of brain stimulation approaches, including (1) transcranial magnetic stimulation (TMS), (2) transcranial direct current stimulation (tDCS), (3) transcranial focused ultrasound stimulation (tFUS), and how to use them to treat seizures. Through this review, we intend to provide a broad view of both recent seizure diagnoses and treatments. Such knowledge would help fresh and experienced researchers to capture the advancements in sensing, detection, classification, and treatment seizures. Last but not least, we provide potential research directions that would attract seizure researchers/engineers in the field.
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Affiliation(s)
- Taeho Kim
- Department of Computer Science, University of Colorado, Boulder, CO, United States
| | - Phuc Nguyen
- Department of Computer Science, University of Colorado, Boulder, CO, United States
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, United States
| | - Nhat Pham
- Department of Computer Science, University of Colorado, Boulder, CO, United States
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Nam Bui
- Department of Computer Science, University of Colorado, Boulder, CO, United States
| | - Hoang Truong
- Department of Computer Science, University of Colorado, Boulder, CO, United States
| | - Sangtae Ha
- Department of Computer Science, University of Colorado, Boulder, CO, United States
| | - Tam Vu
- Department of Computer Science, University of Colorado, Boulder, CO, United States
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
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119
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Sebastián-Romagosa M, Udina E, Ortner R, Dinarès-Ferran J, Cho W, Murovec N, Matencio-Peralba C, Sieghartsleitner S, Allison BZ, Guger C. EEG Biomarkers Related With the Functional State of Stroke Patients. Front Neurosci 2020; 14:582. [PMID: 32733182 PMCID: PMC7358582 DOI: 10.3389/fnins.2020.00582] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 05/12/2020] [Indexed: 12/20/2022] Open
Abstract
Introduction Recent studies explored promising new quantitative methods to analyze electroencephalography (EEG) signals. This paper analyzes the correlation of two EEG parameters, Brain Symmetry Index (BSI) and Laterality Coefficient (LC), with established functional scales for the stroke assessment. Methods Thirty-two healthy subjects and thirty-six stroke patients with upper extremity hemiparesis were recruited for this study. The stroke patients where subdivided in three groups according to the stroke location: Cortical, Subcortical, and Cortical + Subcortical. The participants performed assessment visits to record the EEG in the resting state and perform functional tests using rehabilitation scales. Then, stroke patients performed 25 sessions using a motor-imagery based Brain Computer Interface system (BCI). BSI was calculated with the EEG data in resting state and LC was calculated with the Event-Related Synchronization maps. Results The results of this study demonstrated significant differences in the BSI between the healthy group and Subcortical group (P = 0.001), and also between the healthy and Cortical+Subcortical group (P = 0.019). No significant differences were found between the healthy group and the Cortical group (P = 0.505). Furthermore, the BSI analysis in the healthy group based on gender showed statistical differences (P = 0.027). In the stroke group, the correlation between the BSI and the functional state of the upper extremity assessed by Fugl-Meyer Assessment (FMA) was also significant, ρ = −0.430 and P = 0.046. The correlation between the BSI and the FMA-Lower extremity was not significant (ρ = −0.063, P = 0.852). Similarly, the LC calculated in the alpha band has significative correlation with FMA of upper extremity (ρ = −0.623 and P < 0.001) and FMA of lower extremity (ρ = −0.509 and P = 0.026). Other important significant correlations between LC and functional scales were observed. In addition, the patients showed an improvement in the FMA-upper extremity after the BCI therapy (ΔFMA = 1 median [IQR: 0–8], P = 0.002). Conclusion The quantitative EEG tools used here may help support our understanding of stroke and how the brain changes during rehabilitation therapy. These tools can help identify changes in EEG biomarkers and parameters during therapy that might lead to improved therapy methods and functional prognoses.
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Affiliation(s)
- Marc Sebastián-Romagosa
- Department of Physiology, Universitat Autònoma de Barcelona, Barcelona, Spain.,g.tec Medical Engineering Spain SL, Barcelona, Spain
| | - Esther Udina
- Department of Physiology, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Rupert Ortner
- g.tec Medical Engineering Spain SL, Barcelona, Spain
| | - Josep Dinarès-Ferran
- g.tec Medical Engineering Spain SL, Barcelona, Spain.,Data and Signal Processing Research Group, Department of Engineering, University of Vic - Central University of Catalonia, Vic, Spain
| | - Woosang Cho
- g.tec Medical Engineering GmbH, Schiedlberg, Austria
| | - Nensi Murovec
- g.tec Medical Engineering GmbH, Schiedlberg, Austria
| | | | | | - Brendan Z Allison
- Department of Cognitive Science, University of California at San Diego, La Jolla, CA, United States
| | - Christoph Guger
- g.tec Medical Engineering Spain SL, Barcelona, Spain.,g.tec Medical Engineering GmbH, Schiedlberg, Austria
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120
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Sharma A, Rai JK, Tewari RP. Scalp electroencephalography (sEEG) based advanced prediction of epileptic seizure time and identification of epileptogenic region. BIOMED ENG-BIOMED TE 2020; 65:/j/bmte.ahead-of-print/bmt-2020-0044/bmt-2020-0044.xml. [PMID: 32628623 DOI: 10.1515/bmt-2020-0044] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 03/27/2020] [Indexed: 11/15/2022]
Abstract
Objectives Epilepsy is characterized by uncontrollable seizure during which consciousness of patient is disturbed. Prediction of the seizure in advance will increase the remedial possibilities for the patients suffering from epilepsy. An automated system for seizure prediction is important for seizure enactment, prevention of sudden unexpected deaths and to avoid seizure related injuries. Methods This paper proposes the prediction of an upcoming seizure by analyzing the 23 channel non-stationary EEG signal. EEG signal is divided into smaller segments to change it into quasi-stationary data using an overlapping moving window. Brain region is marked into four regions namely left hemisphere, right hemisphere, central region and temporal region to identify the epileptogenic region. The epileptogenic region shows significant variations during pre-ictal state in comparison to the other regions. So, seizure prediction is carried out by analyzing EEG signals from this region. Seizure prediction is proposed using features extracted from both time and frequency domain. Relative entropy and relative energy are extracted from wavelet transform and Pearson correlation coefficient is obtained from time domain EEG signal. Extracted features have been smoothened using moving average filter. First order derivative of relative features have been used to normalize the intervariability before deciding the threshold for marking the prediction of seizure. Results Isolated seizures where pre-ictal duration of more than 1 h is reported has been detected with an accuracy of 92.18% with precursory warning 18 min in advance and seizure confirmation 12 min in advance. An overall accuracy of 83.33% with false positive alarm rate of 0.01/h has been obtained for all seizure cases with average prediction time of 9.9 min.
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Affiliation(s)
- Aarti Sharma
- Department of ECE, Inderprastha Engineering College, Site-IV, Sahibabad, Ghaziabad, Uttar Pradesh, India
| | - Jaynendra Kumar Rai
- Department of Electronics and Communication Engineering, ASET, Amity University Uttar Pradesh, Sector-125, Noida, India
| | - Ravi Prakash Tewari
- Department of Applied Mechanics, Motilal Nehru National Institute of Technology, Allahabad, Uttar Pradesh, India
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121
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Zhou D, Li X. Epilepsy EEG Signal Classification Algorithm Based on Improved RBF. Front Neurosci 2020; 14:606. [PMID: 32655355 PMCID: PMC7324866 DOI: 10.3389/fnins.2020.00606] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 05/18/2020] [Indexed: 11/24/2022] Open
Abstract
Epilepsy is a chronic recurrent transient brain dysfunction syndrome. It is characterized by recurrent epilepsy caused by abnormal discharge of brain neurons. Epilepsy is one of the common diseases in nervous system. The analysis of EEG signals is a hot topic in current research. In order to solve the problem of epileptic EEG signals classification accurately, we carry out in-depth research on epileptic EEG signals, analyze features from linear and non-linear perspectives, input them into the improved RBF model to dynamically extract effective features, and introduce one against one strategy classifier to reduce the probability of error classification. Experiments show that the proposed algorithm has strong robustness and high epileptic signal recognition rate.
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Affiliation(s)
- Dongmei Zhou
- College of Information Science and Technology, Chengdu University of Technology, Chengdu, China
| | - Xuemei Li
- Xijing Hospital, Air Force Medical University, Xi'an, China
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122
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Identification of Epileptic EEG Signals Using Convolutional Neural Networks. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10124089] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Epilepsy is one of the chronic neurological disorders that is characterized by a sudden burst of excess electricity in the brain. This abnormality appears as a seizure, the detection of which is an important research topic. An important tool used to study brain activity features, neurological disorders and particularly epileptic seizures, is known as electroencephalography (EEG). The visual inspection of epileptic abnormalities in EEG signals by neurologists is time-consuming. Different scientific approaches have been used to accurately detect epileptic seizures from EEG signals, and most of those approaches have obtained good performance. In this study, deep learning based on convolutional neural networks (CNN) was considered to increase the performance of the identification system of epileptic seizures. We applied a cross-validation technique in the design phase of the system. For efficiency, comparative results between other machine-learning approaches and deep CNNs have been obtained. The experiments were performed using standard datasets. The results obtained indicate the efficiency of using CNN in the detection of epilepsy.
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123
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Aileni RM, Pasca S, Florescu A. EEG-Brain Activity Monitoring and Predictive Analysis of Signals Using Artificial Neural Networks. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3346. [PMID: 32545622 PMCID: PMC7348967 DOI: 10.3390/s20123346] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 06/02/2020] [Accepted: 06/08/2020] [Indexed: 01/26/2023]
Abstract
Predictive observation and real-time analysis of the values of biomedical signals and automatic detection of epileptic seizures before onset are beneficial for the development of warning systems for patients because the patient, once informed that an epilepsy seizure is about to start, can take safety measures in useful time. In this article, Daubechies discrete wavelet transform (DWT) was used, coupled with analysis of the correlations between biomedical signals that measure the electrical activity in the brain by electroencephalogram (EEG), electrical currents generated in muscles by electromyogram (EMG), and heart rate monitoring by photoplethysmography (PPG). In addition, we used artificial neural networks (ANN) for automatic detection of epileptic seizures before onset. We analyzed 30 EEG recordings 10 min before a seizure and during the seizure for 30 patients with epilepsy. In this work, we investigated the ANN dimensions of 10, 50, 100, and 150 neurons, and we found that using an ANN with 150 neurons generates an excellent performance in comparison to a 10-neuron-based ANN. However, this analyzes requests in an increased amount of time in comparison with an ANN with a lower neuron number. For real-time monitoring, the neurons number should be correlated with the response time and power consumption used in wearable devices.
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Affiliation(s)
- Raluca Maria Aileni
- Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, 060042 Bucharest, Romania; (S.P.); (A.F.)
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Abstract
Objective: To determine if there was a difference in the volatility characteristics of seizure and non-seizure onset channels in the intracranial electroencephalogram (EEG) in a patient with temporal lobe epilepsy. Methods: The half-life of volatility for the different EEG channels was determined using Autoregressive Moving Average–Generalized Autoregressive Conditional Heteroscedasticity (ARMA–GARCH) models; confidence intervals were constructed using the delta method and an asymptotic method for comparing the half-lives. Results: Clinically determined seizure onsets occurred over strip electrodes named RAST (Right Anterior Subtemporal) and RMST (Right Mid Subtemporal), at locations 2, 3 and 4, on the strip electrodes. The half-lives of volatility for two of the three seizure channels, RAST3 and RAST4, were found to be significantly lower the rest of the channels for six one-minute EEG segments prior to seizure onset and nine one-minute EEG segments of an awake state. The half-lives of volatility for RAST3 and RAST4 were not significantly different to the non-seizure channels for ten one-minute segments of sleep and ten one-minute segments of sleep-to-awake states. The estimates for the half-lives were consistent for randomly selected one-minute EEG segments. Conclusions: The use of GARCH models may be a useful tool in determining hidden properties in epileptiform EEGs that may lead to better understanding of the seizure generating process.
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125
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Raghu S, Sriraam N, Gommer ED, Hilkman DMW, Temel Y, Rao SV, Hegde AS, Kubben PL. Cross-database evaluation of EEG based epileptic seizures detection driven by adaptive median feature baseline correction. Clin Neurophysiol 2020; 131:1567-1578. [PMID: 32417698 DOI: 10.1016/j.clinph.2020.03.033] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 03/04/2020] [Accepted: 03/12/2020] [Indexed: 01/15/2023]
Abstract
OBJECTIVE In long-term electroencephalogram (EEG) signals, automated classification of epileptic seizures is desirable in diagnosing epilepsy patients, as it otherwise depends on visual inspection. To the best of the author's knowledge, existing studies have validated their algorithms using cross-validation on the same database and less number of attempts have been made to extend their work on other databases to test the generalization capability of the developed algorithms. In this study, we present the algorithm for cross-database evaluation for classification of epileptic seizures using five EEG databases collected from different centers. The cross-database framework helps when sufficient epileptic seizures EEG data are not available to build automated seizure detection model. METHODS Two features, namely successive decomposition index and matrix determinant were extracted at a segmentation length of 4 s (50% overlap). Then, adaptive median feature baseline correction (AM-FBC) was applied to overcome the inter-patient and inter-database variation in the feature distribution. The classification was performed using a support vector machine classifier with leave-one-database-out cross-validation. Different classification scenarios were considered using AM-FBC, smoothing of the train and test data, and post-processing of the classifier output. RESULTS Simulation results revealed the highest area under the curve-sensitivity-specificity-false detections (per hour) of 1-1-1-0.15, 0.89-0.99-0.82-2.5, 0.99-0.73-1-1, 0.95-0.97-0.85-1.7, 0.99-0.99-0.92-1.1 using the Ramaiah Medical College and Hospitals, Children's Hospital Boston-Massachusetts Institute of Technology, Temple University Hospital, Maastricht University Medical Centre, and University of Bonn databases respectively. CONCLUSIONS We observe that the AM-FBC plays a significant role in improving seizure detection results by overcoming inter-database variation of feature distribution. SIGNIFICANCE To the best of the author's knowledge, this is the first study reporting on the cross-database evaluation of classification of epileptic seizures and proven to be better generalization capability when evaluated using five databases and can contribute to accurate and robust detection of epileptic seizures in real-time.
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Affiliation(s)
- S Raghu
- Department of Neurosurgery, School for Mental Health and Neuroscience of the Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands; Center for Medical Electronics and Computing, MS Ramaiah Institute of Technology, Bengaluru, India.
| | - Natarajan Sriraam
- Center for Medical Electronics and Computing, MS Ramaiah Institute of Technology, Bengaluru, India.
| | - Erik D Gommer
- Department of Clinical Neurophysiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Danny M W Hilkman
- Department of Clinical Neurophysiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Yasin Temel
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands
| | | | | | - Pieter L Kubben
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands
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126
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Khosla A, Khandnor P, Chand T. A comparative analysis of signal processing and classification methods for different applications based on EEG signals. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.02.002] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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127
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Hosseini MP, Tran TX, Pompili D, Elisevich K, Soltanian-Zadeh H. Multimodal data analysis of epileptic EEG and rs-fMRI via deep learning and edge computing. Artif Intell Med 2020; 104:101813. [DOI: 10.1016/j.artmed.2020.101813] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 12/26/2019] [Accepted: 01/31/2020] [Indexed: 11/28/2022]
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128
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Li Y, Yu Z, Chen Y, Yang C, Li Y, Allen Li X, Li B. Automatic Seizure Detection using Fully Convolutional Nested LSTM. Int J Neural Syst 2020; 30:2050019. [DOI: 10.1142/s0129065720500197] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The automatic seizure detection system can effectively help doctors to monitor and diagnose epilepsy thus reducing their workload. Many outstanding studies have given good results in the two-class seizure detection problems, but most of them are based on hand-wrought feature extraction. This study proposes an end-to-end automatic seizure detection system based on deep learning, which does not require heavy preprocessing on the EEG data or feature engineering. The fully convolutional network with three convolution blocks is first used to learn the expressive seizure characteristics from EEG data. Then these robust EEG features pertinent to seizures are presented as an input to the Nested Long Short-Term Memory (NLSTM) model to explore the inherent temporal dependencies in EEG signals. Lastly, the high-level features obtained from the NLSTM model are fed into the softmax layer to output predicted labels. The proposed method yields an accuracy range of 98.44–100% in 10 different experiments based on the Bonn University database. A larger EEG database is then used to evaluate the performance of the proposed method in real-life situations. The average sensitivity of 97.47%, specificity of 96.17%, and false detection rate of 0.487 per hour are yielded. For CHB–MIT Scalp EEG database, the proposed model also achieves a segment-level sensitivity of 94.07% with a false detection rate of 0.66 per hour. The excellent results obtained on three different EEG databases demonstrate that the proposed method has good robustness and generalization power under ideal and real-life conditions.
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Affiliation(s)
- Yang Li
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, P. R. China
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, P. R. China
| | - Zuyi Yu
- School of Information Science and Engineering, Shandong University, Jinan, Shandong 250100, P. R. China
| | - Yang Chen
- Laboratory of Image Science and Technology, Southeast University, Nanjing, Jiangsu 210096, P. R. China
| | - Chunfeng Yang
- Laboratory of Image Science and Technology, Southeast University, Nanjing, Jiangsu 210096, P. R. China
| | - Yue Li
- School of Clinical Medicine, Dali University, Dali, Yunnan 671000, P. R. China
| | - X. Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Baosheng Li
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, P. R. China
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, P. R. China
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129
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Epileptic seizure prediction by the detection of seizure waveform from the pre-ictal phase of EEG signal. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101720] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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130
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Ieracitano C, Mammone N, Hussain A, Morabito FC. A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia. Neural Netw 2020; 123:176-190. [DOI: 10.1016/j.neunet.2019.12.006] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Revised: 12/03/2019] [Accepted: 12/06/2019] [Indexed: 12/27/2022]
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131
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A review of feature extraction and performance evaluation in epileptic seizure detection using EEG. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101702] [Citation(s) in RCA: 91] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Karabiber Cura O, Kocaaslan Atli S, Türe HS, Akan A. Epileptic seizure classifications using empirical mode decomposition and its derivative. Biomed Eng Online 2020; 19:10. [PMID: 32059668 PMCID: PMC7023773 DOI: 10.1186/s12938-020-0754-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 02/06/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Epilepsy is one of the most common neurological disorders associated with disruption of brain activity. In the classification and detection of epileptic seizures, electroencephalography (EEG) measurements, which record the electrical activities of the brain, are frequently used. Empirical mode decomposition (EMD) and its derivative, ensemble EMD (EEMD) are recently developed methods used to decompose non-stationary and nonlinear signals such as EEG into a finite number of oscillations called intrinsic mode functions (IMFs). Our main objective in this study is to present a hybrid IMF selection method combining four different approaches (energy, correlation, power spectral distance, and statistical significance measures), and investigate the effect of selected IMFs extracted by EMD and EEMD on the classification. We have applied the proposed IMF selection approach on the classification of EEG signals recorded from epilepsy patients who are under treatment at our collaborator hospital. Multichannel EEG signals collected from epilepsy patients are decomposed into IMFs, and then IMF selection was performed. Finally, time- and spectral-domain, and nonlinear features are extracted and feature sets are created for the classification. RESULTS The maximum classification accuracies obtained using various combinations of IMFs were 94.56%, 95.63%, 96.8%, and 96.25% for SVM, KNN, naive Bayes, and logistic regression classifiers, respectively, by using EMD analysis; whereas, the EEMD approach has provided maximum classification accuracies of 96.06%, 97%, 97%, and 96.25% for SVM, KNN, naive Bayes, and logistic regression, respectively. Classification performance with the same features obtained using direct EEG signals instead of the decomposed IMFs was worse than the aforementioned 2 approaches for every combination. CONCLUSION Simulation results demonstrate that the proposed IMF selection approach affects the classification results. Also, EEMD provides a robust method for feature extraction from EEG signals in order to classify pre-seizure and seizure segments.
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Affiliation(s)
- Ozlem Karabiber Cura
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Cigli, Izmir, Turkey
| | - Sibel Kocaaslan Atli
- Department of Biophysics, Faculty of Medicine, Izmir Katip Celebi University, Cigli, Izmir, Turkey
| | - Hatice Sabiha Türe
- Department of Neurology, Faculty of Medicine, Izmir Katip Celebi University, Cigli, Izmir, Turkey
| | - Aydin Akan
- Department of Electrical and Electronics Engineering, Faculty of Engineering, Izmir University of Economics, Balcova, Izmir, Turkey.
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Zhang X, Yao L, Dong M, Liu Z, Zhang Y, Li Y. Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection. IEEE J Biomed Health Inform 2020; 24:2852-2859. [PMID: 32071011 DOI: 10.1109/jbhi.2020.2971610] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Epilepsy is a chronic neurological disorder characterized by the occurrence of spontaneous seizures, which affects about one percent of the worlds population. Most of the current seizure detection approaches strongly rely on patient history records and thus fail in the patient-independent situation of detecting the new patients. To overcome such limitation, we propose a robust and explainable epileptic seizure detection model that effectively learns from seizure states while eliminates the inter-patient noises. A complex deep neural network model is proposed to learn the pure seizure-specific representation from the raw non-invasive electroencephalography (EEG) signals through adversarial training. Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure. The proposed approach is evaluated over the Temple University Hospital EEG (TUH EEG) database. The experimental results illustrate that our model outperforms the competitive state-of-the-art baselines with low latency. Moreover, the designed attention mechanism is demonstrated ables to provide fine-grained information for pathological analysis. We propose an effective and efficient patient-independent diagnosis approach of epileptic seizure based on raw EEG signals without manually feature engineering, which is a step toward the development of large-scale deployment for real-life use.
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Jebakumari VS, Saravanan DS, Devaraj D. Seizure detection in EGG signal with novel optimization algorithm for selecting optimal thresholded offset Gaussian feature. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101708] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Ling SH, Makgawinata H, Monsivais FH, Dos Santos Goncalves Lourenco A, Lyu J, Chai R. Classification of EEG Motor Imagery Tasks Using Convolution Neural Networks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:758-761. [PMID: 31946007 DOI: 10.1109/embc.2019.8857933] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Electroencephalograph (EEG) is a highly nonlinear data and very difficult to be classified. The EEG signal is commonly used in the area of Brain-Computer Interface (BCI). The signal can be used as an operative command for directional movements for a powered wheelchair to assist people with disability in performing the daily activity.In this paper, we aim to classify Electroencephalograph EEG signals extracted from subjects which had been trained to perform four Motoric Imagery (MI) tasks for two classes. The classification will be processed via a Convolutional Neural Network (CNN) utilising all 22 electrodes based on 10-20 system placement. The EEG datasets will be transformed into scaleogram using Continuous Wavelet Transform (CWT) method.We evaluated two different types of image configuration, i.e. layered and stacked input datasets. Our procedure starts from denoising the EEG signals, employing Bump CWT from 8-32 Hz brain wave. Our CNN architecture is based on the Visual Geometry Group (VGG-16) network. Our results show that layered image dataset yields a high accuracy with an average of 68.33% for two classes classification.
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136
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Manshouri N, Maleki M, Kayikcioglu T. An EEG-based stereoscopic research of the PSD differences in pre and post 2D&3D movies watching. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101642] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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137
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EEG mobility artifact removal for ambulatory epileptic seizure prediction applications. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101638] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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138
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Raghu S, Sriraam N, Temel Y, Rao SV, Hegde AS, Kubben PL. Complexity analysis and dynamic characteristics of EEG using MODWT based entropies for identification of seizure onset. J Biomed Res 2020; 34:1-3. [PMID: 32561693 PMCID: PMC7324271 DOI: 10.7555/jbr.33.20190021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
In this paper, complexity analysis and dynamic characteristics of electroencephalogram (EEG) signal based on maximal overlap discrete wavelet transform (MODWT) has been exploited for the identification of seizure onset. Since wavelet-based studies were well suited for classification of normal and epileptic seizure EEG, we have applied MODWT which is an improved version of discrete wavelet transform (DWT). The selection of optimal wavelet sub-band and features plays a crucial role to understand the brain dynamics in epileptic patients. Therefore, we have investigated MODWT using four different wavelets, namely Haar, Coif4, Dmey, and Sym4 sub-bands until seven levels. Further, we have explored the potentials of six entropies, namely sigmoid, Shannon, wavelet, Renyi, Tsallis, and Steins unbiased risk estimator (SURE) entropies in each sub-band. The sigmoid entropy extracted from Haar wavelet in sub-band D4 showed the highest accuracy of 98.44% using support vector machine classifier for the EEG collected from Ramaiah Medical College and Hospitals (RMCH). Further, the highest accuracy of 100% and 94.51% was achieved for the University of Bonn (UBonn) and CHB-MIT databases respectively. The findings of the study showed that Haar and Dmey wavelets were found to be computationally economical and expensive respectively. Besides, in terms of dynamic characteristics, MODWT results revealed that the highest energy present in sub-bands D2, D3, and D4 and entropies in those respective sub-bands outperformed other entropies in terms of classification results for RMCH database. Similarly, using all the entropies, sub-bands D5 and D6 outperformed other sub-bands for UBonn and CHB-MIT databases respectively. In conclusion, the comparison results of MODWT outperformed DWT.
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Affiliation(s)
- Shivarudhrappa Raghu
- Department of Neurosurgery, School for Mental Health and Neuroscience of the Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht 6200 MD, The Netherlands;Center for Medical Electronics and Computing, M S Ramaiah Institute of Technology, Bengaluru 560054, India
| | - Natarajan Sriraam
- Center for Medical Electronics and Computing, M S Ramaiah Institute of Technology, Bengaluru 560054, India
| | - Yasin Temel
- Department of Neurosurgery, Maastricht University Medical Center
| | | | | | - Pieter L Kubben
- Department of Neurosurgery, Maastricht University Medical Center
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139
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Hussain L, Saeed S, Idris A, Awan IA, Shah SA, Majid A, Ahmed B, Chaudhary QA. Regression analysis for detecting epileptic seizure with different feature extracting strategies. BIOMED ENG-BIOMED TE 2019; 64:619-642. [PMID: 31145684 DOI: 10.1515/bmt-2018-0012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 01/08/2019] [Indexed: 11/15/2022]
Abstract
Due to the excitability of neurons in the brain, a neurological disorder is produced known as epilepsy. The brain activity of patients suffering from epilepsy is monitored through electroencephalography (EEG). The multivariate nature of features from time domain, frequency domain, complexity and wavelet entropy based, and the statistical features were extracted from healthy and epileptic subjects using the Bonn University database and seizure and non-seizure intervals using the CHB MIT database. The robust machine learning regression methods based on regression, support vector regression (SVR), regression tree (RT), ensemble regression, Gaussian process regression (GPR) were employed for detecting and predicting epileptic seizures. Performance was measured in terms of root mean square error (RMSE), squared error, mean square error (MSE) and mean absolute error (MAE). Moreover, detailed optimization was performed using a RT to predict the selected features from each feature category. A deeper analysis was conducted on features and tree regression methods where optimal RMSE and MSE results were obtained. The best optimal performance was obtained using the ensemble boosted regression tree (BRT) and exponential GPR with an RMSE of 0.47, an MSE (0.22), an R Square (RS) (0.25) and an MAE (0.30) using the Bonn University database and support vector machine (SVM) fine Gaussian with RMSE (0.63634), RS (0.03), MSE (0.40493) and MAE (0.31744); squared exponential GPR and rational quadratic GPR with an RMSE of 0.63841, an RS (0.03), an MSE (0.40757) and an MAE (0.3472) was obtained using the CHB MIT database. A further deeper analysis for the prediction of selected features was performed on an RT to compute the optimal feasible point, observed and estimated function values, function evaluation time, objective function evaluation time and overall elapsed time.
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Affiliation(s)
- Lal Hussain
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan, E-mail:
| | - Sharjil Saeed
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan
| | - Adnan Idris
- Department of Computer Sciences and Information Technology, The University of Poonch, Rawalakot 12350, Azad Kashmir, Pakistan
| | - Imtiaz Ahmed Awan
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan
| | - Saeed Arif Shah
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan.,College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia
| | - Abdul Majid
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan
| | - Bilal Ahmed
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan
| | - Quratul-Ain Chaudhary
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan
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140
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Sun C, Cui H, Zhou W, Nie W, Wang X, Yuan Q. Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on EasyEnsemble Learning. Int J Neural Syst 2019; 29:1950021. [DOI: 10.1142/s0129065719500217] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Imbalance data classification is a challenging task in automatic seizure detection from electroencephalogram (EEG) recordings when the durations of non-seizure periods are much longer than those of seizure activities. An imbalanced learning model is proposed in this paper to improve the identification of seizure events in long-term EEG signals. To better represent the underlying microstructure distributions of EEG signals while preserving the non-stationary nature, discrete wavelet transform (DWT) and uniform 1D-LBP feature extraction procedure are introduced. A learning framework is then designed by the ensemble of weakly trained support vector machines (SVMs). Under-sampling is employed to split the imbalanced seizure and non-seizure samples into multiple balanced subsets where each of them is utilized to train an individual SVM classifier. The weak SVMs are incorporated to build a strong classifier which emphasizes seizure samples and in the meantime analyzing the imbalanced class distribution of EEG data. Final seizure detection results are obtained in a multi-level decision fusion process by considering temporal and frequency factors. The model was validated over two long-term and one short-term public EEG databases. The model achieved a [Formula: see text]-mean of 97.14% with respect to epoch-level assessment, an event-level sensitivity of 96.67%, and a false detection rate of 0.86/h on the long-term intracranial database. An epoch-level [Formula: see text]-mean of 95.28% and event-level false detection rate of 0.81/h were yielded over the long-term scalp database. The comparisons with 14 published methods demonstrated the improved detection performance for imbalanced EEG signals and the generalizability of the proposed model.
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Affiliation(s)
- Chengfa Sun
- Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250358, P. R. China
| | - Hui Cui
- Department of Computer Science and Information Technology, La Trobe University, Bundoora, VIC 3083, Australia
| | - Weidong Zhou
- School of Microelectronics, Shandong University, Jinan 250101, P. R. China
| | - Weiwei Nie
- Shandong Provincial Qianfoshan Hospital, Shandong First Medical University, Jinan 250014, P. R. China
| | - Xiuying Wang
- School of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia
| | - 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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141
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Swami P, Bhatia M, Tripathi M, Chandra PS, Panigrahi BK, Gandhi TK. Selection of optimum frequency bands for detection of epileptiform patterns. Healthc Technol Lett 2019; 6:126-131. [PMID: 31839968 PMCID: PMC6849498 DOI: 10.1049/htl.2018.5051] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 04/15/2019] [Accepted: 04/25/2019] [Indexed: 01/03/2023] Open
Abstract
The significant research effort in the domain of epilepsy has been directed toward the development of an automated seizure detection system. In their usage of the electrophysiological recordings, most of the proposals thus far have followed the conventional practise of employing all frequency bands following signal decomposition as input features for a classifier. Although seemingly powerful, this approach may prove counterproductive since some frequency bins may not carry relevant information about seizure episodes and may, instead, add noise to the classification process thus degrading performance. A key thesis of the work described here is that the selection of frequency subsets may enhance seizure classification rates. Additionally, the authors explore whether a conservative selection of frequency bins can reduce the amount of training data needed for achieving good classification performance. They have found compelling evidence that using spectral components with <25 Hz frequency in scalp electroencephalograms can yield state-of-the-art classification accuracy while reducing training data requirements to just a tenth of those employed by current approaches.
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Affiliation(s)
- Piyush Swami
- Centre for Biomedical Engineering, Indian Institute of Technology - Delhi, New Delhi 110 016, India.,Department of Electrical Engineering, Indian Institute of Technology - Delhi, New Delhi 110 016, India
| | - Manvir Bhatia
- Department of Neurosciences, Fortis Escorts Hospital, New Delhi 110 025, India.,Neurology and Sleep Centre, New Delhi 110 016, India
| | - Manjari Tripathi
- Department of Neurology, All India Institute of Medical Sciences, New Delhi 110 029, India
| | - Poodipedi Sarat Chandra
- Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi 110 029, India
| | - Bijaya K Panigrahi
- Department of Electrical Engineering, Indian Institute of Technology - Delhi, New Delhi 110 016, India
| | - Tapan K Gandhi
- Department of Electrical Engineering, Indian Institute of Technology - Delhi, New Delhi 110 016, India
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142
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Wu L, Wang S, Wang Y, Zhang K, Bai J, Zheng J. Prediction of Hemodynamic Reactivity by Electroencephalographically Derived Pain Threshold Index in Children Undergoing General Anesthesia: A Prospective Observational Study. J Pain Res 2019; 12:3245-3255. [PMID: 31819608 PMCID: PMC6899069 DOI: 10.2147/jpr.s231596] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Accepted: 11/15/2019] [Indexed: 12/17/2022] Open
Abstract
Purpose The pain threshold index (PTI) is a novel measure of nociception based on integrated electroencephalogram parameters during general anesthesia. The wavelet index (WLI) reflects the depth of sedation. This study aims to evaluate the ability of the PTI and WLI to predict hemodynamic reactivity after tracheal intubation and skin incision in pediatric patients. Patients and methods Pediatric patients (n=134) undergoing elective general surgery or urinary surgery were analyzed. Measurements at predefined time-points during tracheal intubation and skin incision included the PTI, WLI, heart rate (HR), and mean blood pressure (MBP). Receiver-operating characteristic (ROC) curves were computed to evaluate the predictive performance of the PTI and WLI in measuring hemodynamic reactivity (an increase of more than 20% in either MBP or HR) during general anesthesia. Results Of the 134 patients evaluated, positive reactivity of HR and MBP was observed in 95 (70.9%) and 61 (45.5%) patients induced by intubation, respectively, and 19 (14.2%) and 24 (17.9%) patients induced by skin incision, respectively. Using either HR or MBP reactivity induced by intubation as a dichotomous variable, the areas under the curves (AUCs) [95% CI] of PTI and WLI were 0.81[0.73–0.87] and 0.58[0.49–0.67] with the best cutoff values of 62 and 49. The AUCs [95% CI] of PTI and WLI were 0.82[0.75–0.88] and 0.61[0.52–0.69] after skin incision. The best cutoff values of PTI and WLI were 60 and 46, respectively. Conclusion The PTI can predict hemodynamic reactivity with the best cutoff values of 62 and 60 after tracheal intubation and skin incision in pediatric patients during general anesthesia. The WLI failed in predicting hemodynamic changes.
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Affiliation(s)
- Lei Wu
- Department of Anesthesiology, Shanghai Children's Medical Center Affiliated to School of Medicine, Shanghai Jiao Tong University, Pudong, Shanghai, People's Republic of China
| | - Siyuan Wang
- Department of Anesthesiology, 3201 Hospital, Hanzhong City, Shaanxi, People's Republic of China
| | - Yanting Wang
- Department of Anesthesiology, Shanghai Children's Medical Center Affiliated to School of Medicine, Shanghai Jiao Tong University, Pudong, Shanghai, People's Republic of China
| | - Kan Zhang
- Department of Anesthesiology, Shanghai Children's Medical Center Affiliated to School of Medicine, Shanghai Jiao Tong University, Pudong, Shanghai, People's Republic of China
| | - Jie Bai
- Department of Anesthesiology, Shanghai Children's Medical Center Affiliated to School of Medicine, Shanghai Jiao Tong University, Pudong, Shanghai, People's Republic of China
| | - Jijian Zheng
- Department of Anesthesiology, Shanghai Children's Medical Center Affiliated to School of Medicine, Shanghai Jiao Tong University, Pudong, Shanghai, People's Republic of China.,Pediatric Clinical Pharmacology Laboratory, Shanghai Children's Medical Center Affiliated to School of Medicine, Shanghai Jiao Tong University, Pudong, Shanghai, People's Republic of China
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143
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Dash DP, Kolekar MH, Jha K. Multi-channel EEG based automatic epileptic seizure detection using iterative filtering decomposition and Hidden Markov Model. Comput Biol Med 2019; 116:103571. [PMID: 32001007 DOI: 10.1016/j.compbiomed.2019.103571] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 11/30/2019] [Accepted: 11/30/2019] [Indexed: 11/28/2022]
Abstract
Electroencephalography (EEG) is a non-invasive method for the analysis of neurological disorders. Epilepsy is one of the most widespread neurological disorders and often characterized by repeated seizures. This paper intends to conduct an iterative filtering based decomposition of EEG signals to improve upon the accuracy of seizure detection. The proposed approach is evaluated using All India Institute of Medical Science (AIIMS) Patna EEG database and online CHB-MIT surface EEG database. The iterative filtering decomposition technique is applied to extract sub-components from the EEG signal. The feature set obtained from each segmented intrinsic mode function consists of 2-D power spectral density and time-domain features dynamic mode decomposition power, variance, and Katz fractal dimension. The Hidden Markov Model (HMM) based probabilistic model has been designed using the above-stated features representing the seizure and non-seizure EEG events. The EEG signal is classified based on the maximum score obtained from the individual feature-based classifiers. The maximum score derived from each HMM classifier gives the final class information. The proposed decomposition of EEG signals achieved 99.60% and 99.74% accuracy in seizure detection for the online CHB-MIT surface EEG database and AIIMS Patna EEG database, respectively.
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Affiliation(s)
- Deba Prasad Dash
- Department of Electrical Engineering, Indian Institute of Technology, Patna, India.
| | | | - Kamlesh Jha
- Department of Physiology, All India Institute of Medical Sciences, Patna, India.
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144
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Kora P, Abraham A, Meenakshi K. Heart disease detection using hybrid of bacterial foraging and particle swarm optimization. EVOLVING SYSTEMS 2019. [DOI: 10.1007/s12530-019-09312-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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145
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López-Madrona VJ, Matias FS, Mirasso CR, Canals S, Pereda E. Inferring correlations associated to causal interactions in brain signals using autoregressive models. Sci Rep 2019; 9:17041. [PMID: 31745163 PMCID: PMC6863873 DOI: 10.1038/s41598-019-53453-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 10/26/2019] [Indexed: 12/22/2022] Open
Abstract
The specific connectivity of a neuronal network is reflected in the dynamics of the signals recorded on its nodes. The analysis of how the activity in one node predicts the behaviour of another gives the directionality in their relationship. However, each node is composed of many different elements which define the properties of the links. For instance, excitatory and inhibitory neuronal subtypes determine the functionality of the connection. Classic indexes such as the Granger causality (GC) quantifies these interactions, but they do not infer into the mechanism behind them. Here, we introduce an extension of the well-known GC that analyses the correlation associated to the specific influence that a transmitter node has over the receiver. This way, the G-causal link has a positive or negative effect if the predicted activity follows directly or inversely, respectively, the dynamics of the sender. The method is validated in a neuronal population model, testing the paradigm that excitatory and inhibitory neurons have a differential effect in the connectivity. Our approach correctly infers the positive or negative coupling produced by different types of neurons. Our results suggest that the proposed approach provides additional information on the characterization of G-causal connections, which is potentially relevant when it comes to understanding interactions in the brain circuits.
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Affiliation(s)
| | - Fernanda S Matias
- Cognitive Neuroimaging Unit, Commissariat à l'Energie Atomique (CEA), INSERM U992, NeuroSpin Center, 91191, Gif-sur-Yvete, France.,Instituto de Física, Universidade Federal de Alagoas, 57072-970, Maceió, Alagoas, Brazil
| | - Claudio R Mirasso
- Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC), Campus Universitat de les Illes Balears, E-07122, Palma de Mallorca, Spain
| | - Santiago Canals
- Instituto de Neurociencias, CSIC-UMH, Sant Joan d'Alacant, 03550, Spain
| | - Ernesto Pereda
- Departamento de Ingeniería Industrial, Escuela Superior de Ingeniería y Tecnología, IUNE, Universidad de La Laguna, Tenerife, 38205, Spain. .,Laboratory of Cognitive and Computational Neuroscience, CTB, UPM, Madrid, Spain.
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146
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Bigelow MD, Kouzani AZ. Neural stimulation systems for the control of refractory epilepsy: a review. J Neuroeng Rehabil 2019; 16:126. [PMID: 31665058 PMCID: PMC6820988 DOI: 10.1186/s12984-019-0605-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 10/10/2019] [Indexed: 12/18/2022] Open
Abstract
Epilepsy affects nearly 1% of the world's population. A third of epilepsy patients suffer from a kind of epilepsy that cannot be controlled by current medications. For those where surgery is not an option, neurostimulation may be the only alternative to bring relief, improve quality of life, and avoid secondary injury to these patients. Until recently, open loop neurostimulation was the only alternative for these patients. However, for those whose epilepsy is applicable, the medical approval of the responsive neural stimulation and the closed loop vagal nerve stimulation systems have been a step forward in the battle against uncontrolled epilepsy. Nonetheless, improvements can be made to the existing systems and alternative systems can be developed to further improve the quality of life of sufferers of the debilitating condition. In this paper, we first present a brief overview of epilepsy as a disease. Next, we look at the current state of biomarker research in respect to sensing and predicting epileptic seizures. Then, we present the current state of open loop neural stimulation systems. We follow this by investigating the currently approved, and some of the recent experimental, closed loop systems documented in the literature. Finally, we provide discussions on the current state of neural stimulation systems for controlling epilepsy, and directions for future studies.
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Affiliation(s)
- Matthew D Bigelow
- School of Engineering, Deakin University, Geelong, Victoria, 3216, Australia
| | - Abbas Z Kouzani
- School of Engineering, Deakin University, Geelong, Victoria, 3216, Australia.
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147
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Liu G, Zhou W, Geng M. Automatic Seizure Detection Based on S-Transform and Deep Convolutional Neural Network. Int J Neural Syst 2019; 30:1950024. [DOI: 10.1142/s0129065719500242] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Automatic seizure detection is significant for the diagnosis of epilepsy and reducing the massive workload of reviewing continuous EEGs. In this work, a novel approach, combining Stockwell transform (S-transform) with deep Convolutional Neural Networks (CNN), is proposed to detect seizure onsets in long-term intracranial EEG recordings. Primarily, raw EEG data is filtered with wavelet decomposition. Then, S-transform is used to obtain a proper time-frequency representation of each EEG segment. After that, a 15-layer deep CNN using dropout and batch normalization serves as a robust feature extractor and classifier. Finally, smoothing and collar technique are applied to the outputs of CNN to improve the detection accuracy and reduce the false detection rate (FDR). The segment-based and event-based evaluation assessments and receiver operating characteristic (ROC) curves are employed for the performance evaluation on a public EEG database containing 21 patients. A segment-based sensitivity of 97.01% and a specificity of 98.12% are yielded. For the event-based assessment, this method achieves a sensitivity of 95.45% with an FDR of 0.36/h.
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Affiliation(s)
- Guoyang Liu
- School of Microelectronics, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Weidong Zhou
- School of Microelectronics, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Minxing Geng
- School of Microelectronics, Shandong University, Jinan 250100, P. R. China
- Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
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148
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Automatic epileptic EEG detection using convolutional neural network with improvements in time-domain. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.04.028] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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149
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Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier. Comput Biol Med 2019; 110:127-143. [DOI: 10.1016/j.compbiomed.2019.05.016] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2018] [Revised: 05/20/2019] [Accepted: 05/20/2019] [Indexed: 12/16/2022]
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150
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Artola G, Isusquiza E, Errarte A, Barrenechea M, Alberdi A, Hernández-Lorca M, Solesio-Jofre E. Aging Modulates the Resting Brain after a Memory Task: A Validation Study from Multivariate Models. ENTROPY (BASEL, SWITZERLAND) 2019; 21:E411. [PMID: 33267125 PMCID: PMC7514899 DOI: 10.3390/e21040411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 04/12/2019] [Accepted: 04/16/2019] [Indexed: 06/12/2023]
Abstract
Recent work has demonstrated that aging modulates the resting brain. However, the study of these modulations after cognitive practice, resulting from a memory task, has been scarce. This work aims at examining age-related changes in the functional reorganization of the resting brain after cognitive training, namely, neuroplasticity, by means of the most innovative tools for data analysis. To this end, electroencephalographic activity was recorded in 34 young and 38 older participants. Different methods for data analyses, including frequency, time-frequency and machine learning-based prediction models were conducted. Results showed reductions in Alpha power in old compared to young adults in electrodes placed over posterior and anterior areas of the brain. Moreover, young participants showed Alpha power increases after task performance, while their older counterparts exhibited a more invariant pattern of results. These results were significant in the 140-160 s time window in electrodes placed over anterior regions of the brain. Machine learning analyses were able to accurately classify participants by age, but failed to predict whether resting state scans took place before or after the memory task. These findings greatly contribute to the development of multivariate tools for electroencephalogram (EEG) data analysis and improve our understanding of age-related changes in the functional reorganization of the resting brain.
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Affiliation(s)
- Garazi Artola
- Biomedical Engineering Department, Mondragon Unibertsitatea, 20500 Mondragón, Gipuzkoa, Spain
| | - Erik Isusquiza
- Biomedical Engineering Department, Mondragon Unibertsitatea, 20500 Mondragón, Gipuzkoa, Spain
| | - Ane Errarte
- Biomedical Engineering Department, Mondragon Unibertsitatea, 20500 Mondragón, Gipuzkoa, Spain
| | - Maitane Barrenechea
- Biomedical Engineering Department, Mondragon Unibertsitatea, 20500 Mondragón, Gipuzkoa, Spain
| | - Ane Alberdi
- Biomedical Engineering Department, Mondragon Unibertsitatea, 20500 Mondragón, Gipuzkoa, Spain
| | - María Hernández-Lorca
- Departamento de Psicología Biológica y de la salud, Facultad de Psicología, Universidad Autónoma de Madrid, 28049 Madrid, Spain
| | - Elena Solesio-Jofre
- Departamento de Psicología Biológica y de la salud, Facultad de Psicología, Universidad Autónoma de Madrid, 28049 Madrid, Spain
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