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Abedinzadeh Torghabeh F, Modaresnia Y, Moattar MH. Hybrid deep transfer learning-based early diagnosis of autism spectrum disorder using scalogram representation of electroencephalography signals. Med Biol Eng Comput 2024; 62:495-503. [PMID: 37938451 DOI: 10.1007/s11517-023-02959-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 10/28/2023] [Indexed: 11/09/2023]
Abstract
Early diagnosis of autism spectrum disorder (ASD) plays an important role in the rehabilitation of the patient. This goal necessitates higher-level pattern representation and a strong modeling approach. The proposed approach applies scalogram images of electroencephalography signals for the first purpose and a two-level deep learning architecture for better classification. Scalogram images embed both the temporal and spectral information of the signal. On the other hand, the hybrid deep learning hierarchy of convolutional neural network followed by long short-term memory models both spatial and temporal information of the scalogram image. The approach is evaluated on a dataset of 34 ASD samples and 11 normal cases in without-voice and with-voice conditions. To validate the early diagnosis hypothesis, signals from children older than 5 years are used as the training set, and signals from younger subjects are used as the validation set. The proposed method achieves excellent performance of 99.50% and 98.43% for automatically detecting ASD with and without voice, respectively. This classification performance is higher than most recent reported approaches, and the results show the effectiveness of the approach in early diagnosis of ASD and demonstrate the auditory impact on the diagnosis of autism.
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Affiliation(s)
| | - Yeganeh Modaresnia
- Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
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2
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Wolfson SS, Kirk I, Waldie K, King C. EEG Complexity Analysis of Brain States, Tasks and ASD Risk. ADVANCES IN NEUROBIOLOGY 2024; 36:733-759. [PMID: 38468061 DOI: 10.1007/978-3-031-47606-8_37] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Autism spectrum disorder is an increasingly prevalent and debilitating neurodevelopmental condition and an electroencephalogram (EEG) diagnostic challenge. Despite large amounts of electrophysiological research over many decades, an EEG biomarker for autism spectrum disorder (ASD) has not been found. We hypothesized that reductions in complex dynamical system behaviour in the human central nervous system as part of the macroscale neuronal function during cognitive processes might be detectable in whole EEG for higher-risk ASD adults. In three studies, we compared the medians of correlation dimension, largest Lyapunov exponent, Higuchi's fractal dimension, multiscale entropy, multifractal detrended fluctuation analysis and Kolmogorov complexity during resting, cognitive and social skill tasks in 20 EEG channels of 39 adults over a range of ASD risk. We found heterogeneous complexity distribution with clusters of hierarchical sequences pointing to potential cognitive processing differences, but no clear distinction based on ASD risk. We suggest that there is indication of statistically significant differences between complexity measures of brain states and tasks. Though replication of our studies is needed with a larger sample, we believe that our electrophysiological and analytic approach has potential as a biomarker for earlier ASD diagnosis.
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Affiliation(s)
- Stephen S Wolfson
- The University of Auckland School of Psychology, Auckland, Auckland, New Zealand.
| | - Ian Kirk
- The University of Auckland School of Psychology, Auckland, Auckland, New Zealand
| | - Karen Waldie
- The University of Auckland School of Psychology, Auckland, Auckland, New Zealand
| | - Chris King
- The University of Auckland School of Psychology, Auckland, Auckland, New Zealand
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3
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Shukla PK, Maheshwary P, Kundu S, Mondal D, Kumar A, Joshi S, Pareek PK. Analyzing physical activity impact based on ubiquitous nonlinear dynamics and electroencephalography data. Technol Health Care 2024; 32:1301-1312. [PMID: 37545265 DOI: 10.3233/thc-220771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
BACKGROUND Understanding complex systems is made easier with the tools provided by the theory of nonlinear dynamic systems. It provides novel ideas, algorithms, and techniques for signal processing, analysis, and classification. Presently, these ideas are being applied to the investigation of how physiological signals evolve. OBJECTIVE The study applies nonlinear dynamics theory to electroencephalogram (EEG) signals to better comprehend the range of alcoholic mental states. One of the main contributions of this paper is an algorithm for automatically distinguishing between sober and drunken EEG signals based on their salient features. METHODS The study utilized various entropy-based features, including ApEn, SampEn, Shannon and Renyi entropies, PE, TS, FE, WE, and KSE, to extract information from EEG signals. To identify the most relevant features, the study employed ranking methods like T-test, Wilcoxon, and Bhattacharyya, and trained SVM classifiers with the selected features. The Bhattacharyya ranking method was found to be the most effective in achieving high classification accuracy, sensitivity, and specificity. RESULTS Classification accuracy of 95.89%, the sensitivity of 94.43%, and specificity of 96.67% are achieved by the SVM classifier with radial basis function (RBF) for polynomial Kernel using the Bhattacharyya ranking method. CONCLUSION From the result, it is clear that the model serves as a cost-effective and accurate decision-support tool for doctors in diagnosing alcoholism and for rehabilitation centres to monitor the effectiveness of interventions aimed at mitigating or reversing brain damage caused by alcoholism.
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Affiliation(s)
- Prashant Kumar Shukla
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
| | | | - Shakti Kundu
- Directorate of Online Education, Manipal University Jaipur, Jaipur, Rajasthan, India
| | - Dipannita Mondal
- Department of Artificial Intelligence and Data Science, Dr. D.Y. Patil College of Engineering and Innovation, Talegaon Dabhade, Maharashtra, India
| | - Ankit Kumar
- Department of Computer Engineering and Applications, GLA University Mathura, Uttar Pradesh, India
| | - Shubham Joshi
- Department of Computer Science Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, India
| | - Piyush Kumar Pareek
- Department of Artificial Intelligence and Machine Learning and Head, IPR Cell Nitte Meenakshi Institute of Technology, Bengaluru, Karnataka, India
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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Ledesma-Ramírez CI, Hernández-Gloria JJ, Bojorges-Valdez E, Yanez-Suarez O, Piña-Ramírez O. Recurrence quantification analysis during a mental calculation task. CHAOS (WOODBURY, N.Y.) 2023; 33:063154. [PMID: 37368040 DOI: 10.1063/5.0147321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 05/25/2023] [Indexed: 06/28/2023]
Abstract
The identification of brain dynamical changes under different cognitive conditions with noninvasive techniques such as electroencephalography (EEG) is relevant for the understanding of their underlying neural mechanisms. The comprehension of these mechanisms has applications in the early diagnosis of neurological disorders and asynchronous brain computer interfaces. In both cases, there are no reported features that could describe intersubject and intra subject dynamics behavior accurately enough to be applied on a daily basis. The present work proposes the use of three nonlinear features (recurrence rate, determinism, and recurrence times) extracted from recurrence quantification analysis (RQA) to describe central and parietal EEG power series complexity in continuous alternating episodes of mental calculation and rest state. Our results demonstrate a consistent mean directional change of determinism, recurrence rate, and recurrence times between conditions. Increasing values of determinism and recurrence rate were present from the rest state to mental calculation, whereas recurrence times showed the opposite pattern. The analyzed features in the present study showed statistically significant changes between rest and mental calculation states in both individual and population analysis. In general, our study described mental calculation EEG power series as less complex systems in comparison to the rest state. Moreover, ANOVA showed stability of RQA features along time.
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Affiliation(s)
| | | | - Erik Bojorges-Valdez
- Engineering Studies for Innovation, Universidad Iberoamericana, 01219 Ciudad de México, Mexico
| | - Oscar Yanez-Suarez
- Neuroimage Research Lab, Universidad Autónoma Metropolitana, 09340 Ciudad de México, Mexico
| | - Omar Piña-Ramírez
- Bioinformatics and Statistical Analysis Department, Instituto Nacional de Perinatología, 11000 Ciudad de México, Mexico
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Effective Epileptic Seizure Detection by Classifying Focal and Non-focal EEG Signals using Human Learning Optimization-based Hidden Markov Model. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
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7
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Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, Yu Y, Leonov A, Liu C, Feng Z, Chuan-Peng H. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open 2023; 6:e231671. [PMID: 36877519 PMCID: PMC9989906 DOI: 10.1001/jamanetworkopen.2023.1671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Affiliation(s)
- Zhiyi Chen
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Xuerong Liu
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Qingwu Yang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Yan-Jiang Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Kuan Miao
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Zheng Gong
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Yang Yu
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Artemiy Leonov
- Department of Psychology, Clark University, Worcester, Massachusetts
| | - Chunlei Liu
- School of Psychology, Qufu Normal University, Qufu, China
| | - Zhengzhi Feng
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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8
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Selcuk Nogay H, Adeli H. Diagnostic of autism spectrum disorder based on structural brain MRI images using, grid search optimization, and convolutional neural networks. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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9
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Sheela P, Puthankattil SD. MVME-RCMFDE framework for discerning hyper-responsivity in Autism Spectrum Disorders. Comput Biol Med 2022; 149:105958. [PMID: 36007291 DOI: 10.1016/j.compbiomed.2022.105958] [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: 02/02/2022] [Revised: 07/26/2022] [Accepted: 08/06/2022] [Indexed: 12/01/2022]
Abstract
BACKGROUND Autism Spectrum Disorder (ASD), characterized by impaired sensory processing, has a wide range of clinical heterogeneity, which handicaps effective therapeutic interventions. Therefore, it is imperative to develop potential mechanisms for delineating clinically meaningful subgroups, so as to provide individualised medical treatment. In this study, an attempt is being made to differentiate the hyper-responsive subgroup from ASD by analysing the complexity pattern of Visual Evoked Potentials (VEPs), recorded from a group of 30 ASD participants, in the presence of vertical achromatic sinewave gratings at varying contrast conditions of low (5%), medium (50%) and high (90%). METHOD This study proposes a new diagnostic framework incorporating a novel signal decomposition method termed as Modified Variational Mode Extraction (MVME) and a multiscale entropy approach. MVME segments the signal into five constituent modes with less spectral overlap in lower frequencies. Refined Composite Multiscale Fluctuation-based Dispersion entropy (RCMFDE) is extracted from these constituent modes, thereby facilitating the identification of hyper-responsive subgroup in ASD. RESULTS When tested on both simulated and real VEPs, MVME displays appreciable performance in terms of root mean square error and minimal spectral overlap in the lower frequencies, in comparison with the other state-of-the-art techniques. Relative Complexity analysis with RCMFDE exhibits a rising trend in 43%-50% of ASD in modes 1, 2, 3 and 4. CONCLUSION The proposed MVME-RCMFDE approach is efficient in discriminating the hyper-responsive subgroup in ASD in multiple modes namely mode 1, 2, 3 and 4, which correspond to delta, theta, alpha and beta frequency bands of brain signals.
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Affiliation(s)
- Priyalakshmi Sheela
- Department of Electrical Engineering, National Institute of Technology, Calicut, 673601, Kerala, India
| | - Subha D Puthankattil
- Department of Electrical Engineering, National Institute of Technology, Calicut, 673601, Kerala, India.
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10
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Li S, Tang Z, Jin N, Yang Q, Liu G, Liu T, Hu J, Liu S, Wang P, Hao J, Zhang Z, Zhang X, Li J, Wang X, Li Z, Wang Y, Yang B, Ma L. Uncovering Brain Differences in Preschoolers and Young Adolescents with Autism Spectrum Disorder using Deep Learning. Int J Neural Syst 2022; 32:2250044. [DOI: 10.1142/s0129065722500447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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11
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Borri A, Cerasa A, Tonin P, Citrigno L, Porcaro C. Characterizing Fractal Genetic Variation in the Human Genome from the Hapmap Project. Int J Neural Syst 2022; 32:2250028. [PMID: 35579974 DOI: 10.1142/s0129065722500289] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Over the last decades, the exuberant development of next-generation sequencing has revolutionized gene discovery. These technologies have boosted the mapping of single nucleotide polymorphisms (SNPs) across the human genome, providing a complex universe of heterogeneity characterizing individuals worldwide. Fractal dimension (FD) measures the degree of geometric irregularity, quantifying how "complex" a self-similar natural phenomenon is. We compared two FD algorithms, box-counting dimension (BCD) and Higuchi's fractal dimension (HFD), to characterize genome-wide patterns of SNPs extracted from the HapMap data set, which includes data from 1184 healthy subjects of eleven populations. In addition, we have used cluster and classification analysis to relate the genetic distances within chromosomes based on FD similarities to the geographical distances among the 11 global populations. We found that HFD outperformed BCD at both grand average clusterization analysis by the cophenetic correlation coefficient, in which the closest value to 1 represents the most accurate clustering solution (0.981 for the HFD and 0.956 for the BCD) and classification (79.0% accuracy, 61.7% sensitivity, and 96.4% specificity for the HFD with respect to 69.1% accuracy, 43.2% sensitivity, and 94.9% specificity for the BCD) of the 11 populations present in the HapMap data set. These results support the evidence that HFD is a reliable measure helpful in representing individual variations within all chromosomes and categorizing individuals and global populations.
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Affiliation(s)
- Alessandro Borri
- CNR-IASI Biomathematics Laboratory, (BioMatLab), Rome, Italy.,Centre of Excellence for Research DEWS, University of L'Aquila, L'Aquila, Italy
| | - Antonio Cerasa
- Institute for Biomedical Research and Innovation, National Research Council, IRIB-CNR, 98164 Messina, Italy.,S. Anna Institute, 88900 Crotone, Italy.,Pharmacotechnology Documentation and Transfer Unit, Preclinical and Translational Pharmacology, Department of Pharmacy, Health Science and Nutrition, University of Calabria, 87036 Arcavacata, Italy
| | | | - Luigi Citrigno
- Institute for Biomedical Research and Innovation, National Research Council, IRIB-CNR, 87050 Mangone CS, Italy
| | - Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience, Center (PNC), University of Padova, Padova, Italy.,Institute of Cognitive Sciences and Technologies, (ISTC) - National Research Council (CNR), Rome, Italy.,Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
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12
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Ari B, Sobahi N, Alçin ÖF, Sengur A, Acharya UR. Accurate detection of autism using Douglas-Peucker algorithm, sparse coding based feature mapping and convolutional neural network techniques with EEG signals. Comput Biol Med 2022; 143:105311. [PMID: 35158117 DOI: 10.1016/j.compbiomed.2022.105311] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/07/2022] [Accepted: 02/07/2022] [Indexed: 12/26/2022]
Abstract
Autism Spectrum Disorders (ASD) is a collection of complicated neurological disorders that first show in early childhood. Electroencephalogram (EEG) signals are widely used to record the electrical activities of the brain. Manual screening is prone to human errors, tedious, and time-consuming. Hence, a novel automated method involving the Douglas-Peucker (DP) algorithm, sparse coding-based feature mapping approach, and deep convolutional neural networks (CNNs) is employed to detect ASD using EEG recordings. Initially, the DP algorithm is used for each channel to reduce the number of samples without degradation of the EEG signal. Then, the EEG rhythms are extracted by using the wavelet transform. The EEG rhythms are coded by using the sparse representation. The matching pursuit algorithm is used for sparse coding of the EEG rhythms. The sparse coded rhythms are segmented into 8 bits length and then converted to decimal numbers. An image is formed by concatenating the histograms of the decimated rhythm signals. Extreme learning machines (ELM)-based autoencoders (AE) are employed at a data augmentation step. After data augmentation, the ASD and healthy EEG signals are classified using pre-trained deep CNN models. Our proposed method yielded an accuracy of 98.88%, the sensitivity of 100% and specificity of 96.4%, and the F1-score of 99.19% in the detection of ASD automatically. Our developed model is ready to be tested with more EEG signals before its clinical application.
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Affiliation(s)
- Berna Ari
- Firat University, Technology Faculty, Electrical and Electronics Engineering Department, Elazig, Turkey
| | - Nebras Sobahi
- King Abdulaziz University, Department of Electrical and Computer Engineering, Jeddah, Saudi Arabia.
| | - Ömer F Alçin
- Malatya Turgut Ozal University, Faculty of Engineering and Natural Sciences, Department of Electrical and Electronics Engineering, Malatya, Turkey
| | - Abdulkadir Sengur
- Firat University, Technology Faculty, Electrical and Electronics Engineering Department, Elazig, Turkey
| | - U Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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13
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Abdulhay E, Alafeef M, Hadoush H, Venkataraman V, Arunkumar N. EMD-based analysis of complexity with dissociated EEG amplitude and frequency information: a data-driven robust tool -for Autism diagnosis- compared to multi-scale entropy approach. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:5031-5054. [PMID: 35430852 DOI: 10.3934/mbe.2022235] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
OBJECTIVE Autism spectrum disorder (ASD) is usually characterised by altered social skills, repetitive behaviours, and difficulties in verbal/nonverbal communication. It has been reported that electroencephalograms (EEGs) in ASD are characterised by atypical complexity. The most commonly applied method in studies of ASD EEG complexity is multiscale entropy (MSE), where the sample entropy is evaluated across several scales. However, the accuracy of MSE-based classifications between ASD and neurotypical EEG activities is poor owing to several shortcomings in scale extraction and length, the overlap between amplitude and frequency information, and sensitivity to frequency. The present study proposes a novel, nonlinear, non-stationary, adaptive, data-driven, and accurate method for the classification of ASD and neurotypical groups based on EEG complexity and entropy without the shortcomings of MSE. APPROACH The proposed method is as follows: (a) each ASD and neurotypical EEG (122 subjects × 64 channels) is decomposed using empirical mode decomposition (EMD) to obtain the intrinsic components (intrinsic mode functions). (b) The extracted components are normalised through the direct quadrature procedure. (c) The Hilbert transforms of the components are computed. (d) The analytic counterparts of components (and normalised components) are found. (e) The instantaneous frequency function of each analytic normalised component is calculated. (f) The instantaneous amplitude function of each analytic component is calculated. (g) The Shannon entropy values of the instantaneous frequency and amplitude vectors are computed. (h) The entropy values are classified using a neural network (NN). (i) The achieved accuracy is compared to that obtained with MSE-based classification. (j) The consistency of the results of entropy 3D mapping with clinical data is assessed. MAIN RESULTS The results demonstrate that the proposed method outperforms MSE (accuracy: 66.4%), with an accuracy of 93.5%. Moreover, the entropy 3D mapping results are more consistent with the available clinical data regarding brain topography in ASD. SIGNIFICANCE This study presents a more robust alternative to MSE, which can be used for accurate classification of ASD/neurotypical as well as for the examination of EEG entropy across brain zones in ASD.
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Affiliation(s)
- Enas Abdulhay
- Biomedical Engineering department, Jordan University of Science and Technology, 22110 Irbid, Jordan
| | - Maha Alafeef
- Biomedical Engineering department, Jordan University of Science and Technology, 22110 Irbid, Jordan
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Hikmat Hadoush
- Rehabilitation Sciences department, Jordan University of Science and Technology, 22110 Irbid, Jordan
| | - V Venkataraman
- Department of Mathematics, School of Arts, Science and Humanities, SASTRA Deemed University, Thanjavur, 613401, India
| | - N Arunkumar
- Biomedical Engineering department, Rathinam Technical Campus, Coimbatore, India
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14
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Hao C, Wang R, Li M, Ma C, Cai Q, Gao Z. Convolutional neural network based on recurrence plot for EEG recognition. CHAOS (WOODBURY, N.Y.) 2021; 31:123120. [PMID: 34972327 DOI: 10.1063/5.0062242] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 12/03/2021] [Indexed: 06/14/2023]
Abstract
Electroencephalogram (EEG) is a typical physiological signal. The classification of EEG signals is of great significance to human beings. Combining recurrence plot and convolutional neural network (CNN), we develop a novel method for classifying EEG signals. We select two typical EEG signals, namely, epileptic EEG and fatigue driving EEG, to verify the effectiveness of our method. We construct recurrence plots from EEG signals. Then, we build a CNN framework to classify the EEG signals under different brain states. For the classification of epileptic EEG signals, we design three different experiments to evaluate the performance of our method. The results suggest that the proposed framework can accurately distinguish the normal state and the seizure state of epilepsy. Similarly, for the classification of fatigue driving EEG signals, the method also has a good classification accuracy. In addition, we compare with the existing methods, and the results show that our method can significantly improve the detection results.
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Affiliation(s)
- Chongqing Hao
- School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, China
| | - Ruiqi Wang
- School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, China
| | - Mengyu Li
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
| | - Chao Ma
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
| | - Qing Cai
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
| | - Zhongke Gao
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
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15
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Effect of combining features generated through non-linear analysis and wavelet transform of EEG signals for the diagnosis of encephalopathy. Neurosci Lett 2021; 765:136269. [PMID: 34582974 DOI: 10.1016/j.neulet.2021.136269] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 09/19/2021] [Accepted: 09/23/2021] [Indexed: 11/20/2022]
Abstract
Electroencephalogram (EEG) signals portray hidden neuronal interactions in the brain and indicate brain dynamics. These signals are dynamic, complex, chaotic and nonlinear, the nature of which is represented with features - fractal dimensions, entropies and chaotic features. This study aims at examining the discriminative power of individual features and their combination in the diagnosis of a neuro-pathological condition called encephalopathy. Feature combination is accomplished with the help of feature selection using Gini impurity score that improves discriminative power and keeps redundancy minimal. Further, three widely used non-parametric classifiers which are known to be effective with wavelet features on EEG signals - Support Vector Machine, Random Forest, Multilayer Perceptron - are employed for disease classification. The models created by the combination of aforementioned stages are analysed and evaluated with performance scores, leading to an optimal model for automated diagnostic applications.
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16
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Liu G, Tian L, Zhou W. Patient-Independent Seizure Detection Based on Channel-Perturbation Convolutional Neural Network and Bidirectional Long Short-Term Memory. Int J Neural Syst 2021; 32:2150051. [PMID: 34781854 DOI: 10.1142/s0129065721500519] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Automatic seizure detection is of great significance for epilepsy diagnosis and alleviating the massive burden caused by manual inspection of long-term EEG. At present, most seizure detection methods are highly patient-dependent and have poor generalization performance. In this study, a novel patient-independent approach is proposed to effectively detect seizure onsets. First, the multi-channel EEG recordings are preprocessed by wavelet decomposition. Then, the Convolutional Neural Network (CNN) with proper depth works as an EEG feature extractor. Next, the obtained features are fed into a Bidirectional Long Short-Term Memory (BiLSTM) network to further capture the temporal variation characteristics. Finally, aiming to reduce the false detection rate (FDR) and improve the sensitivity, the postprocessing, including smoothing and collar, is performed on the outputs of the model. During the training stage, a novel channel perturbation technique is introduced to enhance the model generalization ability. The proposed approach is comprehensively evaluated on the CHB-MIT public scalp EEG database as well as a more challenging SH-SDU scalp EEG database we collected. Segment-based average accuracies of 97.51% and 93.70%, event-based average sensitivities of 86.51% and 89.89%, and average AUC-ROC of 90.82% and 90.75% are yielded on the CHB-MIT database and SH-SDU database, respectively.
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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
| | - Lan Tian
- 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
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17
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Noninvasive Methods for Fault Detection and Isolation in Internal Combustion Engines Based on Chaos Analysis. SENSORS 2021; 21:s21206925. [PMID: 34696138 PMCID: PMC8539936 DOI: 10.3390/s21206925] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 10/08/2021] [Accepted: 10/13/2021] [Indexed: 11/16/2022]
Abstract
The classic monitoring methods for detecting faults in automotive vehicles based on on-board diagnostics (OBD) are insufficient when diagnosing several mechanical failures. Other sensing techniques present drawbacks such as high invasiveness and limited physical range. The present work presents a fully noninvasive system for fault detection and isolation in internal combustion engines through sound signals processing. An acquisition system was developed, whose data are transmitted to a smartphone in which the signal is processed, and the user has access to the information. A study of the chaotic behavior of the vehicle was carried out, and the feasibility of using fractal dimensions as a tool to diagnose engine misfire and problems in the alternator belt was verified. An artificial neural network was used for fault classification using the fractal dimension data extracted from the sound of the engine. For comparison purposes, a strategy based on wavelet multiresolution analysis was also implemented. The proposed solution allows a diagnosis without having any contact with the vehicle, with low computational cost, without the need for installing sensors, and in real time. The system and method were validated through experimental tests, with a success rate of 99% for the faults under consideration.
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18
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Quantization synchronization of chaotic neural networks with time delay under event-triggered strategy. Cogn Neurodyn 2021; 15:897-914. [PMID: 34603550 DOI: 10.1007/s11571-021-09667-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 12/06/2020] [Accepted: 01/19/2021] [Indexed: 10/22/2022] Open
Abstract
This paper shows solicitude for the quantization synchronization of delayed chaotic master and slave neural networks under an dynamic event-triggered strategy. In virtue of a generalized Halanay-type inequality, a theoretical criterion for quasi-synchronization of master and slave neural networks is derived. Meanwhile, we can obtain an exact upper bound of synchronization error by using this criterion. Compared with output feedback controller with event triggering and quantization, the case where the controller only affected by quantization is also considered. Then, we exclude the Zeno behavior of the event-triggered controller. A sufficient criterion for the existence of the quantized output feedback controllers is also provided. A numerical example is cited to illustrate the efficiency of our theoretical criteria. In addition, some experiments of secure image communication are conducted under quasi-synchronization of master and slave neural networks.
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19
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Tawhid MNA, Siuly S, Wang H, Whittaker F, Wang K, Zhang Y. A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG. PLoS One 2021; 16:e0253094. [PMID: 34170979 PMCID: PMC8232415 DOI: 10.1371/journal.pone.0253094] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 05/27/2021] [Indexed: 12/19/2022] Open
Abstract
Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal communication, and restricted or repetitive behaviors. Currently Electroencephalography (EEG) is the most popular tool to inspect the existence of neurological disorders like autism biomarkers due to its low setup cost, high temporal resolution and wide availability. Generally, EEG recordings produce vast amount of data with dynamic behavior, which are visually analyzed by professional clinician to detect autism. It is laborious, expensive, subjective, error prone and has reliability issue. Therefor this study intends to develop an efficient diagnostic framework based on time-frequency spectrogram images of EEG signals to automatically identify ASD. In the proposed system, primarily, the raw EEG signals are pre-processed using re-referencing, filtering and normalization. Then, Short-Time Fourier Transform is used to transform the pre-processed signals into two-dimensional spectrogram images. Afterward those images are evaluated by machine learning (ML) and deep learning (DL) models, separately. In the ML process, textural features are extracted, and significant features are selected using principal component analysis, and feed them to six different ML classifiers for classification. In the DL process, three different convolutional neural network models are tested. The proposed DL based model achieves higher accuracy (99.15%) compared to the ML based model (95.25%) on an ASD EEG dataset and also outperforms existing methods. The findings of this study suggest that the DL based structure could discover important biomarkers for efficient and automatic diagnosis of ASD from EEG and may assist to develop computer-aided diagnosis system.
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Affiliation(s)
- Md. Nurul Ahad Tawhid
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Victoria, Australia
- * E-mail:
| | - Siuly Siuly
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Victoria, Australia
| | - Hua Wang
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Victoria, Australia
| | | | - Kate Wang
- School of Health and Biomedical Sciences, RMIT University, Melbourne, Victoria, Australia
| | - Yanchun Zhang
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Victoria, Australia
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20
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Abstract
The term "Autism spectrum disorder" (ASD), in vogue at present, has evolved after continual substantial developments taking place over more than a century. ASD is a heterogeneous, multi-factorial, developmental disability in which an unusual pattern of development takes place during infant and toddler years. As per DSM-5, Autism spectrum disorder is a combined phrase for a family of complex developmental disabilities inclusive of "Autistic Disorder, Pervasive Developmental Disorder not Otherwise Specified (PDD-NOS), and Asperger's Disorder". "ASD is characterized not only by persistent impairments in reciprocal social communication and social interactions, but is also manifested by restricted, repetitive patterns of behavior, interests, or activities". The classical clinical signs that exist in two major domains, viz. the 'social domain' and the 'behavioral domain' for the precise diagnosis of ASD have been tabulated and major differences between DSM-5 and DSM-4 are depicted with the help of a figure in this basic review article. A sharp rise in the incidence of ASD cases has been observed worldwide owing to various risk factors such as genetic predisposition coupled with adverse environmental conditions, gynecological interventions, etc. Two official manuals viz. the "Diagnostic and Statistical Manual of Mental Disorders" (DSM) (published by the American Psychiatric Association), and the "International Classification of Diseases" (ICD) (published by the World Health Organization) is being regularly updated to facilitate diagnosis of ASD. ICD-11 guidelines being prospectively implemented with effect from January 2022 have attracted global attention.
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21
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Improvement of brain functional connectivity in autism spectrum disorder: an exploratory study on the potential use of virtual reality. J Neural Transm (Vienna) 2021; 128:371-380. [PMID: 33677622 DOI: 10.1007/s00702-021-02321-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 02/26/2021] [Indexed: 01/11/2023]
Abstract
Patients with Autism Spectrum Disorder (ASD) need to be provided with behavioral, psychological, educational, or skill-building interventions as early as possible. Cognitive Behavior Therapy has proven useful to manage such problems. There is also growing evidence on the usefulness of Virtual Reality Therapy (VRT) in treating various functional deficits in ASD. This exploratory study is aimed at assessing the changes in cognitive functions in children with ASD, and the putative subtending neurophysiological mechanisms, following the provision of rehab training using an innovative VRT system. Twenty patients with ASD, aged 6-15 years, were provided with 24 sessions of VRT by using the pediatric module of the BTS NIRVANA System. Neuropsychological and EEG evaluations were carried out before and at the end of the training. After VRT, all patients showed a significant improvement in their cognitive-behavioral problems concerning attention processes, visuospatial cognition, and anxiety. These findings were paralleled by an evident reshape of frontoparietal connectivity in the alpha and theta frequency range. Our study suggests that VRT could be a useful and promising tool to improve ASD neurorehabilitation outcomes. This improvement is likely to occur through changes in frontoparietal network connectivity following VRT.
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22
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Murugappan M, Murugesan L, Jerritta S, Adeli H. Sudden Cardiac Arrest (SCA) Prediction Using ECG Morphological Features. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-020-04765-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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23
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Abdulhay E, Alafeef M, Hadoush H, Arunkumar N. Resting state EEG-based diagnosis of Autism via elliptic area of continuous wavelet transform complex plot. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-189176] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Autism is a developmental disorder that influences social communication skills. It is currently diagnosed only by behavioral assessment. The assessment is susceptible to the experience of the examiner as well as to the descriptive scaling standard. This paper presents a computer aided approach to discrimination between neuro-typical and autistic children. A new method- based on the computing of the elliptic area of the Continuous Wavelet Transform complex plot of resting state EEG- is presented. First, the complex values of CWT, as a function of both time and frequency, are calculated for every EEG channel. Second, the CWT complex plot is obtained by plotting the real parts of the resulted CWT values versus the related imaginary components. Third, the 95% confidence value of the elliptic area of the complex plot is computed for every channel for both autistic and healthy subjects; and the obtained values are considered as the first set of features. Fourth, three additional features are computed for every channel: the average CWT, the maximum EEG amplitude, and the maximum real part of CWT. The classification of those features is realized through artificial neural network (ANN). The obtained accuracy, sensitivity and specificity values are: 95.9%, 96.7%, and 95.1% respectively.
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Affiliation(s)
- Enas Abdulhay
- Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid, Jordan
| | - Maha Alafeef
- Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid, Jordan
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Hikmat Hadoush
- Department of Rehabilitation Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | - N. Arunkumar
- Department of Biomedical Engineering, Rathinam Technical Campus, Coimbatore, India
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24
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Performance analysis of EEG seizure detection features. Epilepsy Res 2020; 167:106483. [PMID: 33049435 DOI: 10.1016/j.eplepsyres.2020.106483] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 09/02/2020] [Accepted: 09/29/2020] [Indexed: 11/24/2022]
Abstract
Automatic detection of epileptic seizures can serve as a valuable clinical tool which involves a more objective and computationally efficient method for the analysis of EEG data in order to generate increasingly accurate and reliable results. Automatic seizure detection is also an important component of closed-loop responsive cortical stimulation systems. The goal of this study is to evaluate EEG-based features recently proposed for seizure detection to discover the optimum ones for a reliable seizure detection system. We extracted seizure detection features from intracranial EEG signals that were recorded during invasive pre-surgical epilepsy monitoring of people with drug resistant focal epilepsy at the Epilepsy Center of the University Hospital of Freiburg. Features from time, frequency and phase space domains as well as similarity/dissimilarity features were considered. The performance of each feature was investigated using the statistical test ANOVA. Performance analysis was conducted separately on the recordings from the channels within the seizure-onset zone (SOZ-in) and the recordings from the channels outside the seizure-onset zone (SOZ-out). Similarity/dissimilarity features that measure dynamic properties of the EEG signal and the evolving phenomena of the seizures could significantly separate ictal (during seizure) states from pre-ictal (before seizure) states (p < 0.01). Among them, our proposed feature, Bhattacharyya-based dissimilarity index (BBDI), successfully passed Tukey's post-hoc test as well suggesting that it can distinguish both pre-ictal and post-ictal (after seizure) periods from ictal period. BBDI was further applied to detect epileptic seizures and achieved area under the curve of the receiver-operator characteristic (ROC) equal to 0.96 and 0.94 for SOZ-in and SOZ-out channels, respectively. No significant difference (p = 0.59) was observed in the performance of features between SOZ-in recordings and SOZ-out recordings. The discriminative value of EEG seizure detection features was determined by statistical tests. As a result, the best features to be selected for a reliable seizure detection system designed for people with drug-resistant focal epilepsy were suggested, which include similarity/dissimilarity indices.
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25
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Porcaro C, Mayhew SD, Marino M, Mantini D, Bagshaw AP. Characterisation of Haemodynamic Activity in Resting State Networks by Fractal Analysis. Int J Neural Syst 2020; 30:2050061. [PMID: 33034533 DOI: 10.1142/s0129065720500616] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Intrinsic brain activity is organized into large-scale networks displaying specific structural-functional architecture, known as resting-state networks (RSNs). RSNs reflect complex neurophysiological processes and interactions, and have a central role in distinct sensory and cognitive functions, making it crucial to understand and quantify their anatomical and functional properties. Fractal dimension (FD) provides a parsimonious way of summarizing self-similarity over different spatial and temporal scales but despite its suitability for functional magnetic resonance imaging (fMRI) signal analysis its ability to characterize and investigate RSNs is poorly understood. We used FD in a large sample of healthy participants to differentiate fMRI RSNs and examine how the FD property of RSNs is linked with their functional roles. We identified two clusters of RSNs, one mainly consisting of sensory networks (C1, including auditory, sensorimotor and visual networks) and the other more related to higher cognitive (HCN) functions (C2, including dorsal default mode network and fronto-parietal networks). These clusters were defined in a completely data-driven manner using hierarchical clustering, suggesting that quantification of Blood Oxygen Level Dependent (BOLD) signal complexity with FD is able to characterize meaningful physiological and functional variability. Understanding the mechanisms underlying functional RSNs, and developing tools to study their signal properties, is essential for assessing specific brain alterations and FD could potentially be used for the early detection and treatment of neurological disorders.
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Affiliation(s)
- Camillo Porcaro
- Institute of Cognitive Sciences and Technologies (ISTC) - National Research Council (CNR) Rome, Italy.,Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK.,S. Anna Institute and Research in Advanced Neurorehabilitation (RAN), Crotone, Italy.,Department of Information Engineering - Università, Politecnica delle Marche, Ancona, Italy.,Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium
| | - Stephen D Mayhew
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Marco Marino
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Andrew P Bagshaw
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
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26
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Radhakrishnan M, Won D, Manoharan TA, Venkatachalam V, Chavan RM, Nalla HD. Investigating electroencephalography signals of autism spectrum disorder (ASD) using Higuchi Fractal Dimension. BIOMED ENG-BIOMED TE 2020; 66:/j/bmte.ahead-of-print/bmt-2019-0313/bmt-2019-0313.xml. [PMID: 32860666 DOI: 10.1515/bmt-2019-0313] [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: 11/28/2019] [Accepted: 06/15/2020] [Indexed: 11/15/2022]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a deficit of social relationships, interaction, sense of imagination, and constrained interests. Early diagnosis of ASD will aid in devising appropriate training procedures and placing those children in the normal stream. The objective of this research is to analyze the brain response for auditory/visual stimuli in Typically Developing (TD) and children with autism through electroencephalography (EEG). Brain dynamics in the EEG signal can be analyzed well with the help of nonlinear feature primitives. Recent research reveals that, application of fractal-based techniques proves to be effective to estimate of degree of nonlinearity in a signal. This research attempts to analyze the effect of brain dynamics with Higuchi Fractal Dimension (HFD). Also, the performance of the fractal based techniques depends on the selection of proper hyper-parameters involved in it. One of the key parameters involved in computation of HFD is the time interval parameter 'k'. Most of the researches arbitrarily fixes the value of 'k' in the range of all channels. This research proposes an algorithm to estimate the optimal value of the time parameter for each channel. Sub-band analysis was also carried out for the responding channels. Statistical analysis on the experimental reveals that a difference of 30% was observed between autistic and Typically Developing children.
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Affiliation(s)
- Menaka Radhakrishnan
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, 600127,India
| | - Daehan Won
- State University of New York, Binghamton, NY, USA
| | | | - Varsha Venkatachalam
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, 600127,India
| | - Renuka Mahadev Chavan
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, 600127,India
| | - Harathi Devi Nalla
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, 600127,India
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27
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Nogay HS, Adeli H. Machine learning (ML) for the diagnosis of autism spectrum disorder (ASD) using brain imaging. Rev Neurosci 2020; 31:/j/revneuro.ahead-of-print/revneuro-2020-0043/revneuro-2020-0043.xml. [PMID: 32866134 DOI: 10.1515/revneuro-2020-0043] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 07/25/2020] [Indexed: 02/24/2024]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental incurable disorder with a long diagnostic period encountered in the early years of life. If diagnosed early, the negative effects of this disease can be reduced by starting special education early. Machine learning (ML), an increasingly ubiquitous technology, can be applied for the early diagnosis of ASD. The aim of this study is to examine and provide a comprehensive state-of-the-art review of ML research for the diagnosis of ASD based on (a) structural magnetic resonance image (MRI), (b) functional MRI and (c) hybrid imaging techniques over the past decade. The accuracy of the studies with a large number of participants is in general lower than those with fewer participants leading to the conclusion that further large-scale studies are needed. An examination of the age of the participants shows that the accuracy of the automated diagnosis of ASD is higher at a younger age range. ML technology is expected to contribute significantly to the early and rapid diagnosis of ASD in the coming years and become available to clinicians in the near future. This review is aimed to facilitate that.
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Affiliation(s)
- Hidir Selcuk Nogay
- Department of Electrical and Energy, Kayseri University, Kayseri, Turkey
- The Ohio State University, Mathematical Bioscience Institute, Columbus, OH, USA
| | - Hojjat Adeli
- Departments of Biomedical Informatics and Neuroscience, The Ohio State University, Columbus, US
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28
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Ezzatdoost K, Hojjati H, Aghajan H. Decoding olfactory stimuli in EEG data using nonlinear features: A pilot study. J Neurosci Methods 2020; 341:108780. [DOI: 10.1016/j.jneumeth.2020.108780] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Revised: 05/11/2020] [Accepted: 05/11/2020] [Indexed: 11/30/2022]
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29
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Ortiz A, Martinez-Murcia FJ, Luque JL, Giménez A, Morales-Ortega R, Ortega J. Dyslexia Diagnosis by EEG Temporal and Spectral Descriptors: An Anomaly Detection Approach. Int J Neural Syst 2020; 30:2050029. [PMID: 32496139 DOI: 10.1142/s012906572050029x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Diagnosis of learning difficulties is a challenging goal. There are huge number of factors involved in the evaluation procedure that present high variance among the population with the same difficulty. Diagnosis is usually performed by scoring subjects according to results obtained in different neuropsychological (performance-based) tests specifically designed to this end. One of the most frequent disorders is developmental dyslexia (DD), a specific difficulty in the acquisition of reading skills not related to mental age or inadequate schooling. Its prevalence is estimated between 5% and 12% of the population. Traditional tests for DD diagnosis aim to measure different behavioral variables involved in the reading process. In this paper, we propose a diagnostic method not based on behavioral variables but on involuntary neurophysiological responses to different auditory stimuli. The experiments performed use electroencephalography (EEG) signals to analyze the temporal behavior and the spectral content of the signal acquired from each electrode to extract relevant (temporal and spectral) features. Moreover, the relationship of the features extracted among electrodes allows to infer a connectivity-like model showing brain areas that process auditory stimuli in a synchronized way. Then an anomaly detection system based on the reconstruction residuals of an autoencoder using these features has been proposed. Hence, classification is performed by the proposed system based on the differences in the resulting connectivity models that have demonstrated to be a useful tool for differential diagnosis of DD as well as a method to step towards gaining a better knowledge of the brain processes involved in DD. The results corroborate that nonspeech stimulus modulated at specific frequencies related to the sampling processes developed in the brain to capture rhymes, syllables and phonemes produces effects in specific frequency bands that differentiate between controls and DD subjects. The proposed method showed relatively high sensitivity above 0.6, and up to 0.9 in some of the experiments.
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Affiliation(s)
- Andrés Ortiz
- Department of Communications Engineering, University of Malaga, Campus de Teatinos s/n, 29071 Malaga, Spain.,Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, C/Periodista Daniel Saucedo Aranda s/n, 18071 Granada, Spain
| | - Francisco J Martinez-Murcia
- Department of Communications Engineering, University of Malaga, Campus de Teatinos s/n, 29071 Malaga, Spain.,Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, C/Periodista Daniel Saucedo Aranda s/n, 18071 Granada, Spain
| | - Juan L Luque
- Department of Developmental and Educational Psychology, University of Malaga, Campus de Teatinos s/n, 29071 Malaga, Spain
| | - Almudena Giménez
- Department of Basic Psychology, Faculty of Psychology, University of Malaga, Campus de Teatinos s/n, 29071 Malaga, Spain
| | - Roberto Morales-Ortega
- Department of Computer Architecture and Technology, University of Granada, Periodista Daniel Saucedo Aranda, 18071 Granada, Spain
| | - Julio Ortega
- Department of Computer Architecture and Technology, University of Granada, Periodista Daniel Saucedo Aranda, 18071 Granada, Spain
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30
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Alturki FA, AlSharabi K, Abdurraqeeb AM, Aljalal M. EEG Signal Analysis for Diagnosing Neurological Disorders Using Discrete Wavelet Transform and Intelligent Techniques. SENSORS 2020; 20:s20092505. [PMID: 32354161 PMCID: PMC7361958 DOI: 10.3390/s20092505] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 04/21/2020] [Accepted: 04/25/2020] [Indexed: 11/25/2022]
Abstract
Analysis of electroencephalogram (EEG) signals is essential because it is an efficient method to diagnose neurological brain disorders. In this work, a single system is developed to diagnose one or two neurological diseases at the same time (two-class mode and three-class mode). For this purpose, different EEG feature-extraction and classification techniques are investigated to aid in the accurate diagnosis of neurological brain disorders: epilepsy and autism spectrum disorder (ASD). Two different modes, single-channel and multi-channel, of EEG signals are analyzed for epilepsy and ASD. The independent components analysis (ICA) technique is used to remove the artifacts from EEG dataset. Then, the EEG dataset is segmented and filtered to remove noise and interference using an elliptic band-pass filter. Next, the EEG signal features are extracted from the filtered signal using a discrete wavelet transform (DWT) to decompose the filtered signal to its sub-bands delta, theta, alpha, beta and gamma. Subsequently, five statistical methods are used to extract features from the EEG sub-bands: the logarithmic band power (LBP), standard deviation, variance, kurtosis, and Shannon entropy (SE). Further, the features are fed into four different classifiers, linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbor (KNN), and artificial neural networks (ANNs), to classify the features corresponding to their classes. The combination of DWT with SE and LBP produces the highest accuracy among all the classifiers. The overall classification accuracy approaches 99.9% using SVM and 97% using ANN for the three-class single-channel and multi-channel modes, respectively.
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31
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Leming M, Górriz JM, Suckling J. Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks. Int J Neural Syst 2020; 30:2050012. [DOI: 10.1142/s0129065720500124] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Deep learning models for MRI classification face two recurring problems: they are typically limited by low sample size, and are abstracted by their own complexity (the “black box problem”). In this paper, we train a convolutional neural network (CNN) with the largest multi-source, functional MRI (fMRI) connectomic dataset ever compiled, consisting of 43,858 datapoints. We apply this model to a cross-sectional comparison of autism spectrum disorder (ASD) versus typically developing (TD) controls that has proved difficult to characterize with inferential statistics. To contextualize these findings, we additionally perform classifications of gender and task versus rest. Employing class-balancing to build a training set, we trained [Formula: see text] modified CNNs in an ensemble model to classify fMRI connectivity matrices with overall AUROCs of 0.6774, 0.7680, and 0.9222 for ASD versus TD, gender, and task versus rest, respectively. Additionally, we aim to address the black box problem in this context using two visualization methods. First, class activation maps show which functional connections of the brain our models focus on when performing classification. Second, by analyzing maximal activations of the hidden layers, we were also able to explore how the model organizes a large and mixed-center dataset, finding that it dedicates specific areas of its hidden layers to processing different covariates of data (depending on the independent variable analyzed), and other areas to mix data from different sources. Our study finds that deep learning models that distinguish ASD from TD controls focus broadly on temporal and cerebellar connections, with a particularly high focus on the right caudate nucleus and paracentral sulcus.
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Affiliation(s)
- Matthew Leming
- Department of Psychiatry, University of Cambridge, Herchel Smith Building for Brain & Mind Sciences, Forvie Site, Robinson Way, Cambridge, CB20SZ, UK
| | - Juan Manuel Górriz
- Department of Signal Theory, Networking and Communications, University of Granada, Avenida del Hospicio, E-18071 Granada, Spain
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Herchel Smith Building for Brain & Mind Sciences, Forvie Site, Robinson Way, Cambridge, CB20SZ, UK
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Olejarczyk E, Zuchowicz U, Wozniak-Kwasniewska A, Kaminski M, Szekely D, David O. The Impact of Repetitive Transcranial Magnetic Stimulation on Functional Connectivity in Major Depressive Disorder and Bipolar Disorder Evaluated by Directed Transfer Function and Indices Based on Graph Theory. Int J Neural Syst 2020; 30:2050015. [DOI: 10.1142/s012906572050015x] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
The objective of this work was to study the impact of repetitive Transcranial Magnetic Stimulation (rTMS) on the EEG connectivity evaluated by indices based on graph theory, derived from Directed Transfer Function (DTF), in patients with major depressive disorder (MDD) or with bipolar disorder (BD). The results showed the importance of beta and gamma rhythms. The indices density, degree and clustering coefficient increased in MDD responders in beta and gamma bands after rTMS. Interestingly, the density and the degree changed in theta band in both groups of nonresponders (decreased in MDD nonresponders but increased in BD nonresponders). Moreover, both indices of integration (the characteristic path length and the global efficiency) as well as the clustering coefficient increased in BD nonresponders for gamma band. In BD responders, the activity increased in the frontal lobe, mainly in the left hemisphere, while in MDD responders in the central posterior part of brain. The fronto-posterior asymmetry decreased in both groups of responders in delta and beta bands. Changes in inter-hemispheric asymmetry were found only in BD nonresponders in all bands, except gamma band. Comparison between groups showed that the degree increased in delta band independently on disease (BD, MDD). These preliminary results showed that the DTF may be a useful marker allowing for evaluation of effectiveness of the rTMS therapy as well for group differentiation between MDD and BD considering separately groups of responders and nonresponders. However, further investigation should be performed over larger groups of patients to confirmed our findings.
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Affiliation(s)
- Elzbieta Olejarczyk
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Trojdena 4 str., Warsaw 02-109, Poland
| | - Urszula Zuchowicz
- Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, Mickiewicza 30Av., Cracow 30-05, Poland
| | - Agata Wozniak-Kwasniewska
- Inserm, U1216, Grenoble, F-38000, France
- Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Maciej Kaminski
- Department of Biomedical Physics, Faculty of Physics, University of Warsaw, 5 Pasteur str., Warsaw 02-093, Poland
| | - David Szekely
- Inserm, U1216, Grenoble, F-38000, France
- Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Olivier David
- Inserm, U1216, Grenoble, F-38000, France
- Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
- Centre Hospitalier Univ. Grenoble Alpes, Service de Psychiatrie, Grenoble, F-38000, France
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33
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Kaur S, Singh S, Arun P, Kaur D, Bajaj M. Phase Space Reconstruction of EEG Signals for Classification of ADHD and Control Adults. Clin EEG Neurosci 2020; 51:102-113. [PMID: 31533446 DOI: 10.1177/1550059419876525] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Attention deficit hyperactivity disorder (ADHD) is a childhood behavioral disorder that can persist into adulthood. Electroencephalography (EEG) plays a significant role in assessing the neurophysiology of ADHD because of its ability to reveal complex brain activity. The present study proposes an EEG-based diagnosis system using the phase space reconstruction technique to classify ADHD and control adults. Electric activity is recorded for 47 ADHD and 50 control adults during the eyes-open, eyes-closed, and Continuous Performance Test (CPT) condition. Various statistical features are extracted from Euclidean distances based on phase space reconstruction of signals. The proposed system is evaluated with 2 feature selection methods (correlation-based feature selection and particle swarm optimization) and 5 machine learning methods (neural dynamic classifier, support vector machine, enhanced probabilistic neural network, k-nearest neighbor, and naive-Bayes classifier). Experimental results showed the highest testing accuracy of 93.3% under the eyes-open, 90% under the eyes-closed, and 100% under the CPT condition. This study focused on the utility of phase space reconstruction of brain signals to discriminate between ADHD and control adults.
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Affiliation(s)
- Simranjit Kaur
- Department of Computer Science and Engineering, University Institute of Engineering and Technology, Panjab University, Chandigarh, India
| | - Sukhwinder Singh
- Department of Computer Science and Engineering, University Institute of Engineering and Technology, Panjab University, Chandigarh, India
| | - Priti Arun
- Department of Psychiatry, Government Medical College and Hospital, Chandigarh, India
| | - Damanjeet Kaur
- Department of Electrical and Electronics Engineering, University Institute of Engineering and Technology, Panjab University, Chandigarh, India
| | - Manoj Bajaj
- Department of Psychiatry, Government Medical College and Hospital, Chandigarh, India
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Shi W, Li Y, Liu Z, Li J, Wang Q, Yan X, Wang G. Non-Canonical Microstate Becomes Salient in High Density EEG During Propofol-Induced Altered States of Consciousness. Int J Neural Syst 2020; 30:2050005. [PMID: 31969080 DOI: 10.1142/s0129065720500057] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Dynamically assessing the level of consciousness is still challenging during anesthesia. With the help of Electroencephalography (EEG), the human brain electric activity can be noninvasively measured at high temporal resolution. Several typical quasi-stable states are introduced to represent the oscillation of the global scalp electric field. These so-called microstates reflect spatiotemporal dynamics of coherent neural activities and capture the switch of brain states within the millisecond range. In this study, the microstates of high-density EEG were extracted and investigated during propofol-induced transition of consciousness. To analyze microstates on the frequency domain, a novel microstate-wise spectral analysis was proposed by the means of multivariate empirical mode decomposition and Hilbert–Huang transform. During the transition of consciousness, a map with a posterior central maximum denoted as microstate F appeared and became salient. The current results indicated that the coverage, occurrence, and power of microstate F significantly increased in moderate sedation. The results also demonstrated that the transition of brain state from rest to sedation was accompanied by significant increase in mean energy of all frequency bands in microstate F. Combined with studies on the possible cortical sources of microstates, the findings reveal that non-canonical microstate F is highly associated with propofol-induced altered states of consciousness. The results may also support the inference that this distinct topography can be derived from canonical microstate C (anterior-posterior orientation). Finally, this study further develops pertinent methodology and extends possible applications of the EEG microstate during propofol-induced anesthesia.
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Affiliation(s)
- Wen Shi
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
- National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong 510500, P. R. China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
- School of Biomedical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P. R. China
| | - Yamin Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
- National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong 510500, P. R. China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
- School of Biomedical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P. R. China
| | - Zhian Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
- National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong 510500, P. R. China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
| | - Jing Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
- National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong 510500, P. R. China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
- Department of Anesthesiology, Honghui Hospital, Xi’an Jiaotong University, 555 Youyi East Road, Xi’an, Shaanxi 710054, P. R. China
| | - Qiang Wang
- Department of Anesthesiology and Center for Brain Science, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi, P. R. China
| | - Xiangguo Yan
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
- National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong 510500, P. R. China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
| | - Gang Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
- National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong 510500, P. R. China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
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35
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Fu R, Wang H, Han M, Han D, Sun J. Scaling Analysis of Phase Fluctuations of Brain Networks in Dynamic Constrained Object Manipulation. Int J Neural Syst 2020; 30:2050002. [DOI: 10.1142/s0129065720500021] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this study, we investigated the dynamic properties of oscillatory activities in the scalp electro-encephalographs (EEGs) of 20 participants involved in a novel dynamic manipulating task using a physical interface and a virtual feedback. The complexity of such a task a rises from the unexpected relationship between the magnitude of the motion and the feedback. The characterization of complex patterns arising from EEG is an important problem in identifying different mental intentions. We proposed a scaling analysis of phase fluctuation in the scalp EEG to discriminate the network states related to different EEG patterns, which correspond to manipulating the task with right or left movement intention. These intentions are generated while the participant is engaged in such a complex task. The phase characterization method was used to calculate the instantaneous phase from the operational EEG. Then, functional brain networks (FBNs) of 20 subjects based on the task-related EEG were constructed by phase synchronization. The degree features representing the structures and scaling components of brain networks are sensitive to the EEG patterns with left or right motor intention. The correlation between features and mental intentions was investigated by discriminant analysis. For 20 subjects, the average accuracy of state detection is [Formula: see text], and the average mean-squared error (MSE) is [Formula: see text]. The brain state depicted by the results is related to high awareness, the phase characterization is of the effectiveness in EEG processing and FBN construction and the difference of control intentions can be explored by the phase characterization method. This finding may be relevant to understanding some neuronal mechanisms underlying the attention and some applications of closed-loop control for the safety operation of tools.
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Affiliation(s)
- Rongrong Fu
- Measurement Technology and Instrumentation Key Lab of Hebei Province, Department of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei, P. R. China
| | - Han Wang
- Measurement Technology and Instrumentation Key Lab of Hebei Province, Department of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei, P. R. China
| | - Mengmeng Han
- Measurement Technology and Instrumentation Key Lab of Hebei Province, Department of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei, P. R. China
| | - Dongying Han
- School of Vehicles and Energy, Yanshan University, Qinhuangdao, Hebei, P. R. China
| | - Jiedi Sun
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, P. R. China
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36
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Abdolzadegan D, Moattar MH, Ghoshuni M. A robust method for early diagnosis of autism spectrum disorder from EEG signals based on feature selection and DBSCAN method. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.01.008] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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37
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Jin Y, Choi J, Lee S, Kim JW, Hong Y. Pathogenetical and Neurophysiological Features of Patients with Autism Spectrum Disorder: Phenomena and Diagnoses. J Clin Med 2019; 8:E1588. [PMID: 31581672 PMCID: PMC6832208 DOI: 10.3390/jcm8101588] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 09/17/2019] [Accepted: 09/30/2019] [Indexed: 12/29/2022] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is accompanied by social deficits, repetitive and restricted interests, and altered brain development. The majority of ASD patients suffer not only from ASD itself but also from its neuropsychiatric comorbidities. Alterations in brain structure, synaptic development, and misregulation of neuroinflammation are considered risk factors for ASD and neuropsychiatric comorbidities. Electroencephalography has been developed to quantitatively explore effects of these neuronal changes of the brain in ASD. The pineal neurohormone melatonin is able to contribute to neural development. Also, this hormone has an inflammation-regulatory role and acts as a circadian key regulator to normalize sleep. These functions of melatonin may play crucial roles in the alleviation of ASD and its neuropsychiatric comorbidities. In this context, this article focuses on the presumable role of melatonin and suggests that this hormone could be a therapeutic agent for ASD and its related neuropsychiatric disorders.
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Affiliation(s)
- Yunho Jin
- Department of Rehabilitation Science, Graduate School of Inje University, Gimhae 50834, Korea.
- Ubiquitous Healthcare & Anti-aging Research Center (u-HARC), Inje University, Gimhae 50834, Korea.
- Biohealth Products Research Center (BPRC), Inje University, Gimhae 50834, Korea.
- Department of Physical Therapy, College of Healthcare Medical Science & Engineering, Inje University, Gimhae 50834, Korea.
| | - Jeonghyun Choi
- Department of Rehabilitation Science, Graduate School of Inje University, Gimhae 50834, Korea.
- Ubiquitous Healthcare & Anti-aging Research Center (u-HARC), Inje University, Gimhae 50834, Korea.
- Biohealth Products Research Center (BPRC), Inje University, Gimhae 50834, Korea.
- Department of Physical Therapy, College of Healthcare Medical Science & Engineering, Inje University, Gimhae 50834, Korea.
| | - Seunghoon Lee
- Gimhae Industry Promotion & Biomedical Foundation, Gimhae 50969, Korea.
| | - Jong Won Kim
- Department of Healthcare Information Technology, College of Bio-Nano Information Technology, Inje University, Gimhae 50834, Korea.
| | - Yonggeun Hong
- Department of Rehabilitation Science, Graduate School of Inje University, Gimhae 50834, Korea.
- Ubiquitous Healthcare & Anti-aging Research Center (u-HARC), Inje University, Gimhae 50834, Korea.
- Biohealth Products Research Center (BPRC), Inje University, Gimhae 50834, Korea.
- Department of Physical Therapy, College of Healthcare Medical Science & Engineering, Inje University, Gimhae 50834, Korea.
- Department of Medicine, Division of Hematology/Oncology, Harvard Medical School-Beth Israel Deaconess Medical Center, Boston, MA 02215, USA.
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38
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Ma Z. Reachability Analysis of Neural Masses and Seizure Control Based on Combination Convolutional Neural Network. Int J Neural Syst 2019; 30:1950023. [DOI: 10.1142/s0129065719500230] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Epileptic seizures arise from synchronous firing of multiple spatially separated neural masses; therefore, many synchrony measures are used for seizure detection and characterization. However, synchrony measures reflect only the overall interaction strength among populations of neurons but cannot reveal the coupling strengths among individual populations, which is more important for seizure control. The concepts of reachability and reachable cluster were proposed to denote the coupling strengths of a set of neural masses. Here, we describe a seizure control method based on coupling strengths using combination convolutional neural network (CCNN) modeling. The neurophysiologically based neural mass model (NMM), which can bridge signal processing and neurophysiology, was used to simulate the proposed controller. Although the adjacency matrix and reachability matrix could not be identified perfectly, the vast majority of adjacency values were identified, reaching 95.64% using the CCNN with an optimal threshold. For cases of discrete and continuous coupling strengths, the proposed controller maintained the average reachable cluster strengths at about 0.1, indicating effective seizure control.
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Affiliation(s)
- Zhen Ma
- Department of Information Engineering, Binzhou University, Binzhou 256600, P. R. China
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39
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Boroujeni YK, Rastegari AA, Khodadadi H. Diagnosis of attention deficit hyperactivity disorder using non-linear analysis of the EEG signal. IET Syst Biol 2019; 13:260-266. [PMID: 31538960 PMCID: PMC8687398 DOI: 10.1049/iet-syb.2018.5130] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2018] [Revised: 04/21/2019] [Accepted: 06/28/2019] [Indexed: 09/01/2023] Open
Abstract
Attention deficit hyperactivity disorder (ADHD) is a common behavioural disorder that may be found in 5%-8% of the children. Early diagnosis of ADHD is crucial for treating the disease and reducing its harmful effects on education, employment, relationships, and life quality. On the other hand, non-linear analysis methods are widely applied in processing the electroencephalogram (EEG) signals. It has been proved that the brain neuronal activity and its related EEG signals have chaotic behaviour. Hence, chaotic indices can be employed to classify the EEG signals. In this study, a new approach is proposed based on the combination of some non-linear features to distinguish ADHD from normal children. Lyapunov exponent, fractal dimension, correlation dimension and sample, fuzzy and approximate entropies are the non-linear extracted features. For computing, the chaotic time series of obtained EEG in the brain frontal lobe (FP1, FP2, F3, F4, and Fz) need to be analysed. Experiments on a set of EEG signal obtained from 50 ADHD and 26 normal cases yielded a sensitivity, specificity, and accuracy of 98, 92.31, and 96.05%, respectively. The obtained accuracy provides a significant improvement in comparison to the other similar studies in identifying and classifying children with ADHD.
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Affiliation(s)
- Yasaman Kiani Boroujeni
- Department of Molecular and Cell Biochemistry, Falavarjan Branch, Islamic Azad University, Isfahan, Iran
| | - Ali Asghar Rastegari
- Department of Molecular and Cell Biochemistry, Falavarjan Branch, Islamic Azad University, Isfahan, Iran
| | - Hamed Khodadadi
- Department of Electrical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran.
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40
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Wolfers T, Floris DL, Dinga R, van Rooij D, Isakoglou C, Kia SM, Zabihi M, Llera A, Chowdanayaka R, Kumar VJ, Peng H, Laidi C, Batalle D, Dimitrova R, Charman T, Loth E, Lai MC, Jones E, Baumeister S, Moessnang C, Banaschewski T, Ecker C, Dumas G, O’Muircheartaigh J, Murphy D, Buitelaar JK, Marquand AF, Beckmann CF. From pattern classification to stratification: towards conceptualizing the heterogeneity of Autism Spectrum Disorder. Neurosci Biobehav Rev 2019; 104:240-254. [DOI: 10.1016/j.neubiorev.2019.07.010] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 07/10/2019] [Accepted: 07/15/2019] [Indexed: 11/17/2022]
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41
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Abbasi H, Bennet L, Gunn AJ, Unsworth CP. Latent Phase Detection of Hypoxic-Ischemic Spike Transients in the EEG of Preterm Fetal Sheep Using Reverse Biorthogonal Wavelets & Fuzzy Classifier. Int J Neural Syst 2019; 29:1950013. [PMID: 31184228 DOI: 10.1142/s0129065719500138] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Hypoxic-ischemic (HI) studies in preterms lack reliable prognostic biomarkers for diagnostic tests of HI encephalopathy (HIE). Our group's observations from in utero fetal sheep models suggest that potential biomarkers of HIE in the form of developing HI micro-scale epileptiform transients emerge along suppressed EEG/ECoG background during a latent phase of 6-7h post-insult. However, having to observe for the whole of the latent phase disqualifies any chance of clinical intervention. A precise automatic identification of these transients can help for a well-timed diagnosis of the HIE and to stop the spread of the injury before it becomes irreversible. This paper reports fusion of Reverse-Biorthogonal Wavelets with Type-1 Fuzzy classifiers, for the accurate real-time automatic identification and quantification of high-frequency HI spike transients in the latent phase, tested over seven in utero preterm sheep. Considerable high performance of 99.78 ± 0.10% was obtained from the Rbio-Wavelet Type-1 Fuzzy classifier for automatic identification of HI spikes tested over 42h of high-resolution recordings (sampling-freq:1024Hz). Data from post-insult automatic time-localization of high-frequency HI spikes reveals a promising trend in the average rate of the HI spikes, even in the animals with shorter occlusion periods, which highlights considerable higher number of transients within the first 2h post-insult.
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Affiliation(s)
- Hamid Abbasi
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
| | - Laura Bennet
- Department of Physiology, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Alistair J Gunn
- Department of Physiology, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Charles P Unsworth
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
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42
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Ortiz A, Munilla J, Martínez-Murcia FJ, Górriz JM, Ramírez J. Empirical Functional PCA for 3D Image Feature Extraction Through Fractal Sampling. Int J Neural Syst 2019; 29:1850040. [DOI: 10.1142/s0129065718500405] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is especially important in the early diagnosis of dementia. In this work, we present a technique that allows using specific time series analysis techniques with 3D images. This is achieved by sampling the image using a fractal-based method which preserves the spatial relationship among voxels. In addition, a method called Empirical functional PCA (EfPCA) is presented, which combines Empirical Mode Decomposition (EMD) with functional PCA to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis. The devised technique has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), achieving accuracies up to 93% and 92% differential diagnosis tasks (AD versus controls and PD versus Controls, respectively). The results obtained validate the method, proving that the information retrieved by our methodology is significantly linked to the diseases.
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Affiliation(s)
- Andrés Ortiz
- Communications Engineering Department, University of Málaga, Málaga 29071, Spain
| | - Jorge Munilla
- Communications Engineering Department, University of Málaga, Málaga 29071, Spain
| | | | - Juan M. Górriz
- Department of Signal Theory, Communications and Networking, University of Granada, Granada 18060, Spain
| | - Javier Ramírez
- Department of Signal Theory, Communications and Networking, University of Granada, Granada 18060, Spain
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Nobukawa S, Yamanishi T, Nishimura H, Wada Y, Kikuchi M, Takahashi T. Atypical temporal-scale-specific fractal changes in Alzheimer's disease EEG and their relevance to cognitive decline. Cogn Neurodyn 2018; 13:1-11. [PMID: 30728867 PMCID: PMC6339858 DOI: 10.1007/s11571-018-9509-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 07/20/2018] [Accepted: 09/29/2018] [Indexed: 12/20/2022] Open
Abstract
Recent advances in nonlinear analytic methods for electroencephalography have clarified the reduced complexity of spatiotemporal dynamics in brain activity observed in Alzheimer’s disease (AD). However, there are far fewer studies exploring temporal scale dependent fractal properties in AD, despite the importance of studying the dynamics of brain activity within physiologically relevant frequency ranges. Higuchi’s fractal dimension is a widely used index for evaluating fractality in brain activity, but temporal-scale-specific characteristics are lost due to its requirement of averaging over the entire range of temporal scales. In this study, we adapted Higuchi’s fractal algorithm into a method for investigating temporal-scale-specific fractal properties. We then compared the values of the temporal-scale-specific fractal dimension between healthy control (HC) and AD patient groups. Our data indicate that relative to the HC group, the AD group demonstrated reduced fractality at both slow and fast temporal scales. Moreover, we confirmed that the fractality at fast temporal scales correlates with cognitive decline. These properties might serve as a basis for a useful approach to characterizing temporal neural dynamics in AD or other neurodegenerative disorders.
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Affiliation(s)
- Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, 2–17–1 Tsudanuma, Narashino, Chiba 275–0016 Japan
| | - Teruya Yamanishi
- Department of Management Information Science, Fukui University of Technology, 3–6–1 Gakuen, Fukui, Fukui 910–8505 Japan
| | - Haruhiko Nishimura
- Graduate School of Applied Informatics, University of Hyogo, 7–1–28 Chuo-ku, Kobe, Hyogo 650–8588 Japan
| | - Yuji Wada
- Department of Neuropsychiatry, University of Fukui, 23–3 Matsuokashimoaizuki, Eiheiji, Yoshida, Fukui, 910–1193 Japan
| | - Mitsuru Kikuchi
- Research Center for Child Mental Development, Kanazawa University, 13–1 Takaramachi, Kanazawa, Ishikawa 920–8640 Japan
| | - Tetsuya Takahashi
- Department of Neuropsychiatry, University of Fukui, 23–3 Matsuokashimoaizuki, Eiheiji, Yoshida, Fukui, 910–1193 Japan
- Research Center for Child Mental Development, Kanazawa University, 13–1 Takaramachi, Kanazawa, Ishikawa 920–8640 Japan
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Amezquita-Sanchez JP, Valtierra-Rodriguez M, Adeli H, Perez-Ramirez CA. A Novel Wavelet Transform-Homogeneity Model for Sudden Cardiac Death Prediction Using ECG Signals. J Med Syst 2018; 42:176. [PMID: 30117048 DOI: 10.1007/s10916-018-1031-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Accepted: 08/07/2018] [Indexed: 02/01/2023]
Abstract
Sudden cardiac death (SCD) is one of the main causes of death among people. A new methodology is presented for predicting the SCD based on ECG signals employing the wavelet packet transform (WPT), a signal processing technique, homogeneity index (HI), a nonlinear measurement for time series signals, and the Enhanced Probabilistic Neural Network classification algorithm. The effectiveness and usefulness of the proposed method is evaluated using a database of measured ECG data acquired from 20 SCD and 18 normal patients. The proposed methodology presents the following significant advantages: (1) compared with previous works, the proposed methodology achieves a higher accuracy using a single nonlinear feature, HI, thus requiring low computational resource for predicting an SCD onset in real-time, unlike other methodologies proposed in the literature where a large number of nonlinear features are used to predict an SCD event; (2) it is capable of predicting the risk of developing an SCD event up to 20 min prior to the onset with a high accuracy of 95.8%, superseding the prior 12 min prediction time reported recently, and (3) it uses the ECG signal directly without the need for transforming the signal to a heart rate variability signal, thus saving time in the processing.
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Affiliation(s)
- Juan P Amezquita-Sanchez
- Faculty of Engineering, Departments Biomedical and Electromechanical, ENAP-RG, Autonomous University of Queretaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, C. P, 76807, San Juan del Río, Qro., Mexico
| | - Martin Valtierra-Rodriguez
- Faculty of Engineering, Departments Biomedical and Electromechanical, ENAP-RG, Autonomous University of Queretaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, C. P, 76807, San Juan del Río, Qro., Mexico
| | - Hojjat Adeli
- Departments Biomedical Informatics, Neuroscience, and Neurology, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, 43210, USA.
| | - Carlos A Perez-Ramirez
- Faculty of Engineering, Departments Biomedical and Electromechanical, ENAP-RG, Autonomous University of Queretaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, C. P, 76807, San Juan del Río, Qro., Mexico
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Fast and Accurate Diagnosis of Autism (FADA): a novel hierarchical fuzzy system based autism detection tool. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2018; 41:757-772. [PMID: 30054869 DOI: 10.1007/s13246-018-0666-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2017] [Accepted: 07/06/2018] [Indexed: 10/28/2022]
Abstract
The main aim of this research work was to develop and validate a novel graphical user interface based hierarchical fuzzy autism detection tool named as "Fast and Accurate Diagnosis of Autism" for the diagnosis of autism disorder quickly and accurately and in addition, this tool also highlights the highly impaired area in each participant. Two groups of children had been participated in this study which includes autism group (N = 40) and normal group (N = 40). The hierarchical fuzzy expert system had been developed using IF-Then rules based on the experiences of the specialists and both the groups were tested on the designed system. It had been validated that the designed system was easily discriminating between the autistic participants and normal participants with an accuracy of 99%. Moreover, the results of the designed system were compared with Childhood Autism Rating Scale; also the tool was clearly highlighting the most impaired area in each participant. It had also been seen that the designed system has a sensitivity of 98.2% and specificity of 99.2%. It can be said that the designed tool can be used by doctors to diagnose autism along with its severity levels and to highlight the highly impaired area accurately and in no time.
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Cerquera A, Vollebregt MA, Arns M. Nonlinear Recurrent Dynamics and Long-Term Nonstationarities in EEG Alpha Cortical Activity: Implications for Choosing Adequate Segment Length in Nonlinear EEG Analyses. Clin EEG Neurosci 2018; 49:71-78. [PMID: 28805079 DOI: 10.1177/1550059417724695] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Nonlinear analysis of EEG recordings allows detection of characteristics that would probably be neglected by linear methods. This study aimed to determine a suitable epoch length for nonlinear analysis of EEG data based on its recurrence rate in EEG alpha activity (electrodes Fz, Oz, and Pz) from 28 healthy and 64 major depressive disorder subjects. Two nonlinear metrics, Lempel-Ziv complexity and scaling index, were applied in sliding windows of 20 seconds shifted every 1 second and in nonoverlapping windows of 1 minute. In addition, linear spectral analysis was carried out for comparison with the nonlinear results. The analysis with sliding windows showed that the cortical dynamics underlying alpha activity had a recurrence period of around 40 seconds in both groups. In the analysis with nonoverlapping windows, long-term nonstationarities entailed changes over time in the nonlinear dynamics that became significantly different between epochs across time, which was not detected with the linear spectral analysis. Findings suggest that epoch lengths shorter than 40 seconds neglect information in EEG nonlinear studies. In turn, linear analysis did not detect characteristics from long-term nonstationarities in EEG alpha waves of control subjects and patients with major depressive disorder patients. We recommend that application of nonlinear metrics in EEG time series, particularly of alpha activity, should be carried out with epochs around 60 seconds. In addition, this study aimed to demonstrate that long-term nonlinearities are inherent to the cortical brain dynamics regardless of the presence or absence of a mental disorder.
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Affiliation(s)
- Alexander Cerquera
- 1 School of Electronics and Biomedical Engineering, Research Group Complex Systems, Universidad Antonio Nariño, Bogota, Colombia.,2 J. Crayton Pruitt Family Department of Biomedical Engineering, Brain Mapping Lab, University of Florida, Gainesville, FL, USA
| | - Madelon A Vollebregt
- 3 Research Institute Brainclinics, Nijmegen, The Netherlands.,4 Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Cognitive Neuroscience, Nijmegen, The Netherlands
| | - Martijn Arns
- 3 Research Institute Brainclinics, Nijmegen, The Netherlands.,5 Department of Experimental Psychology, Utrecht University, Utrecht, The Netherlands
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Huang LY, She HC, Jung TP. Neural Oscillation Correlates Chemistry Decision-Making. Int J Neural Syst 2018; 28:1750031. [DOI: 10.1142/s0129065717500319] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This study explored the electroencephalography (EEG) dynamics during a chemistry-related decision-making task and further examined whether the correctness of the decision-making performance could be reflected by EEG activity. A total of 66 undergraduate students’ EEG were collected while they participated in a chemistry-related decision-making task in which they had to retrieve the relevant chemistry concepts in order to make correct decisions for each task item. The results showed that it was only in the anterior cingulate cortex (ACC) cluster that distinct patterns in EEG dynamics were displayed for the correct and incorrect responses. The logistic regression results indicated that ACC theta power from 300[Formula: see text]ms to 250[Formula: see text]ms before stimulus onset was the most informative factor for estimating the likelihood of making correct decisions in the chemistry-related decision-making task, while it was the ACC low beta power from 150[Formula: see text]ms to 250[Formula: see text]ms after stimulus onset. The results suggested that the ACC theta augmentation before the stimulus onset serves to actively maintain the relevant information for retrieval from long-term memory, while the ACC low beta augmentation after the stimulus onset may serve the function of mapping the encoded stimulus onto the relevant criteria that the given participant has held within his or her mind to guide the decision-making responses.
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Affiliation(s)
- Li-Yu Huang
- Institute of Education, National Chiao-Tung University, Hsinchu 300, Taiwan
| | - Hsiao-Ching She
- Institute of Education, National Chiao-Tung University, Hsinchu 300, Taiwan
| | - Tzyy-Ping Jung
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California, San Diego La Jolla, California 92093, USA
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Ceballos-Villegas ME, Saldaña Mena JJ, Gutierrez Lozano AL, Sepúlveda-Cañamar FJ, Huidobro N, Manjarrez E, Lomeli J. The Complexity of H-wave Amplitude Fluctuations and Their Bilateral Cross-Covariance Are Modified According to the Previous Fitness History of Young Subjects under Track Training. Front Hum Neurosci 2017; 11:530. [PMID: 29163107 PMCID: PMC5671983 DOI: 10.3389/fnhum.2017.00530] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Accepted: 10/18/2017] [Indexed: 12/28/2022] Open
Abstract
The Hoffmann reflex (H-wave) is produced by alpha-motoneuron activation in the spinal cord. A feature of this electromyography response is that it exhibits fluctuations in amplitude even during repetitive stimulation with the same intensity of current. We herein explore the hypothesis that physical training induces plastic changes in the motor system. Such changes are evaluated with the fractal dimension (FD) analysis of the H-wave amplitude-fluctuations (H-wave FD) and the cross-covariance (CCV) between the bilateral H-wave amplitudes. The aim of this study was to compare the H-wave FD as well as the CCV before and after track training in sedentary individuals and athletes. The training modality in all subjects consisted of running three times per week (for 13 weeks) in a concrete road of 5 km. Given the different physical condition of sedentary vs. athletes, the running time between sedentary and athletes was different. After training, the FD was significantly increased in sedentary individuals but significantly reduced in athletes, although there were no changes in spinal excitability in either group of subjects. Moreover, the CCV between bilateral H-waves exhibited a significant increase in athletes but not in sedentary individuals. These differential changes in the FD and CCV indicate that the plastic changes in the complexity of the H-wave amplitude fluctuations as well as the synaptic inputs to the Ia-motoneuron systems of both legs were correlated to the previous fitness history of the subjects. Furthermore, these findings demonstrate that the FD and CCV can be employed as indexes to study plastic changes in the human motor system.
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Affiliation(s)
- Maria E Ceballos-Villegas
- Sección de Posgrado e Investigación, Laboratorio de Neurofisiología Humana y Control Motor, Escuela Superior de Medicina, Instituto Politécnico Nacional, Mexico City, Mexico
| | - Juan J Saldaña Mena
- Escuela de Quiropráctica, Universidad Estatal del Valle de Ecatepec, Ecatepec de Morelos, Mexico
| | - Ana L Gutierrez Lozano
- Sección de Posgrado e Investigación, Laboratorio de Neurofisiología Humana y Control Motor, Escuela Superior de Medicina, Instituto Politécnico Nacional, Mexico City, Mexico
| | | | - Nayeli Huidobro
- Instituto de Fisiología, Benemérita Universidad Autónoma de Puebla, Puebla, Mexico
| | - Elias Manjarrez
- Instituto de Fisiología, Benemérita Universidad Autónoma de Puebla, Puebla, Mexico
| | - Joel Lomeli
- Sección de Posgrado e Investigación, Laboratorio de Neurofisiología Humana y Control Motor, Escuela Superior de Medicina, Instituto Politécnico Nacional, Mexico City, Mexico
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Sereshkeh AR, Trott R, Bricout A, Chau T. Online EEG Classification of Covert Speech for Brain–Computer Interfacing. Int J Neural Syst 2017; 27:1750033. [DOI: 10.1142/s0129065717500332] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Brain–computer interfaces (BCIs) for communication can be nonintuitive, often requiring the performance of hand motor imagery or some other conversation-irrelevant task. In this paper, electroencephalography (EEG) was used to develop two intuitive online BCIs based solely on covert speech. The goal of the first BCI was to differentiate between 10[Formula: see text]s of mental repetitions of the word “no” and an equivalent duration of unconstrained rest. The second BCI was designed to discern between 10[Formula: see text]s each of covert repetition of the words “yes” and “no”. Twelve participants used these two BCIs to answer yes or no questions. Each participant completed four sessions, comprising two offline training sessions and two online sessions, one for testing each of the BCIs. With a support vector machine and a combination of spectral and time-frequency features, an average accuracy of [Formula: see text] was reached across participants in the online classification of no versus rest, with 10 out of 12 participants surpassing the chance level (60.0% for [Formula: see text]). The online classification of yes versus no yielded an average accuracy of [Formula: see text], with eight participants exceeding the chance level. Task-specific changes in EEG beta and gamma power in language-related brain areas tended to provide discriminatory information. To our knowledge, this is the first report of online EEG classification of covert speech. Our findings support further study of covert speech as a BCI activation task, potentially leading to the development of more intuitive BCIs for communication.
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Affiliation(s)
- Alborz Rezazadeh Sereshkeh
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M4G1R8, Canada
| | - Robert Trott
- School of Computer Science, Engineering & Mathematics, Faculty of Science & Engineering, Flinders University, GPO Box 2100, Adelaide, South Australia 5001, Australia
| | - Aurélien Bricout
- Polytech Grenoble, Department of Health Information Technology, Université Joseph Fourier, 14, Place du Conseil National de la Résistance, 38400 St-Martin-d’Hères, France
| | - Tom Chau
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M4G1R8, Canada
- Polytech Grenoble, Department of Health Information Technology, Université Joseph Fourier, 14, Place du Conseil National de la Résistance, 38400 St-Martin-d’Hères, France
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Gurau O, Bosl WJ, Newton CR. How Useful Is Electroencephalography in the Diagnosis of Autism Spectrum Disorders and the Delineation of Subtypes: A Systematic Review. Front Psychiatry 2017; 8:121. [PMID: 28747892 PMCID: PMC5506073 DOI: 10.3389/fpsyt.2017.00121] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Accepted: 06/23/2017] [Indexed: 01/29/2023] Open
Abstract
Autism spectrum disorders (ASD) are thought to be associated with abnormal neural connectivity. Presently, neural connectivity is a theoretical construct that cannot be easily measured. Research in network science and time series analysis suggests that neural network structure, a marker of neural activity, can be measured with electroencephalography (EEG). EEG can be quantified by different methods of analysis to potentially detect brain abnormalities. The aim of this review is to examine evidence for the utility of three methods of EEG signal analysis in the ASD diagnosis and subtype delineation. We conducted a review of literature in which 40 studies were identified and classified according to the principal method of EEG analysis in three categories: functional connectivity analysis, spectral power analysis, and information dynamics. All studies identified significant differences between ASD patients and non-ASD subjects. However, due to high heterogeneity in the results, generalizations could not be inferred and none of the methods alone are currently useful as a new diagnostic tool. The lack of studies prevented the analysis of these methods as tools for ASD subtypes delineation. These results confirm EEG abnormalities in ASD, but as yet not sufficient to help in the diagnosis. Future research with larger samples and more robust study designs could allow for higher sensitivity and consistency in characterizing ASD, paving the way for developing new means of diagnosis.
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Affiliation(s)
- Oana Gurau
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - William J. Bosl
- School of Nursing and Health Professions, University of San Francisco, San Francisco, CA, United States
- Benioff UCSF Children’s Hospital Oakland Research Institute, Oakland, CA, United States
| | - Charles R. Newton
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- KEMRI-Wellcome Trust Research Program, Centre for Geographic Medicine Research (Coast), Kilifi, Kenya
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