1
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Wolfson EJ, Fekete T, Loewenstein Y, Shriki O. Multi-scale entropy assessment of magnetoencephalography signals in schizophrenia. Sci Rep 2024; 14:14680. [PMID: 38918430 PMCID: PMC11199523 DOI: 10.1038/s41598-024-64704-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 06/12/2024] [Indexed: 06/27/2024] Open
Abstract
Schizophrenia is a severe disruption in cognition and emotion, affecting fundamental human functions. In this study, we applied Multi-Scale Entropy analysis to resting-state Magnetoencephalography data from 54 schizophrenia patients and 98 healthy controls. This method quantifies the temporal complexity of the signal across different time scales using the concept of sample entropy. Results show significantly higher sample entropy in schizophrenia patients, primarily in central, parietal, and occipital lobes, peaking at time scales equivalent to frequencies between 15 and 24 Hz. To disentangle the contributions of the amplitude and phase components, we applied the same analysis to a phase-shuffled surrogate signal. The analysis revealed that most differences originate from the amplitude component in the δ, α, and β power bands. While the phase component had a smaller magnitude, closer examination reveals clear spatial patterns and significant differences across specific brain regions. We assessed the potential of multi-scale entropy as a schizophrenia biomarker by comparing its classification performance to conventional spectral analysis and a cognitive task (the n-back paradigm). The discriminative power of multi-scale entropy and spectral features was similar, with a slight advantage for multi-scale entropy features. The results of the n-back test were slightly below those obtained from multi-scale entropy and spectral features.
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Affiliation(s)
- E J Wolfson
- Department of Cognitive and Brain Sciences, Ben Gurion University of the Negev, 1 Ben-Gurion Blvd., Beer-Sheva, Israel
| | - T Fekete
- Department of Cognitive and Brain Sciences, Ben Gurion University of the Negev, 1 Ben-Gurion Blvd., Beer-Sheva, Israel
| | - Y Loewenstein
- The Edmond & Lily Safra Center for Brain Sciences, Department of Cognitive and Brain Sciences,The Alexander Silberman Institute of Life Sciences and The Federmann Center for the Study of Rationality, The Hebrew University, Jerusalem, Israel
| | - O Shriki
- Department of Cognitive and Brain Sciences, Ben Gurion University of the Negev, 1 Ben-Gurion Blvd., Beer-Sheva, Israel.
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2
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Sun Y, Sun J, Chen X, Wang Y, Gao X. EEG signatures of cognitive decline after mild SARS-CoV-2 infection: an age-dependent study. BMC Med 2024; 22:257. [PMID: 38902696 PMCID: PMC11188525 DOI: 10.1186/s12916-024-03481-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 06/12/2024] [Indexed: 06/22/2024] Open
Abstract
BACKGROUND Current research on the neurological impact of SARS-CoV-2 primarily focuses on the elderly or severely ill individuals. This study aims to explore the diverse neurological consequences of SARS-CoV-2 infection, with a particular focus on mildly affected children and adolescents. METHODS A cohort study was conducted to collect pre- and post-infection resting-state electroencephalogram (EEG) data from 185 participants and 181 structured questionnaires of long-term symptoms across four distinct age groups. The goal was to comprehensively evaluate the impact of SARS-CoV-2 infection on these different age demographics. The study analyzed EEG changes of SARS-CoV-2 by potential biomarkers across age groups using both spatial and temporal approaches. RESULTS Spatial analysis indicated that children and adolescents exhibit smaller changes in brain network and microstate patterns post-infection, implying a milder cognitive impact. Sequential linear analyses showed that SARS-CoV-2 infection is associated with a marked rise in low-complexity, synchronized neural activity within low-frequency EEG bands. This is evidenced by a significant increase in Hjorth activity within the theta band and Hjorth mobility in the delta band. Sequential nonlinear analysis indicated a significant reduction in the Hurst exponent across all age groups, pointing to increased chaos and complexity within the cognitive system following infection. Furthermore, linear regression analysis based on questionnaires established a significant positive relationship between the magnitude of changes in these neural indicators and the persistence of long-term symptoms post-infection. CONCLUSIONS The findings underscore the enduring neurological impacts of SARS-CoV-2 infection, marked by cognitive decline and increased EEG disarray. Although children and adolescents experienced milder effects, cognitive decline and heightened low-frequency electrical activity were evident. These observations might contribute to understanding potential anxiety, insomnia, and neurodevelopmental implications.
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Affiliation(s)
- Yike Sun
- The School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China
| | - Jingnan Sun
- The School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China
| | - Xiaogang Chen
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, 300192, China
| | - Yijun Wang
- Institute of Semiconductor, Chinese Academy of Sciences, Beijing, 100083, China
| | - Xiaorong Gao
- The School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.
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3
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Kachare P, Puri D, Sangle SB, Al-Shourbaji I, Jabbari A, Kirner R, Alameen A, Migdady H, Abualigah L. LCADNet: a novel light CNN architecture for EEG-based Alzheimer disease detection. Phys Eng Sci Med 2024:10.1007/s13246-024-01425-w. [PMID: 38862778 DOI: 10.1007/s13246-024-01425-w] [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: 11/26/2023] [Accepted: 04/10/2024] [Indexed: 06/13/2024]
Abstract
Alzheimer's disease (AD) is a progressive and incurable neurologi-cal disorder with a rising mortality rate, worsened by error-prone, time-intensive, and expensive clinical diagnosis methods. Automatic AD detection methods using hand-crafted Electroencephalogram (EEG) signal features lack accuracy and reliability. A lightweight convolution neural network for AD detection (LCADNet) is investigated to extract disease-specific features while reducing the detection time. The LCADNet uses two convolutional layers for extracting complex EEG features, two fully connected layers for selecting disease-specific features, and a softmax layer for predicting AD detection probability. A max-pooling layer interlaced between convolutional layers decreases the time-domain redundancy in the EEG signal. The efficiency of the LCADNet and four pre-trained models using transfer learning is compared using a publicly available AD detection dataset. The LCADNet shows the lowest computation complexity in terms of both the number of floating point operations and inference time and the highest classification performance across six measures. The generalization of the LCADNet is assessed by cross-testing it with two other publicly available AD detection datasets. It outperforms existing EEG-based AD detection methods with an accuracy of 98.50%. The LCADNet may be a valuable aid for neurologists and its Python implemen- tation can be found at github.com/SandeepSangle12/LCADNet.git.
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Affiliation(s)
- Pramod Kachare
- Department of Electronics and Telecommunication, Ramrao Adik Institute of Technology, D. Y. Patil Campus, Navi-Mumbai, Maharashtra, 400706, India
| | - Digambar Puri
- Department of Electronics and Telecommunication, Ramrao Adik Institute of Technology, D. Y. Patil Campus, Navi-Mumbai, Maharashtra, 400706, India
| | - Sandeep B Sangle
- Department of Electronics and Telecommunication, Ramrao Adik Institute of Technology, D. Y. Patil Campus, Navi-Mumbai, Maharashtra, 400706, India
| | - Ibrahim Al-Shourbaji
- Department of Electrical and Electronics Engineering, Jazan University, Jazan, 45142, Saudi Arabia
- Department of Computer Science, University of Hertfordshire, Hatfield, UK
| | - Abdoh Jabbari
- Department of Electrical and Electronics Engineering, Jazan University, Jazan, 45142, Saudi Arabia
| | - Raimund Kirner
- Department of Computer Science, University of Hertfordshire, Hatfield, UK
| | - Abdalla Alameen
- Department of Computer Engineering and Information, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Wadi Alddawasir, 11991, Saudi Arabia
| | - Hazem Migdady
- CSMIS Department, Oman College of Management and Technology, 320, Barka, Oman
| | - Laith Abualigah
- Jadara Research Center, Jadara University, Irbid, 21110, Jordan.
- Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan.
- MEU Research Unit, Middle East University, Amman, 11831, Jordan.
- Applied science research center, Applied science private university, Amman, 11931, Jordan.
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4
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Li Y, Yang X, Yan S, Sun Z. Complexity decline of hippocampal CA1 circuit model due to cholinergic deficiency associated with Alzheimer's disease. Cogn Neurodyn 2024; 18:1265-1283. [PMID: 38826656 PMCID: PMC11143170 DOI: 10.1007/s11571-023-09958-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 01/19/2023] [Accepted: 03/08/2023] [Indexed: 06/04/2024] Open
Abstract
A hallmark of Alzheimer's disease (AD) is cholinergic system dysfunction, directly affecting the hippocampal neurons. Previous experiments have demonstrated that reduced complexity is one significant effect of AD on electroencephalography (EEG). Motivated by these, this study explores reduced EEG complexity of cholinergic deficiency in AD by neurocomputation. We first construct a new hippocampal CA1 circuit model with cholinergic action. M-current I M and calcium-activated potassium current I AHP are newly introduced in the model to describe cholinergic input from the medial septum. Then, by enhancing I M and I AHP to mimic cholinergic deficiency, how cholinergic deficiency influences the model complexity is investigated by sample entropy (SampEn) and approximate entropy (ApEn). Numerical results show a more severe cholinergic deficit with lower model complexity. Furthermore, we conclude that the decline of SampEn and ApEn is due to the greatly diminished excitability of model neurons. These suggest that decreased neuronal excitability due to cholinergic impairment may contribute to reduced EEG complexity in AD. Subsequently, statistical analysis between simulated AD patients and normal control (NC) groups demonstrates that SampEn and auto-mutual-information (AMI) decrease rates significantly differ. Compared to NC, AD patients have a lower SampEn and a less negative AMI decline rate. These imply a low rate of new-generation information in AD brains with cholinergic deficits. Interestingly, the statistical correlation between SampEn and AMI is analyzed, and they have a large negative Pearson correlation coefficient. Thus, AMI reduction rates may be a complementary tool for complex analysis. Our modeling and complex analysis are expected to provide a deeper understanding of the reduced EEG complexity resulting from cholinergic deficiency.
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Affiliation(s)
- YeZi Li
- School of Mathematics and Statistics, Shaanxi Normal University, Xi’an, 710062 People’s Republic of China
| | - XiaoLi Yang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi’an, 710062 People’s Republic of China
| | - SiLu Yan
- School of Mathematics and Statistics, Shaanxi Normal University, Xi’an, 710062 People’s Republic of China
| | - ZhongKui Sun
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, 710072 People’s Republic of China
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5
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Miraglia F, Pappalettera C, Barbati SA, Podda MV, Grassi C, Rossini PM, Vecchio F. Brain complexity in stroke recovery after bihemispheric transcranial direct current stimulation in mice. Brain Commun 2024; 6:fcae137. [PMID: 38741663 PMCID: PMC11089417 DOI: 10.1093/braincomms/fcae137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 12/22/2023] [Accepted: 05/07/2024] [Indexed: 05/16/2024] Open
Abstract
Stroke is one of the leading causes of disability worldwide. There are many different rehabilitation approaches aimed at improving clinical outcomes for stroke survivors. One of the latest therapeutic techniques is the non-invasive brain stimulation. Among non-invasive brain stimulation, transcranial direct current stimulation has shown promising results in enhancing motor and cognitive recovery both in animal models of stroke and stroke survivors. In this framework, one of the most innovative methods is the bihemispheric transcranial direct current stimulation that simultaneously increases excitability in one hemisphere and decreases excitability in the contralateral one. As bihemispheric transcranial direct current stimulation can create a more balanced modulation of brain activity, this approach may be particularly useful in counteracting imbalanced brain activity, such as in stroke. Given these premises, the aim of the current study has been to explore the recovery after stroke in mice that underwent a bihemispheric transcranial direct current stimulation treatment, by recording their electric brain activity with local field potential and by measuring behavioural outcomes of Grip Strength test. An innovative parameter that explores the complexity of signals, namely the Entropy, recently adopted to describe brain activity in physiopathological states, was evaluated to analyse local field potential data. Results showed that stroke mice had higher values of Entropy compared to healthy mice, indicating an increase in brain complexity and signal disorder due to the stroke. Additionally, the bihemispheric transcranial direct current stimulation reduced Entropy in both healthy and stroke mice compared to sham stimulated mice, with a greater effect in stroke mice. Moreover, correlation analysis showed a negative correlation between Entropy and Grip Strength values, indicating that higher Entropy values resulted in lower Grip Strength engagement. Concluding, the current evidence suggests that the Entropy index of brain complexity characterizes stroke pathology and recovery. Together with this, bihemispheric transcranial direct current stimulation can modulate brain rhythms in animal models of stroke, providing potentially new avenues for rehabilitation in humans.
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Affiliation(s)
- Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, 00163, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, 22060, Como, Italy
| | - Chiara Pappalettera
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, 00163, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, 22060, Como, Italy
| | - Saviana Antonella Barbati
- Department of Neuroscience, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Maria Vittoria Podda
- Department of Neuroscience, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Claudio Grassi
- Department of Neuroscience, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, 00163, Rome, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, 00163, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, 22060, Como, Italy
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6
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Parameshwaran D, Thiagarajan TC. High Variability Periods in the EEG Distinguish Cognitive Brain States. Brain Sci 2023; 13:1528. [PMID: 38002488 PMCID: PMC10669877 DOI: 10.3390/brainsci13111528] [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: 08/28/2023] [Revised: 10/14/2023] [Accepted: 10/25/2023] [Indexed: 11/26/2023] Open
Abstract
OBJECTIVE To describe a novel measure of EEG signal variability that distinguishes cognitive brain states. METHOD We describe a novel characterization of amplitude variability in the EEG signal termed "High Variability Periods" or "HVPs", defined as segments when the standard deviation of a moving window is continuously higher than the quartile cutoff. We characterize the parameter space of the metric in terms of window size, overlap, and threshold to suggest ideal parameter choice and compare its performance as a discriminator of brain state to alternate single channel measures of variability such as entropy, complexity, harmonic regression fit, and spectral measures. RESULTS We show that the average HVP duration provides a substantially distinct view of the signal relative to alternate metrics of variability and, when used in combination with these metrics, significantly enhances the ability to predict whether an individual has their eyes open or closed and is performing a working memory and Raven's pattern completion task. In addition, HVPs disappear under anesthesia and do not reappear in early periods of recovery. CONCLUSIONS HVP metrics enhance the discrimination of various brain states and are fast to estimate. SIGNIFICANCE HVP metrics can provide an additional view of signal variability that has potential clinical application in the rapid discrimination of brain states.
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7
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Kim SE, Shin C, Yim J, Seo K, Ryu H, Choi H, Park J, Min BK. Resting-state electroencephalographic characteristics related to mild cognitive impairments. Front Psychiatry 2023; 14:1231861. [PMID: 37779609 PMCID: PMC10539934 DOI: 10.3389/fpsyt.2023.1231861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/28/2023] [Indexed: 10/03/2023] Open
Abstract
Alzheimer's disease (AD) causes a rapid deterioration in cognitive and physical functions, including problem-solving, memory, language, and daily activities. Mild cognitive impairment (MCI) is considered a risk factor for AD, and early diagnosis and treatment of MCI may help slow the progression of AD. Electroencephalography (EEG) analysis has become an increasingly popular tool for developing biomarkers for MCI and AD diagnosis. Compared with healthy elderly, patients with AD showed very clear differences in EEG patterns, but it is inconclusive for MCI. This study aimed to investigate the resting-state EEG features of individuals with MCI (n = 12) and cognitively healthy controls (HC) (n = 13) with their eyes closed. EEG data were analyzed using spectral power, complexity, functional connectivity, and graph analysis. The results revealed no significant difference in EEG spectral power between the HC and MCI groups. However, we observed significant changes in brain complexity and networks in individuals with MCI compared with HC. Patients with MCI exhibited lower complexity in the middle temporal lobe, lower global efficiency in theta and alpha bands, higher local efficiency in the beta band, lower nodal efficiency in the frontal theta band, and less small-world network topology compared to the HC group. These observed differences may be related to underlying neuropathological alterations associated with MCI progression. The findings highlight the potential of network analysis as a promising tool for the diagnosis of MCI.
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Affiliation(s)
- Seong-Eun Kim
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
| | - Chanwoo Shin
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
| | - Junyeop Yim
- Department of Applied Mathematics, Kongju National University, Gongju-si, Republic of Korea
| | - Kyoungwon Seo
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
| | - Hokyoung Ryu
- Graduate School of Technology and Innovation Management, Hanyang University, Seoul, Republic of Korea
| | - Hojin Choi
- Department of Neurology, College of Medicine, Hanyang University, Seoul, Republic of Korea
| | - Jinseok Park
- Department of Neurology, College of Medicine, Hanyang University, Seoul, Republic of Korea
| | - Byoung-Kyong Min
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
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8
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Dogan S, Baygin M, Tasci B, Loh HW, Barua PD, Tuncer T, Tan RS, Acharya UR. Primate brain pattern-based automated Alzheimer's disease detection model using EEG signals. Cogn Neurodyn 2023; 17:647-659. [PMID: 37265658 PMCID: PMC10229526 DOI: 10.1007/s11571-022-09859-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 07/08/2022] [Accepted: 07/18/2022] [Indexed: 11/03/2022] Open
Abstract
Electroencephalography (EEG) may detect early changes in Alzheimer's disease (AD), a debilitating progressive neurodegenerative disease. We have developed an automated AD detection model using a novel directed graph for local texture feature extraction with EEG signals. The proposed graph was created from a topological map of the macroscopic connectome, i.e., neuronal pathways linking anatomo-functional brain segments involved in visual object recognition and motor response in the primate brain. This primate brain pattern (PBP)-based model was tested on a public AD EEG signal dataset. The dataset comprised 16-channel EEG signal recordings of 12 AD patients and 11 healthy controls. While PBP could generate 448 low-level features per one-dimensional EEG signal, combining it with tunable q-factor wavelet transform created a multilevel feature extractor (which mimicked deep models) to generate 8,512 (= 448 × 19) features per signal input. Iterative neighborhood component analysis was used to choose the most discriminative features (the number of optimal features varied among the individual EEG channels) to feed to a weighted k-nearest neighbor (KNN) classifier for binary classification into AD vs. healthy using both leave-one subject-out (LOSO) and tenfold cross-validations. Iterative majority voting was used to compute subject-level general performance results from the individual channel classification outputs. Channel-wise, as well as subject-level general results demonstrated exemplary performance. In addition, the model attained 100% and 92.01% accuracy for AD vs. healthy classification using the KNN classifier with tenfold and LOSO cross-validations, respectively. Our developed multilevel PBP-based model extracted discriminative features from EEG signals and paved the way for further development of models inspired by the brain connectome.
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Affiliation(s)
- Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey
| | - Burak Tasci
- Vocational School of Technical Sciences, Firat University, Elazig, 23119 Turkey
| | - Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, 463 Clementi Road, Singapore, 599494 Singapore
| | - Prabal D. Barua
- School of Business (Information System), University of Southern Queensland, Toowoomba, QLD 4350 Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007 Australia
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore 169609, Singapore
- Duke-NUS Medical School, Singapore 169857, Singapore
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489 Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore, Singapore
- Department of Biomedical Informatics and Medical Engineering, AsiaUniversity, Taichung, Taiwan
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9
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Vicchietti ML, Ramos FM, Betting LE, Campanharo ASLO. Computational methods of EEG signals analysis for Alzheimer's disease classification. Sci Rep 2023; 13:8184. [PMID: 37210397 DOI: 10.1038/s41598-023-32664-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 03/30/2023] [Indexed: 05/22/2023] Open
Abstract
Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer's disease (AD). AD is a progressive neurological illness that causes neuron cells degeneration, resulting in cognitive impairment. While there is no cure for AD, early diagnosis is critical to improving the quality of life of affected individuals. Here, we apply six computational time-series analysis methods (wavelet coherence, fractal dimension, quadratic entropy, wavelet energy, quantile graphs and visibility graphs) to EEG records from 160 AD patients and 24 healthy controls. Results from raw and wavelet-filtered (alpha, beta, theta and delta bands) EEG signals show that some of the time-series analysis methods tested here, such as wavelet coherence and quantile graphs, can robustly discriminate between AD patients from elderly healthy subjects. They represent a promising non-invasive and low-cost approach to the AD detection in elderly patients.
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Affiliation(s)
- Mário L Vicchietti
- Department of Biodiversity and Biostatistics, Institute of Biosciences, São Paulo State University, Botucatu, 18618-689, Brazil
| | - Fernando M Ramos
- National Institute for Space Research, Earth System Science Center, São José dos Campos, 12227-010, Brazil
| | - Luiz E Betting
- Department of Neurology, Psychology and Psychiatry, Botucatu Medical School, São Paulo State University, Botucatu, 18618-687, Brazil
| | - Andriana S L O Campanharo
- Department of Biodiversity and Biostatistics, Institute of Biosciences, São Paulo State University, Botucatu, 18618-689, Brazil.
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10
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Puri DV, Nalbalwar SL, Nandgaonkar AB, Gawande JP, Wagh A. Automatic detection of Alzheimer’s disease from EEG signals using low-complexity orthogonal wavelet filter banks. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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11
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Zhang Y, Ke L, Ma X, Di Q. Impact of ground-level ozone exposure on sleep quality and electroencephalogram patterns at different time scales. ENVIRONMENTAL RESEARCH 2023; 218:115025. [PMID: 36502906 DOI: 10.1016/j.envres.2022.115025] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 11/15/2022] [Accepted: 12/07/2022] [Indexed: 06/17/2023]
Abstract
Ozone exposure is associated with various adverse health outcomes, but its impact on sleep quality is uncertain. Here we assessed the causal effect of long-term (yearly and monthly) exposure to ozone on nocturnal workday sleep time in a national representative sample from the China Family Panel Study, using a difference-in-differences approach. We further followed ninety healthy Chinese young adults four times in four seasons from September 2020 to June 2021, measured their daily sleep architecture using accelerometers, ascertained daily ozone exposure, recorded 5-min eye-closed resting-state electroencephalogram (EEG) signals at the last day of each one-week-long measurement session, and explored the effect of ozone exposure on objectively-measured sleep architecture. In the national sample, we found that every 1 interquartile range (IQR) μg/m3 increase in yearly and monthly ozone exposure was causally associated with 7.31 (p = 0.0039) and 4.19 (p = 0.040) minutes decline in nocturnal workday sleep time; the dose-response curve represented a quasi-linear pattern with no safety threshold, and plateaued at higher concentrations. In the small-scale study with objectively-measured sleep architecture, we found that every 1 IQR μg/m3 increase in the weekly ozone exposure was associated with 5.33 min decrease in night-time total sleep time (p = 0.031), 1.63 percentage points decrease in sleep efficiency (p < 0.001), 1.99 min increase in sleep latency (p = 0.0070), and 5.34 min increase in wake after sleep onset time (p = 0.0016) in a quasi-linear pattern. Notably, we found the accumulating trend of ozone exposure on sleep quality during both the short-term and long-term periods. We also found that short-term ozone exposure was associated with altered EEG patterns, mediated by sleep quality. This study indicates that long-term and short-term ozone exposures have negative and accumulating impacts on sleep quality and might impair brain functioning. More hidden health burdens of ozone are worth exploring.
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Affiliation(s)
- Yao Zhang
- Soochow College, Soochow University, Suzhou, 215006, China; Division of Sports Science & Physical Education, Tsinghua University, Beijing, 100084, China.
| | - Limei Ke
- School of Medicine, Tsinghua University, Beijing, 100084, China.
| | - Xindong Ma
- Division of Sports Science & Physical Education, Tsinghua University, Beijing, 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, China.
| | - Qian Di
- Vanke School of Public Health, Tsinghua University, Beijing, 100084, China; Institute for Healthy China, Tsinghua University, Beijing, 100084, China.
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12
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Arjaria SK, Rathore AS, Bisen D, Bhattacharyya S. Performances of Machine Learning Models for Diagnosis of Alzheimer's Disease. ANNALS OF DATA SCIENCE 2022; 11:1-29. [PMID: 38625305 PMCID: PMC9574175 DOI: 10.1007/s40745-022-00452-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 04/18/2022] [Accepted: 05/07/2022] [Indexed: 11/29/2022]
Abstract
In recent times, various machine learning approaches have been widely employed for effective diagnosis and prediction of diseases like cancer, thyroid, Covid-19, etc. Likewise, Alzheimer's (AD) is also one progressive malady that destroys memory and cognitive function over time. Unfortunately, there are no dedicated AI-based solutions for diagnoses of AD to go hand in hand with medical diagnosis, even though multiple factors contribute to the diagnosis, making AI a very viable supplementary diagnostic solution. This paper reports an endeavor to apply various machine learning algorithms like SGD, k-Nearest Neighbors, Logistic Regression, Decision tree, Random Forest, AdaBoost, Neural Network, SVM, and Naïve Bayes on the dataset of affected victims to diagnose Alzheimer's disease. Longitudinal collections of subjects from OASIS dataset have been used for prediction. Moreover, some feature selection and dimension reduction methods like Information Gain, Information Gain Ratio, Gini index, Chi-Squared, and PCA are applied to rank different factors and identify the optimum number of factors from the dataset for disease diagnosis. Furthermore, performance is evaluated of each classifier in terms of ROC-AUC, accuracy, F1 score, recall, and precision as well as included comparative analysis between algorithms. Our study suggests that approximately 90% classification accuracy is observed under top-rated four features CDR, SES, nWBV, and EDUC.
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Affiliation(s)
| | | | - Dhananjay Bisen
- Department of Information Technology, Madhav Institute of Technology and Science, Gwalior, India
| | - Sanjib Bhattacharyya
- Department of Pharmaceutical Science and Chinese Traditional Medicine, Southwest University, Chongqing, China
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13
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Hadra MG, Omidvarnia A, Mesbah M. Temporal complexity of EEG encodes human alertness. Physiol Meas 2022; 43. [PMID: 36063816 DOI: 10.1088/1361-6579/ac8f80] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 09/05/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Automatic human alertness monitoring has recently become an important research topic with important applications in many areas such as the detection of drivers' fatigue, monitoring of monotonous tasks that require a high level of alertness such as traffic control and nuclear power plant monitoring, and sleep staging. In this study, we propose that balanced dynamics of Electroencephalography (EEG) (so called EEG temporal complexity) is a potentially useful feature for identifying human alertness states. Recently, a new signal entropy measure, called Range Entropy (RangeEn), was proposed to overcome some limitations of two of the most widely used entropy measures, namely Approximate Entropy (ApEn) and Sample Entropy (SampEn), and showed its relevance for the study of time domain EEG complexity. In this paper, we investigated whether the RangeEn holds discriminating information associated with human alertness states, namely Awake, Drowsy, and Sleep and compare its performance against those of SampEn and ApEn. APPROACH We used EEG data from 60 healthy subjects of both sexes and different ages acquired during whole night sleeps. Using a 30-second sliding window, we computed the three entropy measures of EEG and performed statistical analyses to evaluate the ability of these entropy measures to discriminate among the different human alertness states. MAIN RESULTS Although the three entropy measures contained useful information about human alertness, RangeEn showed a higher discriminative capability compared to ApEn and SampEn especially when using EEG within the Beta frequency band. SIGNIFICANCE Our findings highlight the EEG temporal complexity evolution through the human alertness states. This relationship can potentially be exploited for the development of automatic human alertness monitoring systems and diagnostic tools for different neurological and sleep disorders, including insomnia.
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Affiliation(s)
| | - Amir Omidvarnia
- Center for Neuroprosthetics, EPFL Institute of Bioengineering, Des Mines 9, 1202, Geneva, Lausanne, VD, 1015, SWITZERLAND
| | - Mostefa Mesbah
- Electrical and Computer Engineering, Sultan Qaboos University, College of Engineering, PO Box 33 PC 123, Al-Khoud, Muscat, Muscat, 123, OMAN
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14
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Zhou J, Benoit M, Sharoar MG. Recent advances in pre-clinical diagnosis of Alzheimer's disease. Metab Brain Dis 2022; 37:1703-1725. [PMID: 33900524 DOI: 10.1007/s11011-021-00733-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Accepted: 04/05/2021] [Indexed: 11/26/2022]
Abstract
Alzheimer's disease (AD) is the most common dementia with currently no known cures or disease modifying treatments (DMTs), despite much time and effort from the field. Diagnosis and intervention of AD during the early pre-symptomatic phase of the disease is thought to be a more effective strategy. Therefore, the detection of biomarkers has emerged as a critical tool for monitoring the effect of new AD therapies, as well as identifying patients most likely to respond to treatment. The establishment of the amyloid/tau/neurodegeneration (A/T/N) framework in 2018 has codified the contexts of use of AD biomarkers in neuroimaging and bodily fluids for research and diagnostic purposes. Furthermore, a renewed drive for novel AD biomarkers and innovative methods of detection has emerged with the goals of adding additional insight to disease progression and discovery of new therapeutic targets. The use of biomarkers has accelerated the development of AD drugs and will bring new therapies to patients in need. This review highlights recent methods utilized to diagnose antemortem AD.
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Affiliation(s)
- John Zhou
- Department of Neuroscience, University of Connecticut Health, Farmington, CT, 06030, USA
- Molecular Medicine Program, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, 44195, USA
| | - Marc Benoit
- Department of Neuroscience, University of Connecticut Health, Farmington, CT, 06030, USA
| | - Md Golam Sharoar
- Department of Neuroscience, University of Connecticut Health, Farmington, CT, 06030, USA.
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15
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Li X, Liu Y, Kang J, Sun Y, Xu Y, Yuan Y, Han Y, Xie P. Identifying Amnestic Mild Cognitive Impairment With Convolutional Neural Network Adapted to the Spectral Entropy Heat Map of the Electroencephalogram. Front Hum Neurosci 2022; 16:924222. [PMID: 35874151 PMCID: PMC9298556 DOI: 10.3389/fnhum.2022.924222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
Mild cognitive impairment (MCI) is a preclinical stage of Alzheimer's disease (AD), and early diagnosis and intervention may delay its deterioration. However, the electroencephalogram (EEG) differences between patients with amnestic mild cognitive impairment (aMCI) and healthy controls (HC) subjects are not as significant compared to those with AD. This study addresses this situation by proposing a computer-aided diagnostic method that also aims to improve model performance and assess the sensitive areas of the subject's brain. The EEG data of 46 subjects (20HC/26aMCI) were enhanced with windowed moving segmentation and transformed from 1D temporal data to 2D spectral entropy images to measure the efficient information in the time-frequency domain from the point of view of information entropy; A novel convolution module is devised, which considerably reduces the number of model learning parameters and saves computing resources on the premise of ensuring diagnostic performance; One more thing, the cognitive diagnostic contribution of the corresponding channels in each brain region was measured by the weight coefficient of the input and convolution unit. Our results showed that when the segmental window overlap rate was increased from 0 to 75%, the corresponding generalization accuracy increased from 91.673 ± 0.9578% to 94.642 ± 0.4035%; Approximately 35% reduction in model learnable parameters by optimizing the network structure while maintaining accuracy; The top four channels were FP1, F7, T5, and F4, corresponding to the frontal and temporal lobes, in descending order of the mean value of the weight coefficients. This paper proposes a novel method based on spectral entropy image and convolutional neural network (CNN), which provides a new perspective for the identifying of aMCI based on EEG.
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Affiliation(s)
- Xin Li
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China
| | - Yi Liu
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China
| | - Jiannan Kang
- College of Electronic and Information Engineering, Hebei University, Baoding, China
| | - Yu Sun
- China-Japan Friendship Hospital, Beijing, China
| | - Yonghong Xu
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China
| | - Yi Yuan
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Ping Xie
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China
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16
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Zanin M, Martínez JH. Analyzing international events through the lens of statistical physics: The case of Ukraine. CHAOS (WOODBURY, N.Y.) 2022; 32:051103. [PMID: 35649977 DOI: 10.1063/5.0091628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 04/08/2022] [Indexed: 06/15/2023]
Abstract
During the last few years, statistical physics has received increasing attention as a framework for the analysis of real complex systems; yet, this is less clear in the case of international political events, partly due to the complexity in securing relevant quantitative data on them. Here, we analyze a detailed dataset of violent events that took place in Ukraine since January 2021 and analyze their temporal and spatial correlations through entropy and complexity metrics and functional networks. Results depict a complex scenario with events appearing in a non-random fashion but with eastern-most regions functionally disconnected from the remainder of the country-something opposing the widespread "two Ukraines" view. We further draw some lessons and venues for future analyses.
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Affiliation(s)
- M Zanin
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122 Palma de Mallorca, Spain
| | - J H Martínez
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122 Palma de Mallorca, Spain
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17
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Zolezzi DM, Alonso-Valerdi LM, Ibarra-Zarate DI. Chronic neuropathic pain is more than a perception: Systems and methods for an integral characterization. Neurosci Biobehav Rev 2022; 136:104599. [PMID: 35271915 DOI: 10.1016/j.neubiorev.2022.104599] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 03/01/2022] [Accepted: 03/02/2022] [Indexed: 10/18/2022]
Abstract
The management of chronic neuropathic pain remains a challenge, because pain is subjective, and measuring it objectively is usually out of question. However, neuropathic pain is also a signal provided by maladaptive neuronal activity. Thus, the integral management of chronic neuropathic pain should not only rely on the subjective perception of the patient, but also on objective data that measures the evolution of neuronal activity. We will discuss different objective and subjective methods for the characterization of neuropathic pain. Additionally, the gaps and proposals for an integral management of chronic neuropathic pain will also be discussed. The current management that relies mostly on subjective measures has not been sufficient, therefore, this has hindered advances in pain management and clinical trials. If an integral characterization is achieved, clinical management and stratification for clinical trials could be based on both questionnaires and neuronal activity. Appropriate characterization may lead to an increased effectiveness for new therapies, and a better quality of life for neuropathic pain sufferers.
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Affiliation(s)
- Daniela M Zolezzi
- Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, Monterrey 64849, Nuevo León, México; Center for Neuroplasticity and Pain, Department of Health Science and Technology, Aalborg University, Aalborg 9220, Denmark.
| | | | - David I Ibarra-Zarate
- Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, Puebla 72453, Puebla, México
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18
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Yan H, Wu H, Chen Y, Yang Y, Xu M, Zeng W, Zhang J, Chang C, Wang N. Dynamical Complexity Fingerprints of Occupation-Dependent Brain Functional Networks in Professional Seafarers. Front Neurosci 2022; 16:830808. [PMID: 35368265 PMCID: PMC8973415 DOI: 10.3389/fnins.2022.830808] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 01/24/2022] [Indexed: 12/24/2022] Open
Abstract
The complexity derived from resting-state functional magnetic resonance imaging (rs-fMRI) data has been applied for exploring cognitive states and occupational neuroplasticity. However, there is little information about the influence of occupational factors on dynamic complexity and topological properties of the connectivity networks. In this paper, we proposed a novel dynamical brain complexity analysis (DBCA) framework to explore the changes in dynamical complexity of brain activity at the voxel level and complexity topology for professional seafarers caused by long-term working experience. The proposed DBCA is made up of dynamical brain entropy mapping analysis and complex network analysis based on brain entropy sequences, which generate the dynamical complexity of local brain areas and the topological complexity across brain areas, respectively. First, the transient complexity of voxel-wise brain map was calculated; compared with non-seafarers, seafarers showed decreased dynamic entropy values in the cerebellum and increased values in the left fusiform gyrus (BA20). Further, the complex network analysis based on brain entropy sequences revealed small-worldness in terms of topological complexity in both seafarers and non-seafarers, indicating that it is an inherent attribute of human the brain. In addition, seafarers showed a higher average path length and lower average clustering coefficient than non-seafarers, suggesting that the information processing ability is reduced in seafarers. Moreover, the reduction in efficiency of seafarers suggests that they have a less efficient processing network. To sum up, the proposed DBCA is effective for exploring the dynamic complexity changes in voxel-wise activity and region-wise connectivity, showing that occupational experience can reshape seafarers’ dynamic brain complexity fingerprints.
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Affiliation(s)
- Hongjie Yan
- Department of Neurology, Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, China
| | - Huijun Wu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Yanyan Chen
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Yang Yang
- Key Laboratory of Behavioral Science, Center for Brain Science and Learning Difficulties, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Min Xu
- Center for Brain Disorders and Cognitive Science, Shenzhen University, Shenzhen, China
| | - Weiming Zeng
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China
| | - Jian Zhang
- School of Pharmacy, Health Science Center, Shenzhen University, Shenzhen, China
| | - Chunqi Chang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Peng Cheng Laboratory, Shenzhen, China
- *Correspondence: Nizhuan Wang,
| | - Nizhuan Wang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
- *Correspondence: Nizhuan Wang,
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19
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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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20
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Espinoso A, Andrzejak RG. Phase irregularity: A conceptually simple and efficient approach to characterize electroencephalographic recordings from epilepsy patients. Phys Rev E 2022; 105:034212. [PMID: 35428047 DOI: 10.1103/physreve.105.034212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 03/06/2022] [Indexed: 06/14/2023]
Abstract
The severe neurological disorder epilepsy affects almost 1% of the world population. For patients who suffer from pharmacoresistant focal-onset epilepsy, electroencephalographic (EEG) recordings are essential for the localization of the brain area where seizures start. Apart from the visual inspection of the recordings, quantitative EEG signal analysis techniques proved to be useful for this purpose. Among other features, regularity versus irregularity and phase coherence versus phase independence allowed characterizing brain dynamics from the measured EEG signals. Can phase irregularities also characterize brain dynamics? To address this question, we use the univariate coefficient of phase velocity variation, defined as the ratio of phase velocity standard deviation and the mean phase velocity. Beyond that, as a bivariate measure we use the classical mean phase coherence to quantify the degree of phase locking. All phase-based measures are combined with surrogates to test null hypotheses about the dynamics underlying the signals. In the first part of our analysis, we use the Rössler model system to study our approach under controlled conditions. In the second part, we use the Bern-Barcelona EEG database which consists of focal and nonfocal signals extracted from seizure-free recordings. Focal signals are recorded from brain areas where the first seizure EEG signal changes can be detected, and nonfocal signals are recorded from areas that are not involved in the seizure at its onset. Our results show that focal signals have less phase variability and more phase coherence than nonfocal signals. Once combined with surrogates, the mean phase velocity proved to have the highest discriminative power between focal and nonfocal signals. In conclusion, conceptually simple and easy to compute phase-based measures can help to detect features induced by epilepsy from EEG signals. This holds not only for the classical mean phase coherence but even more so for univariate measures of phase irregularity.
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Affiliation(s)
- Anaïs Espinoso
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Roc Boronat 138, 08018 Barcelona, Catalonia, Spain and Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Carrer Baldiri Reixac 10-12, 08028 Barcelona, Catalonia, Spain
| | - Ralph G Andrzejak
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Roc Boronat 138, 08018 Barcelona, Catalonia, Spain
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21
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Ahmadieh H, Ghassemi F. Assessing the Effects of Alzheimer Disease on EEG Signals Using the Entropy Measure: A Meta-analysis. Basic Clin Neurosci 2022; 13:153-164. [PMID: 36425952 PMCID: PMC9682319 DOI: 10.32598/bcn.2021.1144.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 08/14/2020] [Accepted: 10/03/2020] [Indexed: 06/16/2023] Open
Abstract
INTRODUCTION Alzheimer disease (AD) is the most prevalent neurodegenerative disorder and a type of dementia. About 80% of dementia in older adults is due to AD. According to multiple research articles, AD is associated with several changes in EEG signals, such as slow rhythms, reduction in complexity and functional associations, and disordered functional communication between different brain areas. This research focuses on the entropy parameter. METHODS In this study, the keywords "Entropy," "EEG," and "Alzheimer" were used. In the initial search, 102 articles were found. In the first stage, after investigating the Abstracts of the articles, the number of them was reduced to 62, and upon further review of the remaining articles, the number of articles was reduced to 18. Some papers have used more than one entropy of EEG signals to compare, and some used more than one database. So, 25 entropy measures were considered in this meta-analysis. We used the Standardized Mean Difference (SMD) to find the effect size and compare the effects of AD on the entropy of the EEG signal in healthy people. Funnel plots were used to investigate the bias of meta-analysis. RESULTS According to the articles, entropy seems to be a good benchmark for comparing the EEG signals between healthy people and AD people. CONCLUSION It can be concluded that AD can significantly affect EEG signals and reduce the entropy of EEG signals. HIGHLIGHTS Our primary question addressed in this study is "Can Alzheimer's Disease significantly affect EEG signals or not?" This paper is the first Meta-Analysis study that reveals the effects of Alzheimer's Disease on EEG signals and the caused reduction in the complexity of the EEG signal. According to the articles, results and funnel plots of this Meta-Analysis, entropy seems to be a good benchmark for comparing the EEG signals in healthy people and people who have Alzheimer's Disease. PLAIN LANGUAGE SUMMARY Alzheimer's Disease is one of the most prevalent neurodegenerative disorder which can affect EEG signals. This study is the first Meta-Analysis in this regard and the results confirm that Alzheimer's Disease reduces the complexity of the EEG signals. We used 25 entropy measures applied in 18 articles. The materials in this Meta-Analysis are 1-SMD for finding the effect size and 2- Funnel plot for investigating the bias of Meta-Analysis.
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Affiliation(s)
- Hajar Ahmadieh
- Department of Biomedical Engineering, Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran
| | - Farnaz Ghassemi
- Department of Biomedical Engineering, Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran
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22
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Weighted Brain Network Analysis on Different Stages of Clinical Cognitive Decline. Bioengineering (Basel) 2022; 9:bioengineering9020062. [PMID: 35200415 PMCID: PMC8869328 DOI: 10.3390/bioengineering9020062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/26/2022] [Accepted: 01/29/2022] [Indexed: 11/25/2022] Open
Abstract
This study addresses brain network analysis over different clinical severity stages of cognitive dysfunction using electroencephalography (EEG). We exploit EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients and Alzheimer’s disease (AD) patients. We propose a new framework to study the topological networks with a spatiotemporal entropy measure for estimating the connectivity. Our results show that functional connectivity and graph analysis are frequency-band dependent, and alterations start at the MCI stage. In delta, the SCI group exhibited a decrease of clustering coefficient and an increase of path length compared to MCI and AD. In alpha, the opposite behavior appeared, suggesting a rapid and high efficiency in information transmission across the SCI network. Modularity analysis showed that electrodes of the same brain region were distributed over several modules, and some obtained modules in SCI were extended from anterior to posterior regions. These results demonstrate that the SCI network was more resilient to neuronal damage compared to that of MCI and even more compared to that of AD. Finally, we confirm that MCI is a transitional stage between SCI and AD, with a predominance of high-strength intrinsic connectivity, which may reflect the compensatory response to the neuronal damage occurring early in the disease process.
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23
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Ding Y, Chu Y, Liu M, Ling Z, Wang S, Li X, Li Y. Fully automated discrimination of Alzheimer's disease using resting-state electroencephalography signals. Quant Imaging Med Surg 2022; 12:1063-1078. [PMID: 35111605 DOI: 10.21037/qims-21-430] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 08/24/2021] [Indexed: 12/29/2022]
Abstract
Background The Alzheimer's disease (AD) population increases worldwide, placing a heavy burden on the economy and society. Presently, there is no cure for AD. Developing a convenient method of screening for AD and mild cognitive impairment (MCI) could enable early intervention, thus slowing down the progress of the disease and enabling better overall disease management. Methods In the current study, resting-state electroencephalography (EEG) data were acquired from 113 normal cognition (NC) subjects, 116 amnestic MCI patients, and 72 probable AD patients. After preprocessing by an automatic algorithm, features including spectral power, complexity, and functional connectivity were extracted, and machine-learning classifiers were built to differentiate among the 3 groups. The classification performance was evaluated from multiple perspectives, including accuracy, specificity, sensitivity, area under the curve (AUC) with 95% confidence intervals, and compared to the empirical chance level by permutation tests. Results The analysis of variance results (P<0.05 with false discovery rate correction) confirmed the tendency to slow brain activity, reduced complexity, and connectivity with AD progress. By combining the features, the ability of the machine-learning classifiers, especially the ensemble trees, to differentiate among the 3 groups, was significantly better than that of the empirical chance level of the permutation test. The AUC of the classifier with the best performance was 80.08% for AD vs. NC, 70.82% for AD vs. MCI, and 63.95% for MCI vs. NC. Conclusions The current study presented a fully automatic procedure that could significantly distinguish NC, MCI, and AD subjects via resting-state EEG signals. The study was based on a large data set with evidence-based medical diagnosis and provided further evidence that resting-state EEG data could assist in the discrimination of AD patients.
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Affiliation(s)
- Yue Ding
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,iFLYTEK Research, iFLYTEK CO., LTD., Hefei, China
| | - Yinxue Chu
- iFLYTEK Research, iFLYTEK CO., LTD., Hefei, China
| | - Meng Liu
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Zhenhua Ling
- National Engineering Laboratory for Speech and Language Information Processing, University of Science and Technology of China, Hefei, China
| | - Shijin Wang
- iFLYTEK Research, iFLYTEK CO., LTD., Hefei, China.,State Key Laboratory of Cognitive Intelligence, Hefei, China
| | - Xin Li
- iFLYTEK Research, iFLYTEK CO., LTD., Hefei, China.,National Engineering Laboratory for Speech and Language Information Processing, University of Science and Technology of China, Hefei, China
| | - Yunxia Li
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
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24
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An integrated entropy-spatial framework for automatic gender recognition enhancement of emotion-based EEGs. Med Biol Eng Comput 2022; 60:531-550. [PMID: 35023073 DOI: 10.1007/s11517-021-02452-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 10/01/2021] [Indexed: 12/15/2022]
Abstract
Investigating gender differences based on emotional changes using electroencephalogram (EEG) is essential to understand various human behavior in the individual situation in our daily life. However, gender differences based on EEG and emotional states are not thoroughly investigated. The main novelty of this paper is twofold. First, it aims to propose an automated gender recognition system through the investigation of five entropies which were integrated as a set of entropy domain descriptors (EDDs) to illustrate the changes in the complexity of EEGs. Second, the combination EDD set was used to develop a customized EEG framework by estimating the entropy-spatial descriptors (ESDs) set for identifying gender from emotional-based EEGs. The proposed methods were validated on EEGs of 30 participants who examined short emotional video clips with four audio-visual stimuli (anger, happiness, sadness, and neutral). The individual performance of computed entropies was statistically examined using analysis of variance (ANOVA) to identify a gender role in the brain emotions. Finally, the proposed ESD framework performance was evaluated using three classifiers: support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF), and long short-term memory (LSTM) deep learning model. The results illustrated the effect of individual EDD features as remarkable indices for investigating gender while studying the relationship between EEG brain activity and emotional state changes. Moreover, the proposed ESD achieved significant enhancement in classification accuracy with SVM indicating that ESD may offer a helpful path for reliable improvement of the gender detection from emotional-based EEGs.
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25
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Regularity and randomness in ageing: Differences in resting-state EEG complexity measured by largest Lyapunov exponent. NEUROIMAGE: REPORTS 2021. [DOI: 10.1016/j.ynirp.2021.100054] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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26
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A Comparative Study of Functional Connectivity Measures for Brain Network Analysis in the Context of AD Detection with EEG. ENTROPY 2021; 23:e23111553. [PMID: 34828251 PMCID: PMC8623641 DOI: 10.3390/e23111553] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 11/12/2021] [Accepted: 11/17/2021] [Indexed: 12/15/2022]
Abstract
This work addresses brain network analysis considering different clinical severity stages of cognitive dysfunction, based on resting-state electroencephalography (EEG). We use a cohort acquired in real-life clinical conditions, which contains EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, and Alzheimer’s disease (AD) patients. We propose to exploit an epoch-based entropy measure to quantify the connectivity links in the networks. This entropy measure relies on a refined statistical modeling of EEG signals with Hidden Markov Models, which allow a better estimation of the spatiotemporal characteristics of EEG signals. We also propose to conduct a comparative study by considering three other measures largely used in the literature: phase lag index, coherence, and mutual information. We calculated such measures at different frequency bands and computed different local graph parameters considering different proportional threshold values for a binary network analysis. After applying a feature selection procedure to determine the most relevant features for classification performance with a linear Support Vector Machine algorithm, our study demonstrates the effectiveness of the statistical entropy measure for analyzing the brain network in patients with different stages of cognitive dysfunction.
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Anderson JAE, Grundy JG, Grady CL, Craik FIM, Bialystok E. Bilingualism contributes to reserve and working memory efficiency: Evidence from structural and functional neuroimaging. Neuropsychologia 2021; 163:108071. [PMID: 34715120 DOI: 10.1016/j.neuropsychologia.2021.108071] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 09/14/2021] [Accepted: 10/22/2021] [Indexed: 01/26/2023]
Abstract
This study compared brain and behavioral outcomes for monolingual and bilingual older adults who reported no cognitive or memory problems on three types of memory that typically decline in older age, namely, working memory (measured by n-back), item, and associative recognition. The results showed that bilinguals were faster on the two-back working memory task than monolinguals but used a set of frontostriatal regions less than monolinguals. There was no group difference on an item/associative recognition task. In brain structure, gray matter volume and white matter integrity (fractional anisotropy) were generally lower in bilinguals than in monolinguals, but bilinguals had better white matter integrity than monolinguals in the bilateral superior corona radiata and better gray matter density in the left inferior temporal gyrus. These regions may help preserve bilinguals' executive functions despite generally more significant atrophy throughout the brain than monolinguals in that these structures contribute to efficient communication between executive frontal regions and subcortical motor regions, and perceptual pathways. Reliable negative correlations between brain structure and age were only observed in bilinguals, and to the extent that bilinguals (but not monolinguals) had better brain structure, their performance was enhanced. Collectively, the findings provide evidence for reserve in bilingual older adults.
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Affiliation(s)
- John A E Anderson
- Carleton University, Departments of Cognitive Science and Psychology, Canada.
| | - John G Grundy
- Iowa State University, Department of Psychology, United States
| | - Cheryl L Grady
- Rotman Research Institute at Baycrest Hospital, Canada; University of Toronto, Department of Psychiatry, Canada; University of Toronto, Department of Psychology, Canada
| | - Fergus I M Craik
- Rotman Research Institute at Baycrest Hospital, Canada; University of Toronto, Department of Psychology, Canada
| | - Ellen Bialystok
- Rotman Research Institute at Baycrest Hospital, Canada; York University, Department of Psychology, Canada
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A Novel EEG Derived Measure of Disrupted Delta Wave Activity during Sleep Predicts All-Cause Mortality Risk. Ann Am Thorac Soc 2021; 19:649-658. [PMID: 34672877 DOI: 10.1513/annalsats.202103-315oc] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
RATIONALE Conventional markers of sleep disturbance, based on manual electroencephalography scoring, may not adequately capture important features of more fundamental electroencephalography-related sleep disturbance. OBJECTIVES This study aimed to determine if more comprehensive power-spectral measures of delta wave activity during sleep are stronger independent predictors of mortality than conventional sleep quality and disturbance metrics. METHODS Power spectral analysis of the delta frequency band and spectral entropy-based markers to quantify disruption of electroencephalography delta power during sleep were performed to examine potential associations with mortality risk in the Sleep Heart Health Study cohort (N = 5804). Adjusted Cox proportional hazard models were used to determine the association between disrupted delta wave activity at baseline and all-cause mortality over an ~11y follow-up period. RESULTS Disrupted delta electroencephalography power during sleep was associated with a 32% increased risk of all-cause mortality compared with no fragmentation (hazard ratios 1.32 [95% confidence interval 1.14, 1.50], after adjusting for total sleep time and other clinical and life-style related covariates including sleep apnea. The association was of similar magnitude to a reduction in total sleep time from 6.5h to 4.25h. Conventional measures of sleep quality, including wake after sleep onset and arousal index were not predictive of all-cause mortality. CONCLUSIONS Delta wave activity disruption during sleep is strongly associated with all-cause mortality risk, independent of traditional potential confounders. Future investigation into the potential role of delta sleep disruption on other specific adverse health consequences such as cardiometabolic, mental health and safety outcomes has considerable potential to provide unique neurophysiological insight.
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Monllor P, Cervera-Ferri A, Lloret MA, Esteve D, Lopez B, Leon JL, Lloret A. Electroencephalography as a Non-Invasive Biomarker of Alzheimer's Disease: A Forgotten Candidate to Substitute CSF Molecules? Int J Mol Sci 2021; 22:10889. [PMID: 34639229 PMCID: PMC8509134 DOI: 10.3390/ijms221910889] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/26/2021] [Accepted: 10/05/2021] [Indexed: 12/12/2022] Open
Abstract
Biomarkers for disease diagnosis and prognosis are crucial in clinical practice. They should be objective and quantifiable and respond to specific therapeutic interventions. Optimal biomarkers should reflect the underlying process (pathological or not), be reproducible, widely available, and allow measurements repeatedly over time. Ideally, biomarkers should also be non-invasive and cost-effective. This review aims to focus on the usefulness and limitations of electroencephalography (EEG) in the search for Alzheimer's disease (AD) biomarkers. The main aim of this article is to review the evolution of the most used biomarkers in AD and the need for new peripheral and, ideally, non-invasive biomarkers. The characteristics of the EEG as a possible source for biomarkers will be revised, highlighting its advantages compared to the molecular markers available so far.
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Affiliation(s)
- Paloma Monllor
- CIBERFES, Department of Physiology, Institute INCLIVA, Faculty of Medicine, Health Research University of Valencia, Avda. Blasco Ibanez 17, 46010 Valencia, Spain; (P.M.); (D.E.)
| | - Ana Cervera-Ferri
- Department of Human Anatomy and Embryology, Faculty of Medicine, University of Valencia, 46010 Valencia, Spain;
| | - Maria-Angeles Lloret
- Department of Clinical Neurophysiology, University Clinic Hospital of Valencia, Avda. Blasco Ibanez, 19, 46010 Valencia, Spain;
| | - Daniel Esteve
- CIBERFES, Department of Physiology, Institute INCLIVA, Faculty of Medicine, Health Research University of Valencia, Avda. Blasco Ibanez 17, 46010 Valencia, Spain; (P.M.); (D.E.)
| | - Begoña Lopez
- Department of Neurology, University Clinic Hospital of Valencia, Avda. Blasco Ibanez, 19, 46010 Valencia, Spain;
| | - Jose-Luis Leon
- Ascires Biomedical Group, Department of Neuroradiology, Hospital Clinico Universitario, 46010 Valencia, Spain;
| | - Ana Lloret
- CIBERFES, Department of Physiology, Institute INCLIVA, Faculty of Medicine, Health Research University of Valencia, Avda. Blasco Ibanez 17, 46010 Valencia, Spain; (P.M.); (D.E.)
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Complexity and Entropy Analysis to Improve Gender Identification from Emotional-Based EEGs. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:8537000. [PMID: 34603651 PMCID: PMC8481061 DOI: 10.1155/2021/8537000] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 08/29/2021] [Accepted: 09/01/2021] [Indexed: 12/14/2022]
Abstract
Investigating gender differences based on emotional changes becomes essential to understand various human behaviors in our daily life. Ten students from the University of Vienna have been recruited by recording the electroencephalogram (EEG) dataset while watching four short emotional video clips (anger, happiness, sadness, and neutral) of audiovisual stimuli. In this study, conventional filter and wavelet (WT) denoising techniques were applied as a preprocessing stage and Hurst exponent (Hur) and amplitude-aware permutation entropy (AAPE) features were extracted from the EEG dataset. k-nearest neighbors (kNN) and support vector machine (SVM) classification techniques were considered for automatic gender recognition from emotional-based EEGs. The main novelty of this paper is twofold: first, to investigate Hur as a complexity feature and AAPE as an irregularity parameter for the emotional-based EEGs using two-way analysis of variance (ANOVA) and then integrating these features to propose a new CompEn hybrid feature fusion method towards developing the novel WT_CompEn gender recognition framework as a core for an automated gender recognition model to be sensitive for identifying gender roles in the brain-emotion relationship for females and males. The results illustrated the effectiveness of Hur and AAPE features as remarkable indices for investigating gender-based anger, sadness, happiness, and neutral emotional state. Moreover, the proposed WT_CompEn framework achieved significant enhancement in SVM classification accuracy of 100%, indicating that the novel WT_CompEn may offer a useful way for reliable enhancement of gender recognition of different emotional states. Therefore, the novel WT_CompEn framework is a crucial goal for improving the process of automatic gender recognition from emotional-based EEG signals allowing for more comprehensive insights to understand various gender differences and human behavior effects of an intervention on the brain.
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31
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Xi X, Tao Q, Li J, Kong W, Zhao YB, Wang H, Wang J. Emotion-movement relationship: A study using functional brain network and cortico-muscular coupling. J Neurosci Methods 2021; 362:109320. [PMID: 34390757 DOI: 10.1016/j.jneumeth.2021.109320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 08/04/2021] [Accepted: 08/09/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Emotions play a crucial role in human communication and affect all aspects of human life. However, to date, there have been few studies conducted on how movements under different emotions influence human brain activity and cortico-muscular coupling (CMC). NEW METHODS In this study, for the first time, electroencephalogram (EEG) and electromyogram physiological electrical signals were used to explore this relationship. We performed frequency domain and nonlinear dynamics analyses on EEG signals and used transfer entropy to explore the CMC associated with the emotion-movement relationship. To study the transmission of information between different brain regions, we also constructed a functional brain network and calculated various network metrics using graph theory. RESULTS We found that, compared with a neutral emotional state, movements made during happy and sad emotions had increased CMC strength and EEG power and complexity. The functional brain network metrics of these three emotional states were also different. COMPARISON WITH EXISTING METHODS Much of the emotion-movement relationship research has been based on subjective expression and external performance. Our research method, however, focused on the processing of physiological electrical signals, which contain a wealth of information and can objectively reveal the inner mechanisms of the emotion-movement relationship. CONCLUSIONS Different emotional states can have a significant influence on human movement. This study presents a detailed introduction to brain activity and CMC.
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Affiliation(s)
- Xugang Xi
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China.
| | - Qun Tao
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China
| | - Jingqi Li
- Hangzhou Mingzhou Naokang Rehabilitation Hospital, Hangzhou 311215, China.
| | - Wanzeng Kong
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China; School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Yun-Bo Zhao
- Department of Automation, University of Science and Technology of China, Hefei, China
| | - Huijiao Wang
- Hangzhou Vocational & Technology College, Hangzhou 310018, China
| | - Junhong Wang
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China
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32
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Trinh TT, Tsai CF, Hsiao YT, Lee CY, Wu CT, Liu YH. Identifying Individuals With Mild Cognitive Impairment Using Working Memory-Induced Intra-Subject Variability of Resting-State EEGs. Front Comput Neurosci 2021; 15:700467. [PMID: 34421565 PMCID: PMC8373435 DOI: 10.3389/fncom.2021.700467] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 06/30/2021] [Indexed: 11/22/2022] Open
Abstract
Individuals with mild cognitive impairment (MCI) are at high risk of developing into dementia (e. g., Alzheimer's disease, AD). A reliable and effective approach for early detection of MCI has become a critical challenge. Although compared with other costly or risky lab tests, electroencephalogram (EEG) seems to be an ideal alternative measure for early detection of MCI, searching for valid EEG features for classification between healthy controls (HCs) and individuals with MCI remains to be largely unexplored. Here, we design a novel feature extraction framework and propose that the spectral-power-based task-induced intra-subject variability extracted by this framework can be an encouraging candidate EEG feature for the early detection of MCI. In this framework, we extracted the task-induced intra-subject spectral power variability of resting-state EEGs (as measured by a between-run similarity) before and after participants performing cognitively exhausted working memory tasks as the candidate feature. The results from 74 participants (23 individuals with AD, 24 individuals with MCI, 27 HC) showed that the between-run similarity over the frontal and central scalp regions in the HC group is higher than that in the AD or MCI group. Furthermore, using a feature selection scheme and a support vector machine (SVM) classifier, the between-run similarity showed encouraging leave-one-participant-out cross-validation (LOPO-CV) classification performance for the classification between the MCI and HC (80.39%) groups and between the AD vs. HC groups (78%), and its classification performance is superior to other widely-used features such as spectral powers, coherence, and the complexity estimated by Katz's method extracted from single-run resting-state EEGs (a common approach in previous studies). The results based on LOPO-CV, therefore, suggest that the spectral-power-based task-induced intra-subject EEG variability extracted by the proposed feature extraction framework has the potential to serve as a neurophysiological feature for the early detection of MCI in individuals.
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Affiliation(s)
- Thanh-Tung Trinh
- Neural Engineering and Smart Systems Laboratory, Graduate Institute of Manufacturing Technology, College of Mechanical and Electrical Engineering, National Taipei University of Technology (Taipei Tech), Taipei, Taiwan
| | - Chia-Fen Tsai
- Department of Psychiatry, Division of Geriatric Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Tsung Hsiao
- Neural Engineering and Smart Systems Laboratory, Graduate Institute of Mechatronic Engineering, National Taipei University of Technology (Taipei Tech), Taipei, Taiwan
| | - Chun-Ying Lee
- Department of Mechanical Engineering, National Taipei University of Technology (Taipei Tech), Taipei, Taiwan
| | - Chien-Te Wu
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan
| | - Yi-Hung Liu
- Department of Mechanical Engineering, National Taiwan University of Science and Technology (Taiwan Tech), Taipei, Taiwan
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Li F, Jiang L, Liao Y, Si Y, Yi C, Zhang Y, Zhu X, Yang Z, Yao D, Cao Z, Xu P. Brain variability in dynamic resting-state networks identified by fuzzy entropy: a scalp EEG study. J Neural Eng 2021; 18. [PMID: 34153948 DOI: 10.1088/1741-2552/ac0d41] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 06/21/2021] [Indexed: 11/12/2022]
Abstract
Objective.Exploring the temporal variability in spatial topology during the resting state attracts growing interest and becomes increasingly useful to tackle the cognitive process of brain networks. In particular, the temporal brain dynamics during the resting state may be delineated and quantified aligning with cognitive performance, but few studies investigated the temporal variability in the electroencephalogram (EEG) network as well as its relationship with cognitive performance.Approach.In this study, we proposed an EEG-based protocol to measure the nonlinear complexity of the dynamic resting-state network by applying the fuzzy entropy. To further validate its applicability, the fuzzy entropy was applied into simulated and two independent datasets (i.e. decision-making and P300).Main results.The simulation study first proved that compared to the existing methods, this approach could not only exactly capture the pattern dynamics in time series but also overcame the magnitude effect of time series. Concerning the two EEG datasets, the flexible and robust network architectures of the brain cortex at rest were identified and distributed at the bilateral temporal lobe and frontal/occipital lobe, respectively, whose variability metrics were found to accurately classify different groups. Moreover, the temporal variability of resting-state network property was also either positively or negatively related to individual cognitive performance.Significance.This outcome suggested the potential of fuzzy entropy for evaluating the temporal variability of the dynamic resting-state brain networks, and the fuzzy entropy is also helpful for uncovering the fluctuating network variability that accounts for the individual decision differences.
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Affiliation(s)
- Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Lin Jiang
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Yuanyuan Liao
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Yajing Si
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Psychology, Xinxiang Medical University, Xinxiang 453003, People's Republic of China
| | - Chanli Yi
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Yangsong Zhang
- School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, People's Republic of China
| | - Xianjun Zhu
- The Sichuan Provincial Key Laboratory for Human Disease Gene Study, Prenatal Diagnosis Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.,Research Unit for Blindness Prevention of Chinese Academy of Medical Sciences (2019RU026), Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, People's Republic of China
| | - Zhenglin Yang
- The Sichuan Provincial Key Laboratory for Human Disease Gene Study, Prenatal Diagnosis Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.,Research Unit for Blindness Prevention of Chinese Academy of Medical Sciences (2019RU026), Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, People's Republic of China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Zehong Cao
- Discipline of Information and Communication Technology, University of Tasmania, TAS, Australia
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
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Zhang H, Geng X, Wang Y, Guo Y, Gao Y, Zhang S, Du W, Liu L, Sun M, Jiao F, Yi F, Li X, Wang L. The Significance of EEG Alpha Oscillation Spectral Power and Beta Oscillation Phase Synchronization for Diagnosing Probable Alzheimer Disease. Front Aging Neurosci 2021; 13:631587. [PMID: 34163348 PMCID: PMC8215164 DOI: 10.3389/fnagi.2021.631587] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 05/11/2021] [Indexed: 11/30/2022] Open
Abstract
Alzheimer disease (AD) is the most common cause of dementia in geriatric population. At present, no effective treatments exist to reverse the progress of AD, however, early diagnosis and intervention might delay its progression. The search for biomarkers with good safety, repeatable detection, reliable sensitivity and community application is necessary for AD screening and early diagnosis and timely intervention. Electroencephalogram (EEG) examination is a non-invasive, quantitative, reproducible, and cost-effective technique which is suitable for screening large population for possible AD. The power spectrum, complexity and synchronization characteristics of EEG waveforms in AD patients have distinct deviation from normal elderly, indicating these EEG features can be a promising candidate biomarker of AD. However, current reported deviation results are inconsistent, possibly due to multiple factors such as diagnostic criteria, sample sizes and the use of different computational measures. In this study, we collected two neurological tests scores (MMSE and MoCA) and the resting-state EEG of 30 normal control elderly subjects (NC group) and 30 probable AD patients confirmed by Pittsburgh compound B positron emission tomography (PiB-PET) inspection (AD group). We calculated the power spectrum, spectral entropy and phase synchronization index features of these two groups’ EEG at left/right frontal, temporal, central and occipital brain regions in 4 frequency bands: δ oscillation (1–4 Hz), θ oscillation (4–8 Hz), α oscillation (8–13 Hz), and β oscillation (13–30 Hz). In most brain areas, we found that the AD group had significant differences compared to NC group: (1) decreased α oscillation power and increased θ oscillation power; (2) decreased spectral entropy in α oscillation and elevated spectral entropy in β oscillation; and (3) decrease phase synchronization index in δ, θ, and β oscillation. We also found that α oscillation spectral power and β oscillation phase synchronization index correlated well with the MMSE/MoCA test scores in AD groups. Our study suggests that these two EEG features might be useful metrics for population screening of probable AD patients.
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Affiliation(s)
- Haifeng Zhang
- Medical School of Chinese People's Liberation Army, Beijing, China.,Department of Neurology, The 2nd Medical Center, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army General Hospital, Beijing, China.,Health Service Department of the Guard Bureau of the Joint Staff Department, Joint Staff of the Central Military Commission of Chinese PLA, Beijing, China
| | - Xinling Geng
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Yuanyuan Wang
- Department of Neurology, The 2nd Medical Center, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Yanjun Guo
- Department of Neurology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Ya Gao
- Department of Geriatrics, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Shouzi Zhang
- The Psycho Department of Beijing Geriatric Hospital, Beijing, China
| | - Wenjin Du
- Department of Neurology, Air Force Medical Center, Chinese People's Liberation Army, Beijing, China
| | - Lixin Liu
- The Psycho Department of Beijing Geriatric Hospital, Beijing, China
| | - Mingyan Sun
- Ninth Health Care Department of the Second Medical Center of PLA General Hospital, Beijing, China
| | - Fubin Jiao
- Medical School of Chinese People's Liberation Army, Beijing, China.,Department of Neurology, The 2nd Medical Center, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army General Hospital, Beijing, China.,Health Service Department of the Guard Bureau of the Joint Staff Department, Joint Staff of the Central Military Commission of Chinese PLA, Beijing, China
| | - Fang Yi
- Department of Neurology, The 2nd Medical Center, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army General Hospital, Beijing, China.,Department of Neurology, Lishilu Outpatient, Jingzhong Medical District, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Luning Wang
- Medical School of Chinese People's Liberation Army, Beijing, China.,Department of Neurology, The 2nd Medical Center, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army General Hospital, Beijing, China
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35
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Özbek Y, Fide E, Yener GG. Resting-state EEG alpha/theta power ratio discriminates early-onset Alzheimer's disease from healthy controls. Clin Neurophysiol 2021; 132:2019-2031. [PMID: 34284236 DOI: 10.1016/j.clinph.2021.05.012] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 03/12/2021] [Accepted: 05/17/2021] [Indexed: 11/17/2022]
Abstract
OBJECTIVES The present study aims to compare early-onset Alzheimer's disease (EOAD) patients with healthy controls (HC), and late-onset Alzheimer's disease (LOAD) patients using resting-state delta, theta, alpha, and beta oscillations and provide a cut-off score of alpha/theta ratio to discriminate individuals with EOAD and young HC. METHODS Forty-seven individuals with EOAD, 51 individuals with LOAD, and demographically-matched 49 young and 51 older controls were included in the study. Spectral-power analysis using Fast-Fourier Transformation (FFT) is performed on resting-state electroencephalography (EEG) data. Delta, theta, alpha, and beta oscillations compared between groups and Receiver Operating Characteristic (ROC) curve analysis was conducted. RESULTS Compared to healthy controls individuals with EOAD showed an increase in slow frequency bands and a decrease in fast frequency bands. Frontal alpha/theta power ratio is the best discriminating value between EOAD and young HC with the sensitivity and specificity greater than 80% with area under the curve (AUC) 0.881. CONCLUSIONS EOAD display more widespread and severe electrophysiological abnormalities than LOAD and HC which may reflect more pronounced pathological burden and cholinergic deficits in EOAD. Additionally, the alpha/theta ratio can discriminate EOAD and young HC successfully. SIGNIFICANCE This study is the first to report that resting-state EEG power can be a promising marker for diagnostic accuracy between EOAD and healthy controls.
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Affiliation(s)
- Yağmur Özbek
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey
| | - Ezgi Fide
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey
| | - Görsev G Yener
- Izmir Biomedicine and Genome Center, Dokuz Eylul University Health Campus, Izmir, Turkey; Izmir University of Economics, Faculty of Medicine, Izmir, Turkey.
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36
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Amini M, Pedram MM, Moradi A, Ouchani M. Diagnosis of Alzheimer's Disease by Time-Dependent Power Spectrum Descriptors and Convolutional Neural Network Using EEG Signal. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:5511922. [PMID: 33981355 PMCID: PMC8088352 DOI: 10.1155/2021/5511922] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 03/26/2021] [Accepted: 04/07/2021] [Indexed: 12/22/2022]
Abstract
Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alzheimer's disease and its complications is essential. Electroencephalogram is a technology that allows thousands of neurons with equal spatial orientation of the duration of cerebral cortex electrical activity to be registered by postsynaptic potential. Therefore, in this paper, the time-dependent power spectrum descriptors are used to diagnose the electroencephalogram signal function from three groups: mild cognitive impairment, Alzheimer's disease, and healthy control test samples. The final feature used in three modes of traditional classification methods is recorded: k-nearest neighbors, support vector machine, linear discriminant analysis approaches, and documented results. Finally, for Alzheimer's disease patient classification, the convolutional neural network architecture is presented. The results are indicated using output assessment. For the convolutional neural network approach, the accurate meaning of accuracy is 82.3%. 85% of mild cognitive impairment cases are accurately detected in-depth, but 89.1% of the Alzheimer's disease and 75% of the healthy population are correctly diagnosed. The presented convolutional neural network outperforms other approaches because performance and the k-nearest neighbors' approach is the next target. The linear discriminant analysis and support vector machine were at the low area under the curve values.
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Affiliation(s)
- Morteza Amini
- Department of Cognitive Modeling, Institute for Cognitive Science Studies, Shahid Beheshti University, Tehran, Iran
| | - Mir Mohsen Pedram
- Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
- Department of Cognitive Modeling, Institute for Cognitive Science Studies, Tehran, Iran
| | - AliReza Moradi
- Department of Clinical Psychology, Faculty of Psychology and Educational Science, Kharazmi University, Tehran, Iran
- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran
| | - Mahshad Ouchani
- Institute for Cognitive and Brain Science, Shahid Beheshti University, Tehran, Iran
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Revilla-Vallejo M, Poza J, Gomez-Pilar J, Hornero R, Tola-Arribas MÁ, Cano M, Gómez C. Exploring the Alterations in the Distribution of Neural Network Weights in Dementia Due to Alzheimer's Disease. ENTROPY (BASEL, SWITZERLAND) 2021; 23:500. [PMID: 33922270 PMCID: PMC8146430 DOI: 10.3390/e23050500] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/10/2021] [Accepted: 04/19/2021] [Indexed: 11/17/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder which has become an outstanding social problem. The main objective of this study was to evaluate the alterations that dementia due to AD elicits in the distribution of functional network weights. Functional connectivity networks were obtained using the orthogonalized Amplitude Envelope Correlation (AEC), computed from source-reconstructed resting-state eletroencephalographic (EEG) data in a population formed by 45 cognitive healthy elderly controls, 69 mild cognitive impaired (MCI) patients and 81 AD patients. Our results indicated that AD induces a progressive alteration of network weights distribution; specifically, the Shannon entropy (SE) of the weights distribution showed statistically significant between-group differences (p < 0.05, Kruskal-Wallis test, False Discovery Rate corrected). Furthermore, an in-depth analysis of network weights distributions was performed in delta, alpha, and beta-1 frequency bands to discriminate the weight ranges showing statistical differences in SE. Our results showed that lower and higher weights were more affected by the disease, whereas mid-range connections remained unchanged. These findings support the importance of performing detailed analyses of the network weights distribution to further understand the impact of AD progression on functional brain activity.
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Affiliation(s)
- Marcos Revilla-Vallejo
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain; (J.P.); (J.G.-P.); (R.H.); (C.G.)
| | - Jesús Poza
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain; (J.P.); (J.G.-P.); (R.H.); (C.G.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain;
- IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, 47011 Valladolid, Spain
| | - Javier Gomez-Pilar
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain; (J.P.); (J.G.-P.); (R.H.); (C.G.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain;
| | - Roberto Hornero
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain; (J.P.); (J.G.-P.); (R.H.); (C.G.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain;
- IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, 47011 Valladolid, Spain
| | - Miguel Ángel Tola-Arribas
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain;
- Department of Neurology, Río Hortega University Hospital, 47012 Valladolid, Spain
| | - Mónica Cano
- Department of Clinical Neurophysiology, Río Hortega University Hospital, 47012 Valladolid, Spain;
| | - Carlos Gómez
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain; (J.P.); (J.G.-P.); (R.H.); (C.G.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain;
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38
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Frequency Bands Selection for Seizure Classification and Forecasting Using NLP, Random Forest and SVM Models. Brain Inform 2021. [DOI: 10.1007/978-3-030-86993-9_29] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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Santos Toural JE, Montoya Pedrón A, Marañón Reyes EJ. A new method for classification of subjects with major cognitive disorder, Alzheimer type, based on electroencephalographic biomarkers. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
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Perpetuini D, Chiarelli AM, Filippini C, Cardone D, Croce P, Rotunno L, Anzoletti N, Zito M, Zappasodi F, Merla A. Working Memory Decline in Alzheimer's Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E1380. [PMID: 33279924 PMCID: PMC7762102 DOI: 10.3390/e22121380] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 11/30/2020] [Accepted: 12/03/2020] [Indexed: 12/14/2022]
Abstract
Alzheimer's disease (AD) is characterized by working memory (WM) failures that can be assessed at early stages through administering clinical tests. Ecological neuroimaging, such as Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS), may be employed during these tests to support AD early diagnosis within clinical settings. Multimodal EEG-fNIRS could measure brain activity along with neurovascular coupling (NC) and detect their modifications associated with AD. Data analysis procedures based on signal complexity are suitable to estimate electrical and hemodynamic brain activity or their mutual information (NC) during non-structured experimental paradigms. In this study, sample entropy of whole-head EEG and frontal/prefrontal cortex fNIRS was evaluated to assess brain activity in early AD and healthy controls (HC) during WM tasks (i.e., Rey-Osterrieth complex figure and Raven's progressive matrices). Moreover, conditional entropy between EEG and fNIRS was evaluated as indicative of NC. The findings demonstrated the capability of complexity analysis of multimodal EEG-fNIRS to detect WM decline in AD. Furthermore, a multivariate data-driven analysis, performed on these entropy metrics and based on the General Linear Model, allowed classifying AD and HC with an AUC up to 0.88. EEG-fNIRS may represent a powerful tool for the clinical evaluation of WM decline in early AD.
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Affiliation(s)
- David Perpetuini
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Antonio Maria Chiarelli
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Chiara Filippini
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Daniela Cardone
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Pierpaolo Croce
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Ludovica Rotunno
- Department of Medicine and Science of Ageing, University G. D’Annunzio, Via Dei Vestini 31, 66100 Chieti, Italy; (L.R.); (N.A.); (M.Z.)
| | - Nelson Anzoletti
- Department of Medicine and Science of Ageing, University G. D’Annunzio, Via Dei Vestini 31, 66100 Chieti, Italy; (L.R.); (N.A.); (M.Z.)
| | - Michele Zito
- Department of Medicine and Science of Ageing, University G. D’Annunzio, Via Dei Vestini 31, 66100 Chieti, Italy; (L.R.); (N.A.); (M.Z.)
| | - Filippo Zappasodi
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Arcangelo Merla
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
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Wang Z. Brain Entropy Mapping in Healthy Aging and Alzheimer's Disease. Front Aging Neurosci 2020; 12:596122. [PMID: 33240080 PMCID: PMC7683386 DOI: 10.3389/fnagi.2020.596122] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 10/06/2020] [Indexed: 12/18/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease, for which aging remains the major risk factor. Aging is under a consistent pressure of increasing brain entropy (BEN) due to the progressive brain deteriorations. Noticeably, the brain constantly consumes a large amount of energy to maintain its functional integrity, likely creating or maintaining a big "reserve" to counteract the high entropy. Malfunctions of this latent reserve may indicate a critical point of disease progression. The purpose of this study was to characterize BEN in aging and AD and to test an inverse-U-shape BEN model: BEN increases with age and AD pathology in normal aging but decreases in the AD continuum. BEN was measured with resting state fMRI and compared across aging and the AD continuum. Associations of BEN with age, education, clinical symptoms, and pathology were examined by multiple regression. The analysis results highlighted resting BEN in the default mode network, medial temporal lobe, and prefrontal cortex and showed that: (1) BEN increased with age and pathological deposition in normal aging but decreased with age and pathological deposition in the AD continuum; (2) AD showed catastrophic BEN reduction, which was related to more severe cognitive impairment and daily function disability; and (3) BEN decreased with education years in normal aging, but not in the AD continuum. BEN evolution follows an inverse-U trajectory when AD progresses from normal aging to AD dementia. Education is beneficial for suppressing the entropy increase potency in normal aging.
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Affiliation(s)
- Ze Wang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, United States
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42
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Maturana-Candelas A, Gómez C, Poza J, Ruiz-Gómez SJ, Hornero R. Inter-band Bispectral Analysis of EEG Background Activity to Characterize Alzheimer's Disease Continuum. Front Comput Neurosci 2020; 14:70. [PMID: 33100999 PMCID: PMC7554631 DOI: 10.3389/fncom.2020.00070] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 06/25/2020] [Indexed: 11/13/2022] Open
Abstract
The aim of this study was to characterize the EEG alterations in inter-band interactions along the Alzheimer's disease (AD) continuum. For this purpose, EEG background activity from 51 healthy control subjects, 51 mild cognitive impairment patients, 50 mild AD patients, 50 moderate AD patients, and 50 severe AD patients was analyzed by means of bispectrum. Three inter-band features were extracted from bispectrum matrices: bispectral relative power (BispRP), cubic bispectral entropy (BispEn), and bispectral median frequency (BispMF). BispRP results showed an increase of delta and theta interactions with other frequency bands and the opposite behavior for alpha, beta-1, and beta-2. Delta and theta interactions, along with the rest of the spectrum, also experimented a decrease of BispEn with disease progression, suggesting these bands interact with a reduced variety of components in advanced stages of dementia. Finally, BispMF showed a consistent reduction along the AD continuum in all bands, which is reflective of an interaction of the global spectrum with lower frequency bands as the disease develops. Our results indicate a progressive decrease in inter-band interactions with the severity of the disease, especially those involving high frequency components. Since inter-band coupling oscillations are related to complex and multi-scaled brain processes, these alterations likely reflect the neurodegeneration associated with the AD continuum.
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Affiliation(s)
- Aarón Maturana-Candelas
- Biomedical Engineering Group, Escuela Técnica Superior de Ingenieros, de Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Carlos Gómez
- Biomedical Engineering Group, Escuela Técnica Superior de Ingenieros, de Telecomunicación, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, Escuela Técnica Superior de Ingenieros, de Telecomunicación, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain.,Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, Valladolid, Spain
| | - Saúl J Ruiz-Gómez
- Biomedical Engineering Group, Escuela Técnica Superior de Ingenieros, de Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group, Escuela Técnica Superior de Ingenieros, de Telecomunicación, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain.,Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, Valladolid, Spain
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Chen F, Zhao L, Li B, Yang L. Depression evaluation based on prefrontal EEG signals in resting state using fuzzy measure entropy. Physiol Meas 2020; 41:095007. [PMID: 33021227 DOI: 10.1088/1361-6579/abb144] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Depression is a mental disorder that causes emotional changes and even suicide. However, there is still a lack of objective physiological data to support current clinical depression diagnosis. Accurate computer-aided diagnosis systems are becoming more and more crucial and urgent for future depression diagnosis. The purpose of this study is to analyze the electroencephalogram (EEG) regularity of depression using fuzzy measure entropy (FMEn), and thus to explore its role in the computer-aided diagnosis of depression. APPROACH Three-channel EEG signals among 35 subjects (divided into two groups according to the severity of the disease) were recorded in this study. First, the frontal delta, theta, alpha and beta frequency bands were extracted after preprocessing, and the sample entropy (SEn) and the FMEn were calculated. Then, the difference between the two groups and the correlation between the entropy values and the Hamilton Depression Rating Scale scores were analyzed using statistical analysis. Finally, the results of FMEn were compared with those of SEn. MAIN RESULTS A better statistically significant difference between the two groups using FMEn was revealed, with p < 0.01 in the theta and alpha bands. In terms of SEn, only SEn_Fp2 in the delta band, SEn_Fp2 in the theta band and SEn_Fp1 in the alpha band performed better, showing significant differences with p = 0.0006, p = 0.002 and p = 0.0114. SIGNIFICANCE These findings suggest that frontal EEG signal complexity analysis with depression using FMEn might be more sensitive than that using SEn. FMEn could be considered as a promising biomarker for future clinical depression detection.
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Affiliation(s)
- Feifei Chen
- School of Control Science and Engineering, Shandong University, Jinan, People's Republic of China
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44
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Santos Toural JE, Montoya Pedrón A, Marañón Reyes EJ. Classification among healthy, mild cognitive impairment and Alzheimer’s disease subjects based on wavelet entropy and relative beta and theta power. Pattern Anal Appl 2020. [DOI: 10.1007/s10044-020-00910-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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45
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Cuesta-Frau D, Dakappa PH, Mahabala C, Gupta AR. Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E1034. [PMID: 33286803 PMCID: PMC7597093 DOI: 10.3390/e22091034] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 09/10/2020] [Accepted: 09/11/2020] [Indexed: 11/16/2022]
Abstract
Fever is a readily measurable physiological response that has been used in medicine for centuries. However, the information provided has been greatly limited by a plain thresholding approach, overlooking the additional information provided by temporal variations and temperature values below such threshold that are also representative of the subject status. In this paper, we propose to utilize continuous body temperature time series of patients that developed a fever, in order to apply a method capable of diagnosing the specific underlying fever cause only by means of a pattern relative frequency analysis. This analysis was based on a recently proposed measure, Slope Entropy, applied to a variety of records coming from dengue and malaria patients, among other fever diseases. After an input parameter customization, a classification analysis of malaria and dengue records took place, quantified by the Matthews Correlation Coefficient. This classification yielded a high accuracy, with more than 90% of the records correctly labelled in some cases, demonstrating the feasibility of the approach proposed. This approach, after further studies, or combined with more measures such as Sample Entropy, is certainly very promising in becoming an early diagnosis tool based solely on body temperature temporal patterns, which is of great interest in the current Covid-19 pandemic scenario.
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Affiliation(s)
- David Cuesta-Frau
- Technological Institute of Informatics, Universitat Politècnica de València, Alcoi Campus, 03801 Alcoi, Spain
| | | | - Chakrapani Mahabala
- Department of Medicine, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal 575001, India; (C.M.); (A.R.G.)
| | - Arjun R. Gupta
- Department of Medicine, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal 575001, India; (C.M.); (A.R.G.)
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Sarlabous L, Aquino-Esperanza J, Magrans R, de Haro C, López-Aguilar J, Subirà C, Batlle M, Rué M, Gomà G, Ochagavia A, Fernández R, Blanch L. Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation. Sci Rep 2020; 10:13911. [PMID: 32807815 PMCID: PMC7431581 DOI: 10.1038/s41598-020-70814-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 08/05/2020] [Indexed: 11/28/2022] Open
Abstract
Patient-ventilator asynchronies can be detected by close monitoring of ventilator screens by clinicians or through automated algorithms. However, detecting complex patient-ventilator interactions (CP-VI), consisting of changes in the respiratory rate and/or clusters of asynchronies, is a challenge. Sample Entropy (SE) of airway flow (SE-Flow) and airway pressure (SE-Paw) waveforms obtained from 27 critically ill patients was used to develop and validate an automated algorithm for detecting CP-VI. The algorithm's performance was compared versus the gold standard (the ventilator's waveform recordings for CP-VI were scored visually by three experts; Fleiss' kappa = 0.90 (0.87-0.93)). A repeated holdout cross-validation procedure using the Matthews correlation coefficient (MCC) as a measure of effectiveness was used for optimization of different combinations of SE settings (embedding dimension, m, and tolerance value, r), derived SE features (mean and maximum values), and the thresholds of change (Th) from patient's own baseline SE value. The most accurate results were obtained using the maximum values of SE-Flow (m = 2, r = 0.2, Th = 25%) and SE-Paw (m = 4, r = 0.2, Th = 30%) which report MCCs of 0.85 (0.78-0.86) and 0.78 (0.78-0.85), and accuracies of 0.93 (0.89-0.93) and 0.89 (0.89-0.93), respectively. This approach promises an improvement in the accurate detection of CP-VI, and future study of their clinical implications.
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Affiliation(s)
- Leonardo Sarlabous
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain.
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - José Aquino-Esperanza
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain
- Biomedical Research Networking Center in Respiratory Disease (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Facultat de Medicina, Universitat de Barcelona, Barcelona, Spain
| | | | - Candelaria de Haro
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain
- Biomedical Research Networking Center in Respiratory Disease (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Josefina López-Aguilar
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain
- Biomedical Research Networking Center in Respiratory Disease (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Carles Subirà
- Department of Intensive Care, Fundació Althaia, Universitat Internacional de Catalunya , Manresa, Spain
| | - Montserrat Batlle
- Department of Intensive Care, Fundació Althaia, Universitat Internacional de Catalunya , Manresa, Spain
| | - Montserrat Rué
- Department of Basic Medical Sciences, Universitat de Lleida-IRBLLEIDA, Lleida, Spain
| | - Gemma Gomà
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain
| | - Ana Ochagavia
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain
- Biomedical Research Networking Center in Respiratory Disease (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Rafael Fernández
- Biomedical Research Networking Center in Respiratory Disease (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Department of Intensive Care, Fundació Althaia, Universitat Internacional de Catalunya , Manresa, Spain
| | - Lluís Blanch
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain
- Biomedical Research Networking Center in Respiratory Disease (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- BetterCare S.L, Sabadell, Spain
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Gutiérrez-de Pablo V, Gómez C, Poza J, Maturana-Candelas A, Martins S, Gomes I, Lopes AM, Pinto N, Hornero R. Relationship between the Presence of the ApoE ε4 Allele and EEG Complexity along the Alzheimer's Disease Continuum. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3849. [PMID: 32664228 PMCID: PMC7411888 DOI: 10.3390/s20143849] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 06/29/2020] [Accepted: 07/08/2020] [Indexed: 12/15/2022]
Abstract
Alzheimer's disease (AD) is the most prevalent cause of dementia, being considered a major health problem, especially in developed countries. Late-onset AD is the most common form of the disease, with symptoms appearing after 65 years old. Genetic determinants of AD risk are vastly unknown, though, ε 4 allele of the ApoE gene has been reported as the strongest genetic risk factor for AD. The objective of this study was to analyze the relationship between brain complexity and the presence of ApoE ε 4 alleles along the AD continuum. For this purpose, resting-state electroencephalography (EEG) activity was analyzed by computing Lempel-Ziv complexity (LZC) from 46 healthy control subjects, 49 mild cognitive impairment subjects, 45 mild AD patients, 44 moderate AD patients and 33 severe AD patients, subdivided by ApoE status. Subjects with one or more ApoE ε 4 alleles were included in the carriers subgroups, whereas the ApoE ε 4 non-carriers subgroups were formed by subjects without any ε 4 allele. Our results showed that AD continuum is characterized by a progressive complexity loss. No differences were observed between AD ApoE ε 4 carriers and non-carriers. However, brain activity from healthy subjects with ApoE ε 4 allele (carriers subgroup) is more complex than from non-carriers, mainly in left temporal, frontal and posterior regions (p-values < 0.05, FDR-corrected Mann-Whitney U-test). These results suggest that the presence of ApoE ε 4 allele could modify the EEG complexity patterns in different brain regions, as the temporal lobes. These alterations might be related to anatomical changes associated to neurodegeneration, increasing the risk of suffering dementia due to AD before its clinical onset. This interesting finding might help to advance in the development of new tools for early AD diagnosis.
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Affiliation(s)
- Víctor Gutiérrez-de Pablo
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain; (V.G.-d.P.); (J.P.); (A.M.-C.); (R.H.)
| | - Carlos Gómez
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain; (V.G.-d.P.); (J.P.); (A.M.-C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain; (V.G.-d.P.); (J.P.); (A.M.-C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain
- Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, 47011 Valladolid, Spain
| | - Aarón Maturana-Candelas
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain; (V.G.-d.P.); (J.P.); (A.M.-C.); (R.H.)
| | - Sandra Martins
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), 4200-135 Porto, Portugal; (S.M.); (I.G.); (A.M.L.); (N.P.)
- Institute of Research and Innovation in Health (i3S), University of Porto, 4200-135 Porto, Portugal
| | - Iva Gomes
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), 4200-135 Porto, Portugal; (S.M.); (I.G.); (A.M.L.); (N.P.)
- Institute of Research and Innovation in Health (i3S), University of Porto, 4200-135 Porto, Portugal
| | - Alexandra M. Lopes
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), 4200-135 Porto, Portugal; (S.M.); (I.G.); (A.M.L.); (N.P.)
- Institute of Research and Innovation in Health (i3S), University of Porto, 4200-135 Porto, Portugal
| | - Nádia Pinto
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), 4200-135 Porto, Portugal; (S.M.); (I.G.); (A.M.L.); (N.P.)
- Institute of Research and Innovation in Health (i3S), University of Porto, 4200-135 Porto, Portugal
- Center of Mathematics of the University of Porto (CMUP), 4169-007 Porto, Portugal
| | - Roberto Hornero
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain; (V.G.-d.P.); (J.P.); (A.M.-C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain
- Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, 47011 Valladolid, Spain
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48
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Ren P, Ma M, Xie G, Wu Z, Wu D. Altered complexity of resting-state BOLD activity in Alzheimer's disease-related neurodegeneration: a multiscale entropy analysis. Aging (Albany NY) 2020; 12:13571-13582. [PMID: 32649309 PMCID: PMC7377896 DOI: 10.18632/aging.103463] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 05/27/2020] [Indexed: 11/25/2022]
Abstract
Brain complexity, which reflects the ability of the brain to adapt to a changing environment, has been found to be significantly changed with age. However, there is less evidence on the alterations of brain complexity in neurodegenerative disorders such as Alzheimer's disease (AD). Here we investigated the altered complexity of resting-state blood oxygen level-dependent signals in AD-related neurodegeneration using multiscale entropy (MSE) analysis. All participants were recruited from the Alzheimer's Disease Neuroimaging Initiative, including healthy controls (HC, n=62), amnestic mild cognitive impairment (aMCI, n =81) patients, and Alzheimer's disease (AD, n=25) patients. Our results showed time scale-dependent MSE differences across the three groups. In scale=1, significantly changed MSE patterns (HC>aMCI>AD) were found in four brain regions, including the hippocampus, middle frontal gyrus, intraparietal lobe, and superior frontal gyrus. In scale=4, reversed MSE patterns (HC<aMCI<AD) were found in the middle frontal gyrus and middle occipital gyrus. Furthermore, the values of regional entropy were significantly associated with cognitive functions positively on the short time scale, while negatively on the longer time scale. Our findings suggest that MSE could be a reliable measure for characterizing brain deterioration in AD, and may provide insights into the neural mechanism of AD-related neurodegeneration.
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Affiliation(s)
- Ping Ren
- Shenzhen Mental Health Center, Shenzhen, Guangdong, China.,Shenzhen Kangning Hospital, Shenzhen, Guangdong, China
| | - Manxiu Ma
- Center for Neurobiology Research, Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA 24016, USA
| | - Guohua Xie
- Shenzhen Mental Health Center, Shenzhen, Guangdong, China.,Shenzhen Kangning Hospital, Shenzhen, Guangdong, China
| | - Zhiwei Wu
- Shenzhen Mental Health Center, Shenzhen, Guangdong, China.,Shenzhen Kangning Hospital, Shenzhen, Guangdong, China
| | - Donghui Wu
- Shenzhen Mental Health Center, Shenzhen, Guangdong, China.,Shenzhen Kangning Hospital, Shenzhen, Guangdong, China
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49
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Low-Speed Bearing Fault Diagnosis Based on Permutation and Spectral Entropy Measures. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10134666] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Despite its influence on wind energy service life, condition-based maintenance is still challenging to perform. For offshore wind farms, which are placed in harsh and remote environments, damage detection is critically important to schedule maintenance tasks and reduce operation and maintenance costs. One critical component to be monitored on a wind turbine is the pitch bearing, which can operate at low speed and high loads. Classical methods and features for general purpose bearings cannot be applied effectively to wind turbine pitch bearings owing to their specific operating conditions (high loads and non-constant very low speed with changing direction). Thus, damage detection of wind turbine pitch bearings is currently a challenge. In this study, entropy indicators are proposed as an alternative to classical bearing analysis. For this purpose, spectral and permutation entropy are combined to analyze a raw vibration signal from a low-speed bearing in several scenarios. The results indicate that entropy values change according to different types of damage on bearings, and the sensitivity of the entropy types differs among them. The study offers some important insights into the use of entropy indicators for feature extraction and it lays the foundation for future bearing prognosis methods.
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50
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Krishnan PT, Joseph Raj AN, Balasubramanian P, Chen Y. Schizophrenia detection using MultivariateEmpirical Mode Decomposition and entropy measures from multichannel EEG signal. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.05.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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