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Chen YB, Cui HS, Huang CW, Hsu WT. Improving Transmission Line Fault Diagnosis Based on EEMD and Power Spectral Entropy. ENTROPY (BASEL, SWITZERLAND) 2024; 26:806. [PMID: 39330139 PMCID: PMC11431672 DOI: 10.3390/e26090806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Revised: 09/18/2024] [Accepted: 09/20/2024] [Indexed: 09/28/2024]
Abstract
The fault diagnosis on a transmission line based on the characteristics of the power spectral entropy is proposed in this article. The data preprocessing for the experimental measurement is also introduced using the EEMD. The EEMD is used to preprocess experimental measurements, which are nonlinear and non-stationary fault signals, to overcome the mode mixing. This study focuses on the fault location detection of transmission lines during faults. The proposed method is adopted for different fault types through simulation under the fault point by collecting current and voltage signals at a distance from the fault point. An analysis and comprehensive evaluation of three-phase measured current and voltage signals at distinct fault locations is conducted. The form and position of the fault are distinguished directly and effectively, thereby significantly improving the transmission line efficiency and accuracy of fault diagnosis.
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Affiliation(s)
- Yuan-Bin Chen
- Department of Electrical Engineering, Zhaoqing University, Zhaoqing 526060, China
| | - Hui-Shan Cui
- Department of Electrical Engineering, Zhaoqing University, Zhaoqing 526060, China
| | - Chia-Wei Huang
- Department of Electrical Engineering, Zhaoqing University, Zhaoqing 526060, China
| | - Wei-Tai Hsu
- Department of Electrical Engineering, Zhaoqing University, Zhaoqing 526060, China
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2
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Al Kouzbary M, Al Kouzbary H, Liu J, Shasmin HN, Arifin N, Osman NAA. Analysis of human ambulation as a chaotic time-series: with nonlinear dynamics tools. Comput Methods Biomech Biomed Engin 2024:1-13. [PMID: 39230205 DOI: 10.1080/10255842.2024.2399023] [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: 05/20/2024] [Revised: 07/29/2024] [Accepted: 08/20/2024] [Indexed: 09/05/2024]
Abstract
The aim of the present study is to investigate the complexity and stability of human ambulation and the implications on robotic prostheses control systems. Fourteen healthy individuals participate in two experiments, the first group run at three different speeds. The second group ascended and descended stairs of a five-level building block at a self-selected speed. All participants completed the experiment with seven inertial measurement units wrapped around the lower body segments and waist. The data were analyzed to determine the fractal dimension, spectral entropy, and the Lyapunov exponent (LyE). Two methods were used to calculate the long-term LyE, first LyE calculated using the full size of data sets. And the embedding dimensions were calculated using Average Mutual Information (AMI) and the False Nearest Neighbor (FNN) algorithm was used to find the time delay. Besides, a second approach was developed to find long-term LyE where the time delay was based on the average period of the gait cycle using adaptive event-based window. The average values of spectral entropy are 0.538 and 0.575 for stairs ambulation and running, respectively. The degree of uncertainty and complexity increases with the ambulation speed. The short term LyEs for tibia orientation have the minimum range of variation when it comes to stairs ascent and descent. Using two-way analysis of variance we demonstrated the effect of the ambulation speed and type of ambulation on spectral entropy. Moreover, it was shown that the fractal dimension only changed significantly with ambulation speed.
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Affiliation(s)
- Mouaz Al Kouzbary
- Center for Applied Biomechanics, Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Hamza Al Kouzbary
- Center for Applied Biomechanics, Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Jingjing Liu
- Center for Applied Biomechanics, Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Hanie Nadia Shasmin
- Center for Applied Biomechanics, Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Nooranida Arifin
- Center for Applied Biomechanics, Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Noor Azuan Abu Osman
- Center for Applied Biomechanics, Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
- The Chancellery, University of Malaya, Kuala Lumpur, Malaysia
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3
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Menesse G, Torres JJ. Information dynamics of in silico EEG Brain Waves: Insights into oscillations and functions. PLoS Comput Biol 2024; 20:e1012369. [PMID: 39236071 PMCID: PMC11407780 DOI: 10.1371/journal.pcbi.1012369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 09/17/2024] [Accepted: 07/26/2024] [Indexed: 09/07/2024] Open
Abstract
The relation between electroencephalography (EEG) rhythms, brain functions, and behavioral correlates is well-established. Some physiological mechanisms underlying rhythm generation are understood, enabling the replication of brain rhythms in silico. This offers a pathway to explore connections between neural oscillations and specific neuronal circuits, potentially yielding fundamental insights into the functional properties of brain waves. Information theory frameworks, such as Integrated Information Decomposition (Φ-ID), relate dynamical regimes with informational properties, providing deeper insights into neuronal dynamic functions. Here, we investigate wave emergence in an excitatory/inhibitory (E/I) balanced network of integrate and fire neurons with short-term synaptic plasticity. This model produces a diverse range of EEG-like rhythms, from low δ waves to high-frequency oscillations. Through Φ-ID, we analyze the network's information dynamics and its relation with different emergent rhythms, elucidating the system's suitability for functions such as robust information transfer, storage, and parallel operation. Furthermore, our study helps to identify regimes that may resemble pathological states due to poor informational properties and high randomness. We found, e.g., that in silico β and δ waves are associated with maximum information transfer in inhibitory and excitatory neuron populations, respectively, and that the coexistence of excitatory θ, α, and β waves is associated to information storage. Additionally, we observed that high-frequency oscillations can exhibit either high or poor informational properties, potentially shedding light on ongoing discussions regarding physiological versus pathological high-frequency oscillations. In summary, our study demonstrates that dynamical regimes with similar oscillations may exhibit vastly different information dynamics. Characterizing information dynamics within these regimes serves as a potent tool for gaining insights into the functions of complex neuronal networks. Finally, our findings suggest that the use of information dynamics in both model and experimental data analysis, could help discriminate between oscillations associated with cognitive functions and those linked to neuronal disorders.
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Affiliation(s)
- Gustavo Menesse
- Department of Electromagnetism and Physics of the Matter & Institute Carlos I for Theoretical and Computational Physics, University of Granada, Granada, Spain
- Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Asunción, San Lorenzo, Paraguay
| | - Joaquín J Torres
- Department of Electromagnetism and Physics of the Matter & Institute Carlos I for Theoretical and Computational Physics, University of Granada, Granada, Spain
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Kheirabadi R, Omranpour H. Learning classifiers in clustered data: BCI pattern recognition model for EEG-based human emotion recognition. Comput Methods Biomech Biomed Engin 2024; 27:1649-1663. [PMID: 37668071 DOI: 10.1080/10255842.2023.2252953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/25/2023] [Accepted: 08/17/2023] [Indexed: 09/06/2023]
Abstract
Evidence suggests that human emotions can be detected using Electroencephalography (EEG) brain signals. Recorded EEG signals, due to their large size, may not initially perform well in classification. For this reason, various feature selection methods are used to improve the performance of classification. The nature of EEG signals is complex and unstable. This article uses the Empirical Mode Decomposition (EMD) method, which is one of the most successful methods in analyzing these signals in recent years. In the proposed model, first, the EEG signals are decomposed using EMD into the number of Intrinsic Mode Functions (IMF), and then, the statistical properties of the IMFs are extracted. To improve the performance of the proposed model, using the RBF kernel and Least Absolute Shrinkage and Selection Operator (LASSO) feature selection, an effective subset of the features that have changed the space is selected. The data are then clustered, and finally, each cluster is classified with a decision tree and random forest and KNN. The purpose of clustering is to increase the accuracy of the classification, which is achieved by focusing each cluster on a limited number of classes. This experiment was performed on the DEAP dataset. The results show that the proposed model with 99.17% accuracy could perform better than recent research such as deep learning and show good performance. In the latest years, with the development of the BCI system, the demand for recognizing emotions based on EEG has increased. We provide a method for classifying clustered data that is efficient for high accuracy.
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Affiliation(s)
- Raoufeh Kheirabadi
- Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran
| | - Hesam Omranpour
- Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran
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Al-Qazzaz NK, Alrahhal M, Jaafer SH, Ali SHBM, Ahmad SA. Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches. Med Eng Phys 2024; 130:104206. [PMID: 39160030 DOI: 10.1016/j.medengphy.2024.104206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 05/16/2024] [Accepted: 07/01/2024] [Indexed: 08/21/2024]
Abstract
Epilepsy is one of the most common brain diseases, characterised by repeated seizures that occur on a regular basis. During a seizure, a patient's muscles flex uncontrollably, causing a loss of mobility and balance, which can be harmful or even fatal. Developing an automatic approach for warning patients of oncoming seizures necessitates substantial research. Analyzing the electroencephalogram (EEG) output from the human brain's scalp region can help predict seizures. EEG data were analyzed to extract time domain features such as Hurst exponent (Hur), Tsallis entropy (TsEn), enhanced permutation entropy (impe), and amplitude-aware permutation entropy (AAPE). In order to automatically diagnose epileptic seizure in children from normal children, this study conducted two sessions. In the first session, the extracted features from the EEG dataset were classified using three machine learning (ML)-based models, including support vector machine (SVM), K nearest neighbor (KNN), or decision tree (DT), and in the second session, the dataset was classified using three deep learning (DL)-based recurrent neural network (RNN) classifiers in The EEG dataset was obtained from the Neurology Clinic of the Ibn Rushd Training Hospital. In this regard, extensive explanations and research from the time domain and entropy characteristics demonstrate that employing GRU, LSTM, and BiLSTM RNN deep learning classifiers on the All-time-entropy fusion feature improves the final classification results.
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Affiliation(s)
- Noor Kamal Al-Qazzaz
- Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, 47146, Iraq.
| | - Maher Alrahhal
- Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Hyderabad, University College of Engineering, Science and Technology Hyderabad, Telangana, India.
| | - Sumai Hamad Jaafer
- Medical Laboratory Department, Erbil Medical Institute, Erbil Polytechnic University, Kirkuk Road, Hadi Chawshli Street, Kurdistan Region, Erbil, Iraq.
| | - Sawal Hamid Bin Mohd Ali
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, 43600, Malaysia; Centre of Advanced Electronic and Communication Engineering, Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, 43600, Malaysia.
| | - Siti Anom Ahmad
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM Serdang, Selangor, 43400, Malaysia; Malaysian Research Institute of Ageing (MyAgeing)TM, Universiti Putra Malaysia, UPM Serdang, Selangor, 43400, Malaysia.
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Tost A, Bachiller A, Medina-Rivera I, Romero S, Serna LY, Rojas-Martínez M, García-Cazorla Á, Mañanas MÁ. Repetitive active and passive cognitive stimulations induce EEG changes in patients with Rett syndrome. Pediatr Res 2024:10.1038/s41390-024-03254-9. [PMID: 39014240 DOI: 10.1038/s41390-024-03254-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 07/18/2024]
Abstract
BACKGROUND Despite being considered a rare disease, Rett syndrome is a leading cause of profound cognitive impairment in females. This study explores game-based cognitive stimulation to enhance attention during learning tasks, offering an alternative treatment perspective. METHODS Fifteen diagnosed Rett syndrome girls participated in four 24-minute sessions, including a 5-minute initial resting state recording. Primary indicators for analysis included relative power and spectral entropy. RESULTS Significant findings indicated variations among conditions (resting state, active task, passive task) in response to stimulation. Notably, over four days, evolution occurred, characterized by decreasing delta power and increasing theta and beta power. Topographic maps confirmed these shifts, highlighting affected brain areas. Linear regression emphasized the most significant impact on the first day, with subsequent shifts towards higher frequencies, particularly during the resting state. By the fourth day, resting-state patterns resembled those during cognitive activities. CONCLUSION Findings suggest cognitive stimulation induces substantial EEG spectral changes, potentially linked to cognitive enhancements in Rett syndrome. The shift towards higher frequency bands and increased spectral entropy align with enhanced brain activation during cognitive sessions, underscoring the potential of cognitive stimulation therapies and calling for further research to optimize abilities in individuals with Rett syndrome. IMPACT Game-based cognitive stimulation induces substantial EEG changes in individuals with Rett syndrome, enhancing cognitive functions, notably attention during learning. This study conducts a distinctive examination, assessing the habituation paradigm through the combination of game-based cognitive stimulation and learning, providing valuable insights into enhancing attention in Rett syndrome. Impacting understanding of cognitive processes in Rett syndrome, this research reveals significant EEG variations during tasks, emphasizing the potential of cognitive stimulation for attention enhancement and the need for further research in tailored interventions.
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Affiliation(s)
- Ana Tost
- Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain.
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain.
| | - Alejandro Bachiller
- Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | | | - Sergio Romero
- Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Leidy-Yanet Serna
- Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Monica Rojas-Martínez
- Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Ángeles García-Cazorla
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
- Neurology Department, Neurometabolic Unit and Synaptic Metabolism Lab, Institut Pediàtric de Recerca, Hospital Sant Joan de Déu, metabERN and CIBERER-ISCIII, Barcelona, Spain
| | - Miguel Ángel Mañanas
- Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
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Gogna Y, Tiwari S, Singla R. Evaluating the performance of the cognitive workload model with subjective endorsement in addition to EEG. Med Biol Eng Comput 2024; 62:2019-2036. [PMID: 38433179 DOI: 10.1007/s11517-024-03049-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 02/13/2024] [Indexed: 03/05/2024]
Abstract
The aptitude-oriented exercises from almost all domains impose cognitive load on their operators. Evaluating such load poses several challenges owing to many factors like measurement mode and complexity, nature of the load, overloading conditions, etc. Nevertheless, the physiological measurement of a specific genre of cognitive load and subjective measurement have not been reported along with each other. In this study, the electroencephalography (EEG)-driven machine learning (Support Vector Machine (SVM)) model is sought along with the support of NASA's Task Load Index (NASA-TLX) rating scale for a novel purpose in workload exploration of operators. The Cognitive Load Theory (CLT) was used as the foundation to design the intrinsic stimulus (Spot the Difference task), as most workloads operators are exposed to are notably intrinsic. The SVM-based three-level classification accuracy ranged from 85.4 to 97.4% (p < 0.05), and the NASA-TLX-based three-level classification accuracy ranged from 88.33 to 97.33%. The t-test results show that the neurometric indices contributing to the classification significantly differed (p < 0.05) for every level. The NASA-TLX scale was utilised for validation in its basic form after the validity (Pearson correlation coefficients 0.338 to 0.805 (p < 0.05)) and reliability (Cronbach's α = 0.753) test. This modeling is beneficial to phase out particular-level cognitive exercises from the curriculum during under or overload workload (critical) conditions.
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Affiliation(s)
- Yamini Gogna
- ICE Department, Dr. B. R. Ambedkar NIT Jalandhar, GT Road Bye-Pass, Jalandhar, Punjab, 144008, India.
| | - Sheela Tiwari
- ICE Department, Dr. B. R. Ambedkar NIT Jalandhar, GT Road Bye-Pass, Jalandhar, Punjab, 144008, India
| | - Rajesh Singla
- ICE Department, Dr. B. R. Ambedkar NIT Jalandhar, GT Road Bye-Pass, Jalandhar, Punjab, 144008, India
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Cheng T, Hu Y, Qin X, Ma J, Zha D, Xie H, Ji T, Liu Q, Wang Z, Hao H, Wu Y, Li L. A predictive model combining connectomics and entropy biomarkers to discriminate long-term vagus nerve stimulation efficacy for pediatric patients with drug-resistant epilepsy. CNS Neurosci Ther 2024; 30:e14751. [PMID: 39015946 PMCID: PMC11252558 DOI: 10.1111/cns.14751] [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: 09/19/2023] [Revised: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 07/18/2024] Open
Abstract
AIMS To predict the vagus nerve stimulation (VNS) efficacy for pediatric drug-resistant epilepsy (DRE) patients, we aim to identify preimplantation biomarkers through clinical features and electroencephalogram (EEG) signals and thus establish a predictive model from a multi-modal feature set with high prediction accuracy. METHODS Sixty-five pediatric DRE patients implanted with VNS were included and followed up. We explored the topological network and entropy features of preimplantation EEG signals to identify the biomarkers for VNS efficacy. A Support Vector Machine (SVM) integrated these biomarkers to distinguish the efficacy groups. RESULTS The proportion of VNS responders was 58.5% (38/65) at the last follow-up. In the analysis of parieto-occipital α band activity, higher synchronization level and nodal efficiency were found in responders. The central-frontal θ band activity showed significantly lower entropy in responders. The prediction model reached an accuracy of 81.5%, a precision of 80.1%, and an AUC (area under the receiver operating characteristic curve) of 0.838. CONCLUSION Our results revealed that, compared to nonresponders, VNS responders had a more efficient α band brain network, especially in the parieto-occipital region, and less spectral complexity of θ brain activities in the central-frontal region. We established a predictive model integrating both preimplantation clinical and EEG features and exhibited great potential for discriminating the VNS responders. This study contributed to the understanding of the VNS mechanism and improved the performance of the current predictive model.
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Affiliation(s)
- Tung‐yang Cheng
- National Engineering Research Center of Neuromodulation, School of Aerospace EngineeringTsinghua UniversityBeijingChina
| | - Yingbing Hu
- National Engineering Research Center of Neuromodulation, School of Aerospace EngineeringTsinghua UniversityBeijingChina
- Tsinghua‐Berkeley Shenzhen InstituteTsinghua UniversityShenzhenChina
| | - Xiaoya Qin
- National Engineering Research Center of Neuromodulation, School of Aerospace EngineeringTsinghua UniversityBeijingChina
- Tsinghua‐Berkeley Shenzhen InstituteTsinghua UniversityShenzhenChina
| | - Jiayi Ma
- Department of PediatricsPeking University First HospitalBeijingChina
| | - Daqi Zha
- National Engineering Research Center of Neuromodulation, School of Aerospace EngineeringTsinghua UniversityBeijingChina
| | - Han Xie
- Department of PediatricsPeking University First HospitalBeijingChina
| | - Taoyun Ji
- Department of PediatricsPeking University First HospitalBeijingChina
- Pediatric Epilepsy CenterPeking University First HospitalBeijingChina
| | - Qingzhu Liu
- Pediatric Epilepsy CenterPeking University First HospitalBeijingChina
| | - Zhiyan Wang
- CAS Key Laboratory of Mental Health, Institute of PsychologyChinese Academy of SciencesBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
| | - Hongwei Hao
- National Engineering Research Center of Neuromodulation, School of Aerospace EngineeringTsinghua UniversityBeijingChina
| | - Ye Wu
- Department of PediatricsPeking University First HospitalBeijingChina
- Pediatric Epilepsy CenterPeking University First HospitalBeijingChina
| | - Luming Li
- National Engineering Research Center of Neuromodulation, School of Aerospace EngineeringTsinghua UniversityBeijingChina
- IDG/McGovern Institute for Brain Research at Tsinghua UniversityBeijingChina
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Samal P, Hashmi MF. An improved empirical mode decomposition method with ensemble classifiers for analysis of multichannel EEG in BCI emotion recognition. Comput Methods Biomech Biomed Engin 2024:1-24. [PMID: 38920119 DOI: 10.1080/10255842.2024.2369257] [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: 12/07/2023] [Accepted: 06/12/2024] [Indexed: 06/27/2024]
Abstract
Emotion recognition using EEG is a difficult study because the signals' unstable behavior, which is brought on by the brain's complex neuronal activity, makes it difficult to extract the underlying patterns inside it. Therefore, to analyse the signal more efficiently, in this article, a hybrid model based on IEMD-KW-Ens (Improved Empirical Mode Decomposition-Kruskal Wallis-Ensemble classifiers) technique is used. Here IEMD based technique is proposed to interpret EEG signals by adding an improved sifting stopping criterion with median filter to get the optimal decomposed EEG signals for further processing. A mixture of time, frequency and non-linear distinct features are extracted for constructing the feature vector. Afterward, we conducted feature selection using KW test to remove the insignificant ones from the feature set. Later the classification of emotions in three-dimensional model is performed in two categories i.e. machine learning based RUSBoosted trees and deep learning based convolutional neural network (CNN) for DEAP and DREAMER datasets and the outcomes are evaluated for valence, arousal, and dominance classes. The findings demonstrate that the hybrid model can successfully classify emotions in multichannel EEG signals. The decomposition approach is also instructive for improving the model's utility in emotional computing.
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Affiliation(s)
- Priyadarsini Samal
- Department of Electronics and Communication Engineering, National Institute of Technology, Warangal, Telangana, India
| | - Mohammad Farukh Hashmi
- Department of Electronics and Communication Engineering, National Institute of Technology, Warangal, Telangana, India
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Mercier M, Pepi C, Carfi-Pavia G, De Benedictis A, Espagnet MCR, Pirani G, Vigevano F, Marras CE, Specchio N, De Palma L. The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach. Sci Rep 2024; 14:10887. [PMID: 38740844 PMCID: PMC11091060 DOI: 10.1038/s41598-024-60622-5] [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: 10/06/2023] [Accepted: 04/25/2024] [Indexed: 05/16/2024] Open
Abstract
Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009-April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46-65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.
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Affiliation(s)
- Mattia Mercier
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
- Department of Physiology, Behavioural Neuroscience PhD Program, Sapienza University, Rome, Italy
| | - Chiara Pepi
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Giusy Carfi-Pavia
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Alessandro De Benedictis
- Neurosurgery Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy
| | | | - Greta Pirani
- Department of Mechanical and Aerospace Engineering - DIMA, Sapienza University of Rome, Rome, Italy
| | - Federico Vigevano
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Carlo Efisio Marras
- Neurosurgery Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy
| | - Nicola Specchio
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy.
| | - Luca De Palma
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
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11
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Shiam AA, Hassan KM, Islam MR, Almassri AMM, Wagatsuma H, Molla MKI. Motor Imagery Classification Using Effective Channel Selection of Multichannel EEG. Brain Sci 2024; 14:462. [PMID: 38790441 PMCID: PMC11119243 DOI: 10.3390/brainsci14050462] [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: 04/05/2024] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 05/26/2024] Open
Abstract
Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain-computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to select effective EEG channels for motor imagery (MI) classification in brain-computer interface (BCI) systems. The method identifies channels with higher entropy scores, which is an indication of greater information content. It discards redundant or noisy channels leading to reduced computational complexity and improved classification accuracy. High entropy means a more disordered pattern, whereas low entropy means a less disordered pattern with less information. The entropy of each channel for individual trials is calculated. The weight of each channel is represented by the mean entropy of the channel over all the trials. A set of channels with higher mean entropy are selected as effective channels for MI classification. A limited number of sub-band signals are created by decomposing the selected channels. To extract the spatial features, the common spatial pattern (CSP) is applied to each sub-band space of EEG signals. The CSP-based features are used to classify the right-hand and right-foot MI tasks using a support vector machine (SVM). The effectiveness of the proposed approach is validated using two publicly available EEG datasets, known as BCI competition III-IV(A) and BCI competition IV-I. The experimental results demonstrate that the proposed approach surpasses cutting-edge techniques.
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Affiliation(s)
- Abdullah Al Shiam
- Department of Computer Science and Engineering, Sheikh Hasina University, Netrokona 2400, Bangladesh;
| | - Kazi Mahmudul Hassan
- Department of Computer Science and Engineering, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh 2224, Bangladesh;
| | - Md. Rabiul Islam
- Department of Medicine, University of Texas Health Science Center, San Antonio, TX 78229, USA;
| | - Ahmed M. M. Almassri
- Department of Intelligent Robotics, Faculty of Engineering, Toyama Prefectural University, Toyama 939-0398, Japan;
| | - Hiroaki Wagatsuma
- Department of Human Intelligence Systems, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Fukuoka 808-0196, Japan;
| | - Md. Khademul Islam Molla
- Department of Computer Science and Engineering, The University of Rajshahi, Rajshahi 6205, Bangladesh
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12
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Patel P, Balasubramanian S, Annavarapu RN. Cross subject emotion identification from multichannel EEG sub-bands using Tsallis entropy feature and KNN classifier. Brain Inform 2024; 11:7. [PMID: 38441825 PMCID: PMC11358557 DOI: 10.1186/s40708-024-00220-3] [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: 09/12/2023] [Accepted: 02/05/2024] [Indexed: 08/29/2024] Open
Abstract
Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain-computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4-7 Hz), alpha-α (8-15 Hz), beta-β (16-31 Hz), gamma-γ (32-55 Hz), and the overall frequency range (0-75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F-score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q = 3. In addition, the highest accuracy and F-score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements.
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Affiliation(s)
- Pragati Patel
- Department of Physics, Pondicherry University, Puducherry, 605014, India
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13
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Hirata A, Niitsu M, Phang CR, Kodera S, Kida T, Rashed EA, Fukunaga M, Sadato N, Wasaka T. High-resolution EEG source localization in personalized segmentation-free head model with multi-dipole fitting. Phys Med Biol 2024; 69:055013. [PMID: 38306964 DOI: 10.1088/1361-6560/ad25c3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 02/02/2024] [Indexed: 02/04/2024]
Abstract
Objective. Electroencephalograms (EEGs) are often used to monitor brain activity. Several source localization methods have been proposed to estimate the location of brain activity corresponding to EEG readings. However, only a few studies evaluated source localization accuracy from measured EEG using personalized head models in a millimeter resolution. In this study, based on a volume conductor analysis of a high-resolution personalized human head model constructed from magnetic resonance images, a finite difference method was used to solve the forward problem and to reconstruct the field distribution.Approach. We used a personalized segmentation-free head model developed using machine learning techniques, in which the abrupt change of electrical conductivity occurred at the tissue interface is suppressed. Using this model, a smooth field distribution was obtained to address the forward problem. Next, multi-dipole fitting was conducted using EEG measurements for each subject (N= 10 male subjects, age: 22.5 ± 0.5), and the source location and electric field distribution were estimated.Main results.For measured somatosensory evoked potential for electrostimulation to the wrist, a multi-dipole model with lead field matrix computed with the volume conductor model was found to be superior than a single dipole model when using personalized segmentation-free models (6/10). The correlation coefficient between measured and estimated scalp potentials was 0.89 for segmentation-free head models and 0.71 for conventional segmented models. The proposed method is straightforward model development and comparable localization difference of the maximum electric field from the target wrist reported using fMR (i.e. 16.4 ± 5.2 mm) in previous study. For comparison, DUNEuro based on sLORETA was (EEG: 17.0 ± 4.0 mm). In addition, somatosensory evoked magnetic fields obtained by Magnetoencephalography was 25.3 ± 8.5 mm using three-layer sphere and sLORETA.Significance. For measured EEG signals, our procedures using personalized head models demonstrated that effective localization of the somatosensory cortex, which is located in a non-shallower cortex region. This method may be potentially applied for imaging brain activity located in other non-shallow regions.
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Affiliation(s)
- Akimasa Hirata
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
- Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan
| | - Masamune Niitsu
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
| | - Chun Ren Phang
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
- Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan
| | - Sachiko Kodera
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
- Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan
| | - Tetsuo Kida
- Department of Functioning and Disability, Institute for Developmental Research, Aichi Developmental Disability Center, Kasugai 480-0392, Japan
| | - Essam A Rashed
- Graduate School of Information Science, University of Hyogo, Kobe 650-0047, Japan
| | - Masaki Fukunaga
- Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan
| | - Norihiro Sadato
- Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan
| | - Toshiaki Wasaka
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
- Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan
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14
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Chen C, Chen Z, Hu M, Zhou S, Xu S, Zhou G, Zhou J, Li Y, Chen B, Yao D, Li F, Liu Y, Su S, Xu P, Ma X. EEG brain network variability is correlated with other pathophysiological indicators of critical patients in neurology intensive care unit. Brain Res Bull 2024; 207:110881. [PMID: 38232779 DOI: 10.1016/j.brainresbull.2024.110881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 12/13/2023] [Accepted: 01/13/2024] [Indexed: 01/19/2024]
Abstract
Continuous electroencephalogram (cEEG) plays a crucial role in monitoring and postoperative evaluation of critical patients with extensive EEG abnormalities. Recently, the temporal variability of dynamic resting-state functional connectivity has emerged as a novel approach to understanding the pathophysiological mechanisms underlying diseases. However, little is known about the underlying temporal variability of functional connections in critical patients admitted to neurology intensive care unit (NICU). Furthermore, considering the emerging field of network physiology that emphasizes the integrated nature of human organisms, we hypothesize that this temporal variability in brain activity may be potentially linked to other physiological functions. Therefore, this study aimed to investigate network variability using fuzzy entropy in 24-hour dynamic resting-state networks of critical patients in NICU, with an emphasis on exploring spatial topology changes over time. Our findings revealed both atypical flexible and robust architectures in critical patients. Specifically, the former exhibited denser functional connectivity across the left frontal and left parietal lobes, while the latter showed predominantly short-range connections within anterior regions. These patterns of network variability deviating from normality may underlie the altered network integrity leading to loss of consciousness and cognitive impairment observed in these patients. Additionally, we explored changes in 24-hour network properties and found simultaneous decreases in brain efficiency, heart rate, and blood pressure between approximately 1 pm and 5 pm. Moreover, we observed a close relationship between temporal variability of resting-state network properties and other physiological indicators including heart rate as well as liver and kidney function. These findings suggest that the application of a temporal variability-based cEEG analysis method offers valuable insights into underlying pathophysiological mechanisms of critical patients in NICU, and may present novel avenues for their condition monitoring, intervention, and treatment.
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Affiliation(s)
- Chunli Chen
- 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
| | - Zhaojin Chen
- 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
| | - Meiling Hu
- Clinical Medical College of Chengdu Medical College, Chengdu 610500, People's Republic of China; The First Affiliated Hospital of Chengdu Medical College, Chengdu 610599, People's Republic of China
| | - Sha Zhou
- Clinical Medical College of Chengdu Medical College, Chengdu 610500, People's Republic of China; The First Affiliated Hospital of Chengdu Medical College, Chengdu 610599, People's Republic of China
| | - Shiyun 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
| | - Guan Zhou
- 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
| | - Jixuan Zhou
- 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
| | - Yuqin 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
| | - Baodan Chen
- 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
| | - 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
| | - 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
| | - Yizhou Liu
- Clinical Medical College of Chengdu Medical College, Chengdu 610500, People's Republic of China; The First Affiliated Hospital of Chengdu Medical College, Chengdu 610599, People's Republic of China
| | - Simeng Su
- Clinical Medical College of Chengdu Medical College, Chengdu 610500, People's Republic of China; The First Affiliated Hospital of Chengdu Medical College, Chengdu 610599, People's Republic of China
| | - 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.
| | - Xuntai Ma
- Clinical Medical College of Chengdu Medical College, Chengdu 610500, People's Republic of China; The First Affiliated Hospital of Chengdu Medical College, Chengdu 610599, People's Republic of China.
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15
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McFadden J. Carving Nature at Its Joints: A Comparison of CEMI Field Theory with Integrated Information Theory and Global Workspace Theory. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1635. [PMID: 38136515 PMCID: PMC10743215 DOI: 10.3390/e25121635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 11/29/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023]
Abstract
The quest to comprehend the nature of consciousness has spurred the development of many theories that seek to explain its underlying mechanisms and account for its neural correlates. In this paper, I compare my own conscious electromagnetic information field (cemi field) theory with integrated information theory (IIT) and global workspace theory (GWT) for their ability to 'carve nature at its joints' in the sense of predicting the entities, structures, states and dynamics that are conventionally recognized as being conscious or nonconscious. I go on to argue that, though the cemi field theory shares features of both integrated information theory and global workspace theory, it is more successful at carving nature at its conventionally accepted joints between conscious and nonconscious systems, and is thereby a more successful theory of consciousness.
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Affiliation(s)
- Johnjoe McFadden
- Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, UK
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16
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Velichko A, Boriskov P, Belyaev M, Putrolaynen V. A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science. SENSORS (BASEL, SWITZERLAND) 2023; 23:7137. [PMID: 37631674 PMCID: PMC10458403 DOI: 10.3390/s23167137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 08/03/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023]
Abstract
The study presents a bio-inspired chaos sensor model based on the perceptron neural network for the estimation of entropy of spike train in neurodynamic systems. After training, the sensor on perceptron, having 50 neurons in the hidden layer and 1 neuron at the output, approximates the fuzzy entropy of a short time series with high accuracy, with a determination coefficient of R2~0.9. The Hindmarsh-Rose spike model was used to generate time series of spike intervals, and datasets for training and testing the perceptron. The selection of the hyperparameters of the perceptron model and the estimation of the sensor accuracy were performed using the K-block cross-validation method. Even for a hidden layer with one neuron, the model approximates the fuzzy entropy with good results and the metric R2~0.5 ÷ 0.8. In a simplified model with one neuron and equal weights in the first layer, the principle of approximation is based on the linear transformation of the average value of the time series into the entropy value. An example of using the chaos sensor on spike train of action potential recordings from the L5 dorsal rootlet of rat is provided. The bio-inspired chaos sensor model based on an ensemble of neurons is able to dynamically track the chaotic behavior of a spike signal and transmit this information to other parts of the neurodynamic model for further processing. The study will be useful for specialists in the field of computational neuroscience, and also to create humanoid and animal robots, and bio-robots with limited resources.
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Affiliation(s)
- Andrei Velichko
- Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin str., 185910 Petrozavodsk, Russia; (P.B.); (M.B.); (V.P.)
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Wang J, Liang S, Zhang J, Wu Y, Zhang L, Gao R, He D, Shi CJR. EEG Signal Epilepsy Detection With a Weighted Neighbor Graph Representation and Two-Stream Graph-Based Framework. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3176-3187. [PMID: 37506006 DOI: 10.1109/tnsre.2023.3299839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2023]
Abstract
Epilepsy is one of the most common neurological diseases. Clinically, epileptic seizure detection is usually performed by analyzing electroencephalography (EEG) signals. At present, deep learning models have been widely used for single-channel EEG signal epilepsy detection, but this method is difficult to explain the classification results. Researchers have attempted to solve interpretive problems by combining graph representation of EEG signals with graph neural network models. Recently, the combination of graph representations and graph neural network (GNN) models has been increasingly applied to single-channel epilepsy detection. By this methodology, the raw EEG signal is transformed to its graph representation, and a GNN model is used to learn latent features and classify whether the data indicates an epileptic seizure episode. However, existing methods are faced with two major challenges. First, existing graph representations tend to have high time complexity as they generally require each vertex to traverse all other vertices to construct a graph structure. Some of them also have high space complexity for being dense. Second, while separate graph representations can be derived from a single-channel EEG signal in both time and frequency domains, existing GNN models for epilepsy detection can learn from a single graph representation, which makes it hard to let the information from the two domains complement each other. For addressing these challenges, we propose a Weighted Neighbour Graph (WNG) representation for EEG signals. Reducing the redundant edges of the existing graph, WNG can be both time and space-efficient, and as informative as its less efficient counterparts. We then propose a two-stream graph-based framework to simultaneously learn features from WNG in both time and frequency domain. Extensive experiments demonstrate the effectiveness and efficiency of the proposed methods.
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18
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Lemoine É, Toffa D, Pelletier-Mc Duff G, Xu AQ, Jemel M, Tessier JD, Lesage F, Nguyen DK, Bou Assi E. Machine-learning for the prediction of one-year seizure recurrence based on routine electroencephalography. Sci Rep 2023; 13:12650. [PMID: 37542101 PMCID: PMC10403587 DOI: 10.1038/s41598-023-39799-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 07/31/2023] [Indexed: 08/06/2023] Open
Abstract
Predicting seizure recurrence risk is critical to the diagnosis and management of epilepsy. Routine electroencephalography (EEG) is a cornerstone of the estimation of seizure recurrence risk. However, EEG interpretation relies on the visual identification of interictal epileptiform discharges (IEDs) by neurologists, with limited sensitivity. Automated processing of EEG could increase its diagnostic yield and accessibility. The main objective was to develop a prediction model based on automated EEG processing to predict one-year seizure recurrence in patients undergoing routine EEG. We retrospectively selected a consecutive cohort of 517 patients undergoing routine EEG at our institution (training set) and a separate, temporally shifted cohort of 261 patients (testing set). We developed an automated processing pipeline to extract linear and non-linear features from the EEGs. We trained machine learning algorithms on multichannel EEG segments to predict one-year seizure recurrence. We evaluated the impact of IEDs and clinical confounders on performances and validated the performances on the testing set. The receiver operating characteristic area-under-the-curve for seizure recurrence after EEG in the testing set was 0.63 (95% CI 0.55-0.71). Predictions were still significantly above chance in EEGs with no IEDs. Our findings suggest that there are changes other than IEDs in the EEG signal embodying seizure propensity.
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Affiliation(s)
- Émile Lemoine
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Denahin Toffa
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Geneviève Pelletier-Mc Duff
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - An Qi Xu
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Mezen Jemel
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Jean-Daniel Tessier
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Frédéric Lesage
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montréal, Qc, Canada
- Centre de Recherche de l'institut de Cardiologie de Montréal, Montréal, Qc, Canada
| | - Dang K Nguyen
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Elie Bou Assi
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada.
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada.
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Sadoun MSN, Ur Rahman MM, Al-Naffouri T, Laleg-Kirati TM. EEG Epileptic Data Classification Using the Schrodinger Operator's Spectrum. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-6. [PMID: 38083329 DOI: 10.1109/embc40787.2023.10340881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Epilepsy is a common brain disorder characterized by recurrent, unprovoked seizures which affects over 65 million people. Visual inspection of Electroencephalograms (EEG) is common for diagnosis; however, it requires time and expertise. Therefore, an accurate computer-aided epileptic seizure diagnosis system would be valuable. A new research tendency when tackling epileptic seizure detection tends towards minimizing human manual intervention by designing frameworks with autonomous feature engineering. In this optic, this paper proposes a new approach for EEG epileptic data classification. Features derived from the Semi-Classical Signal Analysis (SCSA) method, a quantum-inspired signal processing method well-suited for the characterization of pulse-shaped physiological signals, are proposed. In addition nonlinear dynamical features that proved efficient in characterizing nonlinear dynamics of neural activity have been extracted. Moreover, hyperparameters' optimization, correlation analysis and feature selection have been performed. The selected features are fed into five different machine learning classifiers. The performance of the proposed approach has been analyzed using Bonn university database. The results show that all classifiers yield a performance accuracy of 93% and above.Clinical relevance- The paper contributes to the design of methods and algorithms to build reliable software solutions to assist medical experts and reduce epilepsy disease's diagnosis time and errors.
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20
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Mohammed Hussein R, George LE, Sabar Miften F. Accurate method for sleep stages classification using discriminated features and single EEG channel. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
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21
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Guo F, Li Y, Jian Z, Cui Y, Gong W, Li A, Jing W, Xu P, Chen K, Guo D, Yao D, Xia Y. Dose-related adaptive reconstruction of DMN in isoflurane administration: a study in the rat. BMC Anesthesiol 2023; 23:224. [PMID: 37380958 PMCID: PMC10303294 DOI: 10.1186/s12871-023-02153-6] [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: 01/13/2023] [Accepted: 05/26/2023] [Indexed: 06/30/2023] Open
Abstract
BACKGROUND The anesthetic states are accompanied by functional alterations. However, the dose-related adaptive alterations in the higher-order network under anesthesia, e. g. default mode network (DMN), are poorly revealed. METHODS We implanted electrodes in brain regions of the rat DMN to acquire local field potentials to investigate the perturbations produced by anesthesia. Relative power spectral density, static functional connectivity (FC), fuzzy entropy of dynamic FC, and topological features were computed from the data. RESULTS The results showed that adaptive reconstruction was induced by isoflurane, exhibiting reduced static and stable long-range FC, and altered topological features. These reconstruction patterns were in a dose-related fashion. CONCLUSION These results might impart insights into the neural network mechanisms underlying anesthesia and suggest the potential of monitoring the depth of anesthesia based on the parameters of DMN.
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Affiliation(s)
- Fengru Guo
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Yuqin Li
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Zhaoxin Jian
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Yan Cui
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Wenhui Gong
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Airui Li
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Wei Jing
- Department of Physiology, School of Basic Medicine and Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 4030030 China
| | - Peng Xu
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Ke Chen
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Daqing Guo
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Dezhong Yao
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Yang Xia
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
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22
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A difference attention ResNet-LSTM network for epileptic seizure detection using EEG signal. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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23
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Ein Shoka AA, Dessouky MM, El-Sayed A, Hemdan EED. EEG seizure detection: concepts, techniques, challenges, and future trends. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-31. [PMID: 37362745 PMCID: PMC10071471 DOI: 10.1007/s11042-023-15052-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 08/07/2022] [Accepted: 02/27/2023] [Indexed: 06/28/2023]
Abstract
A central nervous system disorder is usually referred to as epilepsy. In epilepsy brain activity becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of awareness. Consequently, epilepsy patients face problems in daily life due to precautions they must take to adapt to this condition, particularly when they use heavy equipment, e.g., vehicle derivation. Epilepsy studies rely primarily on electroencephalography (EEG) signals to evaluate brain activity during seizures. It is troublesome and time-consuming to manually decide the location of seizures in EEG signals. The automatic detection framework is one of the principal tools to help doctors and patients take appropriate precautions. This paper reviews the epilepsy mentality disorder and the types of seizure, preprocessing operations that are performed on EEG data, a generally extracted feature from the signal, and a detailed view on classification procedures used in this problem and provide insights on the difficulties and future research directions in this innovative theme. Therefore, this paper presents a review of work on recent methods for the epileptic seizure process along with providing perspectives and concepts to researchers to present an automated EEG-based epileptic seizure detection system using IoT and machine learning classifiers for remote patient monitoring in the context of smart healthcare systems. Finally, challenges and open research points in EEG seizure detection are investigated.
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Affiliation(s)
- Athar A. Ein Shoka
- Faculty of Electronic Engineering, Computer Science and Engineering Department, Menoufia University, Menouf, Egypt
| | - Mohamed M. Dessouky
- Faculty of Electronic Engineering, Computer Science and Engineering Department, Menoufia University, Menouf, Egypt
- Department of Computer Science & Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Ayman El-Sayed
- Faculty of Electronic Engineering, Computer Science and Engineering Department, Menoufia University, Menouf, Egypt
| | - Ezz El-Din Hemdan
- Faculty of Electronic Engineering, Computer Science and Engineering Department, Menoufia University, Menouf, Egypt
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24
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Sun Y, Yang C, Xu Z, Lu Y. Recurrence Plot-Assisted Detection of Focal/Non-focal EEG Signals Using Ensemble Deep Features. J Med Biol Eng 2023. [DOI: 10.1007/s40846-023-00785-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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25
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Goshvarpour A, Goshvarpour A. An Innovative Information-Based Strategy for Epileptic EEG Classification. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11253-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/01/2023]
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26
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Liu B, Bai H, Chen W, Chen H, Zhang Z. Automatic detection method of epileptic seizures based on IRCMDE and PSO-SVM. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:9349-9363. [PMID: 37161246 DOI: 10.3934/mbe.2023410] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Multi-scale dispersion entropy (MDE) has been widely used to extract nonlinear features of electroencephalography (EEG) signals and realize automatic detection of epileptic seizures. However, information loss and poor robustness will exist when MDE is used to measure the nonlinear complexity of the time sequence. To solve the above problems, an automatic detection method for epilepsy was proposed, based on improved refined composite multi-scale dispersion entropy (IRCMDE) and particle swarm algorithm optimization support vector machine (PSO-SVM). First, the refined composite multi-scale dispersion entropy (RCMDE) is introduced, and then the segmented average calculation of coarse-grained sequence is replaced by local maximum calculation to solve the problem of information loss. Finally, the entropy value is normalized to improve the robustness of characteristic parameters, and IRCMDE is formed. The simulated results show that when examining the complexity of the simulated signal, IRCMDE can eliminate the issue of information loss compared with MDE and RCMDE and weaken the entropy change caused by different parameter selections. In addition, IRCMDE is used as the feature parameter of the epileptic EEG signal, and PSO-SVM is used to identify the feature parameters. Compared with MDE-PSO-SVM, and RCMDE-PSO-SVM methods, IRCMDE-PSO-SVM can obtain more accurate recognition results.
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Affiliation(s)
- Bei Liu
- College of Mathematics and Physics, Hunan University of Arts and Science, Changde 415000, China
- Hunan University of Arts and Science, Hunan Province Key Laboratory of Photoelectric Information Integration and Optical Manufacturing Technology, Changde 415000, China
| | - Hongzi Bai
- College of Mathematics and Physics, Hunan University of Arts and Science, Changde 415000, China
| | - Wei Chen
- College of Mathematics and Physics, Hunan University of Arts and Science, Changde 415000, China
| | - Huaquan Chen
- College of Mathematics and Physics, Hunan University of Arts and Science, Changde 415000, China
| | - Zhen Zhang
- Furong College, Hunan University of Arts and Science, Changde 415000, China
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27
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Alhassan S, Soudani A, Almusallam M. Energy-Efficient EEG-Based Scheme for Autism Spectrum Disorder Detection Using Wearable Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:2228. [PMID: 36850829 PMCID: PMC9962521 DOI: 10.3390/s23042228] [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: 12/29/2022] [Revised: 02/06/2023] [Accepted: 02/15/2023] [Indexed: 06/15/2023]
Abstract
The deployment of wearable wireless systems that collect physiological indicators to aid in diagnosing neurological disorders represents a potential solution for the new generation of e-health systems. Electroencephalography (EEG), a recording of the brain's electrical activity, is a promising physiological test for the diagnosis of autism spectrum disorders. It can identify the abnormalities of the neural system that are associated with autism spectrum disorders. However, streaming EEG samples remotely for classification can reduce the wireless sensor's lifespan and creates doubt regarding the application's feasibility. Therefore, decreasing data transmission may conserve sensor energy and extend the lifespan of wireless sensor networks. This paper suggests the development of a sensor-based scheme for early age autism detection. The proposed scheme implements an energy-efficient method for signal transformation allowing relevant feature extraction for accurate classification using machine learning algorithms. The experimental results indicate an accuracy of 96%, a sensitivity of 100%, and around 95% of F1 score for all used machine learning models. The results also show that our scheme energy consumption is 97% lower than streaming the raw EEG samples.
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Affiliation(s)
- Sarah Alhassan
- Department of Computer Science, College of Computer and Information Science, King Saud University, Riyadh 11362, Saudi Arabia
- Department of Computer Science, College of Computer and Information Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia
| | - Adel Soudani
- Department of Computer Science, College of Computer and Information Science, King Saud University, Riyadh 11362, Saudi Arabia
| | - Manan Almusallam
- Department of Computer Science, College of Computer and Information Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia
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28
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Cimr D, Fujita H, Tomaskova H, Cimler R, Selamat A. Automatic seizure detection by convolutional neural networks with computational complexity analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107277. [PMID: 36463672 DOI: 10.1016/j.cmpb.2022.107277] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/20/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVES Nowadays, an automated computer-aided diagnosis (CAD) is an approach that plays an important role in the detection of health issues. The main advantages should be in early diagnosis, including high accuracy and low computational complexity without loss of the model performance. One of these systems type is concerned with Electroencephalogram (EEG) signals and seizure detection. We designed a CAD system approach for seizure detection that optimizes the complexity of the required solution while also being reusable on different problems. METHODS The methodology is built-in deep data analysis for normalization. In comparison to previous research, the system does not necessitate a feature extraction process that optimizes and reduces system complexity. The data classification is provided by a designed 8-layer deep convolutional neural network. RESULTS Depending on used data, we have achieved the accuracy, specificity, and sensitivity of 98%, 98%, and 98.5% on the short-term Bonn EEG dataset, and 96.99%, 96.89%, and 97.06% on the long-term CHB-MIT EEG dataset. CONCLUSIONS Through the approach to detection, the system offers an optimized solution for seizure diagnosis health problems. The proposed solution should be implemented in all clinical or home environments for decision support.
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Affiliation(s)
- Dalibor Cimr
- Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic
| | - Hamido Fujita
- Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Vietnam; DaSCI Andalusian Institute of Data Science and Computational Intelligence, University of Granada, Granada, Spain; Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia; Regional Research Center, Iwate Prefectural University, Iwate Japan.
| | - Hana Tomaskova
- Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic
| | - Richard Cimler
- Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
| | - Ali Selamat
- Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
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29
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Goel S, Agrawal R, Bharti R. Epileptic seizure prediction and classification based on statistical features using LSTM fully connected neural network. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-222745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Epilepsy, the most common neurological disorder by which over 65 million people are affected across the world. Recent research has shown a very large interest to predict and diagnose epilepsy well before time. The continuous monitoring of EEG signals for seizure detection in electroencephalogram (EEG) is a very tedious and time taking process and therefore requires a qualified and trained clinical specialist. This paper presents a novel approach to detect and predict the epileptic signal in the recorded electroencephalogram (EEG). There is always a requirement for a nonlinear technique to examine the EEG signals due to the random nature of EEG signals. Therefore, we are providing an alternate method that extracts various entropy measures such Sample Entropy, Spectral Entropy, Permutation Entropy, and Shannon Entropy as statistical features from EEG signal. Based on these extracted features LSTM Fully connected Neural Network is used to classify the EEG signal as Focal and Non-focal. The proposed method gives a new insight into EEG signals by providing sensitivity as an added measure using deep learning along with accuracy and precision.
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Affiliation(s)
- Sachin Goel
- Veer Madho Singh Bhandari Uttarakhand Technical University, Dehradun, India
| | - Rajeev Agrawal
- Lloyd Institute of Engineering & Technology, Greater Noida, India
| | - R.K. Bharti
- Bipin Tripathi Kumaon Institute of Technology, Dwarahat, Uttarakhand, India
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30
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Han Y, Bai Y, Liu Q, Zhao Y, Chen T, Wang W, Ni G. Assessing vestibular function using electroencephalogram rhythms evoked during the caloric test. Front Neurol 2023; 14:1126214. [PMID: 36908620 PMCID: PMC9996014 DOI: 10.3389/fneur.2023.1126214] [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: 12/17/2022] [Accepted: 01/31/2023] [Indexed: 02/25/2023] Open
Abstract
Introduction The vestibular system is responsible for motion perception and balance preservation in the body. The vestibular function examination is useful for determining the cause of associated symptoms, diagnosis, and therapy of the patients. The associated cerebral cortex processes and integrates information and is the ultimate perceptual site for vestibular-related symptoms. In recent clinical examinations, less consideration has been given to the cortex associated with the vestibular system. As a result, it is crucial to increase focus on the expression of the cortical level while evaluating vestibular function. From the viewpoint of neuroelectrophysiology, electroencephalograms (EEG) can enhance the assessments of vestibular function at the cortex level. Methods This study recorded nystagmus and EEG data throughout the caloric test. Four phases were considered according to the vestibular activation status: before activation, activation, fixation suppression, and recovery. In different phases, the distribution and changes of the relative power of the EEG rhythms (delta, theta, alpha, and beta) were analyzed, and the correlation between EEG characteristics and nystagmus was also investigated. Results The results showed that, when the vestibule was activated, the alpha power of the occipital region increased, and the beta power of the central and top regions and the occipital region on the left decreased. The changes in the alpha and beta rhythms significantly correlate with nystagmus values in left warm stimulation. Discussion Our findings offer a fresh perspective on cortical electrophysiology for the assessment of vestibular function by demonstrating that the relative power change in EEG rhythms can be used to assess vestibular function.
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Affiliation(s)
- Yutong Han
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, China
| | - Yanru Bai
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, China
| | - Qiang Liu
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China.,Institute of Otolaryngology of Tianjin, Tianjin, China.,Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
| | - Yuncheng Zhao
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, China.,Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Taisheng Chen
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China.,Institute of Otolaryngology of Tianjin, Tianjin, China.,Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
| | - Wei Wang
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China.,Institute of Otolaryngology of Tianjin, Tianjin, China.,Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
| | - Guangjian Ni
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, China
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31
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Chawla P, Rana SB, Kaur H, Singh K, Yuvaraj R, Murugappan M. A decision support system for automated diagnosis of Parkinson’s disease from EEG using FAWT and entropy features. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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32
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Mishra S, Kumar Satapathy S, Mohanty SN, Pattnaik CR. A DM-ELM based classifier for EEG brain signal classification for epileptic seizure detection. Commun Integr Biol 2022; 16:2153648. [PMID: 36531748 PMCID: PMC9757406 DOI: 10.1080/19420889.2022.2153648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Epilepsy is one of the dreaded conditions that had taken billions of people under its cloud worldwide. Detecting the seizure at the correct time in an individual is something that medical practitioners focus in order to help people save their lives. Analysis of the Electroencephalogram (EEG) signal from the scalp area of the human brain can help in detecting the seizure beforehand. This paper presents a novel classification technique to classify EEG brain signals for epilepsy identification based on Discrete Wavelet Transform and Moth Flame Optimization-based Extreme Learning Machine (DM-ELM). ELM is a very popular machine learning method based on Neural Networks (NN) where the model is trained rigorously to get the minimized error rate and maximized accuracy. Here we have used several experimental evaluations to compare the performance of basic ELM and DM-ELM and it has been experimentally proved that DM-ELM outperforms basic ELM but with few time constraints.
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Affiliation(s)
- Shruti Mishra
- Department of Computer Science & Engineering, Vellore Institute of Technology, Chennai, india
| | - Sandeep Kumar Satapathy
- Department of Computer Science & Engineering, Vellore Institute of Technology, Chennai, india
| | - Sachi Nandan Mohanty
- School of Computer Science &Engineering, VIT-AP University, Amaravati, India,CONTACT Sachi Nandan Mohanty School of Computer Science &Engineering, VIT-AP University, Amaravati, India
| | - Chinmaya Ranjan Pattnaik
- Department of Computer Science & Engineering, Ajay Binaya Institute of Technology, Cuttack, India
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33
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Nafea MS, Ismail ZH. Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals-A Systematic Literature Review. Bioengineering (Basel) 2022; 9:781. [PMID: 36550987 PMCID: PMC9774931 DOI: 10.3390/bioengineering9120781] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 12/13/2022] Open
Abstract
Electroencephalography (EEG) is a complicated, non-stationary signal that requires extensive preprocessing and feature extraction approaches to be accurately analyzed. In recent times, Deep learning (DL) has shown great promise in exploiting the characteristics of EEG signals as it can learn relevant features from raw data autonomously. Although studies involving DL have become more common in the last two years, the topic of whether DL truly delivers advantages over conventional Machine learning (ML) methodologies remains unsettled. This study aims to present a detailed overview of the main challenges in the field of seizure detection, prediction, and classification utilizing EEG data, and the approaches taken to solve them using ML and DL methods. A systematic review was conducted surveying peer-reviewed publications published between 2017 and 16 July 2022 using two scientific databases (Web of Science and Scopus) totaling 6822 references after discarding duplicate publications. Whereas 2262 articles were screened based on the title, abstract, and keywords, only 214 were eligible for full-text assessment. A total of 91 papers have been included in this survey after meeting the eligible inclusion and exclusion criteria. The most significant findings from the review are summarized, and several important concepts involving ML and DL for seizure detection, prediction, and classification are discussed in further depth. This review aims to learn more about the different approaches for identifying different types and stages of epileptic seizures, which may then be employed to enhance the lives of epileptic patients in the future, as well as aid experts in the field.
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Affiliation(s)
- Mohamed Sami Nafea
- Computer Engineering Department, College of Engineering and Technology, Arab Academy for Science and Technology (AAST), Cairo 2033, Egypt
- Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia
| | - Zool Hilmi Ismail
- Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia
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34
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Zanetti R, Pale U, Teijeiro T, Atienza D. Approximate zero-crossing: a new interpretable, highly discriminative and low-complexity feature for EEG and iEEG seizure detection. J Neural Eng 2022; 19. [PMID: 36356314 DOI: 10.1088/1741-2552/aca1e4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 11/10/2022] [Indexed: 11/12/2022]
Abstract
Objective. Long-term monitoring of people with epilepsy based on electroencephalography (EEG) and intracranial EEG (iEEG) has the potential to deliver key clinical information for personalised epilepsy treatment. More specifically, in outpatient settings, the available solutions are not satisfactory either due to poor classification performance or high complexity to be executed in resource-constrained devices (e.g. wearable systems). Therefore, we hypothesize that obtaining high discriminative features is the main avenue to improve low-complexity seizure-detection algorithms.Approach. Inspired by how neurologists recognize ictal EEG data, and to tackle this problem by targeting resource-constrained wearable devices, we introduce a new interpretable and highly discriminative feature for EEG and iEEG, namely approximate zero-crossing (AZC). We obtain AZC by applying a polygonal approximation to mimic how our brain selects prominent patterns among noisy data and then using a zero-crossing count as a measure of the dominating frequency. By employing Kullback-Leiber divergence, leveraging CHB-MIT and SWEC-ETHZ iEEG datasets, we compare the AZC discriminative power against a set of 56 classical literature features (CLF). Moreover, we assess the performances of a low-complexity seizure detection method using only AZC features versus employing the CLF set.Main results. Three AZC features obtained with different approximation thresholds are among the five with the highest median discriminative power. Moreover, seizure classification based on only AZC features outperforms an equivalent CLF-based method. The former detects 102 and 194 seizures, against 99 and 161 for the latter (CHB-MIT and SWEC-ETHZ, respectively). Moreover, the AZC-based method keeps a similar false-alarm rate (i.e. an average of 2.1 and 1.0, against 2.0 and 0.5, per day).Significance. We propose a new feature and demonstrate its capability in seizure classification for both scalp and intracranial EEG. We envision the use of such a feature to improve outpatient monitoring with resource-constrained devices.
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Affiliation(s)
- R Zanetti
- Embedded Systems Laboratory (ESL), École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - U Pale
- Embedded Systems Laboratory (ESL), École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - T Teijeiro
- Embedded Systems Laboratory (ESL), École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.,Department of Mathematics, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - D Atienza
- Embedded Systems Laboratory (ESL), École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
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35
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Exploiting deterministic features in apparently stochastic data. Sci Rep 2022; 12:19843. [PMID: 36400910 PMCID: PMC9674651 DOI: 10.1038/s41598-022-23212-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 10/26/2022] [Indexed: 11/19/2022] Open
Abstract
Many processes in nature are the result of many coupled individual subsystems (like population dynamics or neurosystems). Not always such systems exhibit simple stable behaviors that in the past science has mostly focused on. Often, these systems are characterized by bursts of seemingly stochastic activity, interrupted by quieter periods. The hypothesis is that the presence of a strong deterministic ingredient is often obscured by the stochastic features. We test this by modeling classically stochastic considered real-world data from both, the stochastic as well as the deterministic approaches to find that the deterministic approach's results level with those from the stochastic side. Moreover, the deterministic approach is shown to reveal the full dynamical systems landscape, which can be exploited for steering the dynamics into a desired regime.
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36
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Schizophrenia Diagnosis by Weighting the Entropy Measures of the Selected EEG Channel. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00762-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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37
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Azami H, Sanei S, Rajji TK. Ensemble entropy: A low bias approach for data analysis. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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38
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Aung ST, Hassan M, Brady M, Mannan ZI, Azam S, Karim A, Zaman S, Wongsawat Y. Entropy-Based Emotion Recognition from Multichannel EEG Signals Using Artificial Neural Network. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6000989. [PMID: 36275950 PMCID: PMC9584707 DOI: 10.1155/2022/6000989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 09/22/2022] [Indexed: 11/17/2022]
Abstract
Humans experience a variety of emotions throughout the course of their daily lives, including happiness, sadness, and rage. As a result, an effective emotion identification system is essential for electroencephalography (EEG) data to accurately reflect emotion in real-time. Although recent studies on this problem can provide acceptable performance measures, it is still not adequate for the implementation of a complete emotion recognition system. In this research work, we propose a new approach for an emotion recognition system, using multichannel EEG calculation with our developed entropy known as multivariate multiscale modified-distribution entropy (MM-mDistEn) which is combined with a model based on an artificial neural network (ANN) to attain a better outcome over existing methods. The proposed system has been tested with two different datasets and achieved better accuracy than existing methods. For the GAMEEMO dataset, we achieved an average accuracy ± standard deviation of 95.73% ± 0.67 for valence and 96.78% ± 0.25 for arousal. Moreover, the average accuracy percentage for the DEAP dataset reached 92.57% ± 1.51 in valence and 80.23% ± 1.83 in arousal.
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Affiliation(s)
- Si Thu Aung
- Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Salaya, Thailand
| | - Mehedi Hassan
- Computer Science and Engineering, North Western University, Khulna, Bangladesh
| | - Mark Brady
- Asia Pacific College of Business and Law, Charles Darwin University, Casuarina, NT, Australia
| | - Zubaer Ibna Mannan
- Department of Smart Computing, Kyungdong University, Global Campus, Goseong-Gun, Republic of Korea
| | - Sami Azam
- College of Engineering IT and Environment, Charles Darwin University, Casuarina, NT, Australia
| | - Asif Karim
- College of Engineering IT and Environment, Charles Darwin University, Casuarina, NT, Australia
| | - Sadika Zaman
- Computer Science and Engineering, North Western University, Khulna, Bangladesh
| | - Yodchanan Wongsawat
- Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Salaya, Thailand
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39
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Hsieh JC, Alawieh H, Li Y, Iwane F, Zhao L, Anderson R, Abdullah S, Kevin Tang KW, Wang W, Pyatnitskiy I, Jia Y, Millán JDR, Wang H. A highly stable electrode with low electrode-skin impedance for wearable brain-computer interface. Biosens Bioelectron 2022; 218:114756. [DOI: 10.1016/j.bios.2022.114756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 09/19/2022] [Accepted: 09/23/2022] [Indexed: 11/17/2022]
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40
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Morteza Ghazali S, Alizadeh M, Mazloum J, Baleghi Y. Modified binary salp swarm algorithm in EEG signal classification for epilepsy seizure detection. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103858] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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41
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Suhail T, Indiradevi K, Suhara E, Poovathinal SA, Ayyappan A. Distinguishing cognitive states using electroencephalography local activation and functional connectivity patterns. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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42
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N.J. S, M.S.P. S, S. TG. EEG-based classification of normal and seizure types using relaxed local neighbour difference pattern and artificial neural network. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108508] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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43
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From chaos to clock in recurrent neural net. Case study. Biosystems 2022; 220:104739. [PMID: 35868428 DOI: 10.1016/j.biosystems.2022.104739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 07/14/2022] [Accepted: 07/14/2022] [Indexed: 11/24/2022]
Abstract
What is the reason for complex dynamical patterns registered from real biological neuronal networks? Noise and dynamical reconfiguring of a network (functional/dynamic connectome) were proposed as possible answers. In this case study, we report a complex dynamical pattern observed in a simple deterministic network of 25 excitatory neurons with fixed connectome. After a short initial stimulation, the network is engaged into a complex dynamics, which lasts for a long time. Eventually, with no external intervention, the dynamics comes to a periodic one with a short period. The long transient is positively checked for being chaotic. We conclude that the complex dynamics observed is the output of neural computation performed in the process of neuronal firings and spikes propagation.
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A Bayesian Dynamic Inference Approach Based on Extracted Gray Level Co-Occurrence (GLCM) Features for the Dynamical Analysis of Congestive Heart Failure. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136350] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The adoptability of the heart to external and internal stimuli is reflected by heart rate variability (HRV). Reduced HRV can be a predictor of post-infarction mortality. In this study, we propose an automated system to predict and diagnose congestive heart failure using short-term heart rate variability analysis. Based on the nonlinear, nonstationary, and highly complex dynamics of congestive heart failure, we extracted multimodal features to capture the temporal, spectral, and complex dynamics. Recently, the Bayesian inference approach has been recognized as an attractive option for the deeper analysis of static features, in order to perform a comprehensive analysis of extracted nodes (features). We computed the gray level co-occurrence (GLCM) features from congestive heart failure signals and then ranked them based on ROC methods. This study focused on utilizing the dissimilarity feature, which is ranked as highly important, as a target node for the empirical analysis of dynamic profiling and optimization, in order to explain the nonlinear dynamics of GLCM features extracted from heart failure signals, and distinguishing CHF from NSR. We applied Bayesian inference and Pearson’s correlation (PC). The association, in terms of node force and mapping, was computed. The higher-ranking target node was used to compute the posterior probability, total effect, arc contribution, network profile, and compression. The highest value of ROC was obtained for dissimilarity, at 0.3589. Based on the information-gain algorithm, the highest strength of the relationship was obtained between nodes “dissimilarity” and “cluster performance” (1.0146), relative to mutual information (81.33%). Moreover, the highest relative binary significance was yielded for dissimilarity for 1/3rd (80.19%), 2/3rd (74.95%) and 3/3rd (100%). The results revealed that the proposed methodology can provide further in-depth insights for the early diagnosis and prognosis of congestive heart failure.
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Yang H, Huang S, Guo S, Sun G. Multi-Classifier Fusion Based on MI–SFFS for Cross-Subject Emotion Recognition. ENTROPY 2022; 24:e24050705. [PMID: 35626587 PMCID: PMC9141183 DOI: 10.3390/e24050705] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 05/09/2022] [Accepted: 05/10/2022] [Indexed: 02/01/2023]
Abstract
With the widespread use of emotion recognition, cross-subject emotion recognition based on EEG signals has become a hot topic in affective computing. Electroencephalography (EEG) can be used to detect the brain’s electrical activity associated with different emotions. The aim of this research is to improve the accuracy by enhancing the generalization of features. A Multi-Classifier Fusion method based on mutual information with sequential forward floating selection (MI_SFFS) is proposed. The dataset used in this paper is DEAP, which is a multi-modal open dataset containing 32 EEG channels and multiple other physiological signals. First, high-dimensional features are extracted from 15 EEG channels of DEAP after using a 10 s time window for data slicing. Second, MI and SFFS are integrated as a novel feature-selection method. Then, support vector machine (SVM), k-nearest neighbor (KNN) and random forest (RF) are employed to classify positive and negative emotions to obtain the output probabilities of classifiers as weighted features for further classification. To evaluate the model performance, leave-one-out cross-validation is adopted. Finally, cross-subject classification accuracies of 0.7089, 0.7106 and 0.7361 are achieved by the SVM, KNN and RF classifiers, respectively. The results demonstrate the feasibility of the model by splicing different classifiers’ output probabilities as a portion of the weighted features.
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Affiliation(s)
- Haihui Yang
- College of Electronic Engineering, Heilongjiang University, Harbin 150080, China; (H.Y.); (S.H.); (S.G.)
- Key Laboratory of Information Fusion Estimation and Detection, Harbin 150080, China
| | - Shiguo Huang
- College of Electronic Engineering, Heilongjiang University, Harbin 150080, China; (H.Y.); (S.H.); (S.G.)
- Key Laboratory of Information Fusion Estimation and Detection, Harbin 150080, China
| | - Shengwei Guo
- College of Electronic Engineering, Heilongjiang University, Harbin 150080, China; (H.Y.); (S.H.); (S.G.)
- Key Laboratory of Information Fusion Estimation and Detection, Harbin 150080, China
| | - Guobing Sun
- College of Electronic Engineering, Heilongjiang University, Harbin 150080, China; (H.Y.); (S.H.); (S.G.)
- Key Laboratory of Information Fusion Estimation and Detection, Harbin 150080, China
- Correspondence: ; Tel.: +86-18946119665
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Gómez-Tapia C, Bozic B, Longo L. On the Minimal Amount of EEG Data Required for Learning Distinctive Human Features for Task-Dependent Biometric Applications. Front Neuroinform 2022; 16:844667. [PMID: 35620278 PMCID: PMC9127527 DOI: 10.3389/fninf.2022.844667] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Biometrics is the process of measuring and analyzing human characteristics to verify a given person's identity. Most real-world applications rely on unique human traits such as fingerprints or iris. However, among these unique human characteristics for biometrics, the use of Electroencephalogram (EEG) stands out given its high inter-subject variability. Recent advances in Deep Learning and a deeper understanding of EEG processing methods have led to the development of models that accurately discriminate unique individuals. However, it is still uncertain how much EEG data is required to train such models. This work aims at determining the minimal amount of training data required to develop a robust EEG-based biometric model (+95% and +99% testing accuracies) from a subject for a task-dependent task. This goal is achieved by performing and analyzing 11,780 combinations of training sizes, by employing various neural network-based learning techniques of increasing complexity, and feature extraction methods on the affective EEG-based DEAP dataset. Findings suggest that if Power Spectral Density or Wavelet Energy features are extracted from the artifact-free EEG signal, 1 and 3 s of data per subject is enough to achieve +95% and +99% accuracy, respectively. These findings contributes to the body of knowledge by paving a way for the application of EEG to real-world ecological biometric applications and by demonstrating methods to learn the minimal amount of data required for such applications.
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47
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Automated method for real-time AMD screening of fundus images dedicated for mobile devices. Med Biol Eng Comput 2022; 60:1449-1479. [DOI: 10.1007/s11517-022-02546-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 03/06/2022] [Indexed: 01/01/2023]
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48
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EEG Oscillatory Power and Complexity for Epileptic Seizure Detection. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094181] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Monitoring patients at risk of epileptic seizure is critical for optimal treatment and ensuing the reduction of seizure risk and complications. In general, seizure detection is done manually in hospitals and involves time-consuming visual inspection and interpretation by experts of electroencephalography (EEG) recordings. The purpose of this study is to investigate the pertinence of band-limited spectral power and signal complexity in order to discriminate between seizure and seizure-free EEG brain activity. The signal complexity and spectral power are evaluated in five frequency intervals, namely, the delta, theta, alpha, beta, and gamma bands, to be used as EEG signal feature representation. Classification of seizure and seizure-free data was performed by prevalent potent classifiers. Substantial comparative performance evaluation experiments were performed on a large EEG data record of 341 patients in the Temple University Hospital EEG seizure database. Based on statistically validated criteria, results show the efficiency of band-limited spectral power and signal complexity when using random forest and gradient-boosting decision tree classifiers (95% of the area under the curve (AUC) and 91% for both F-measure and accuracy). These results support the use of these automatic classification schemes to assist the practicing neurologist interpret EEG records more accurately and without tedious visual inspection.
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Fujita Y, Yanagisawa T, Fukuma R, Ura N, Oshino S, Kishima H. Abnormal phase-amplitude coupling characterizes the interictal state in epilepsy. J Neural Eng 2022; 19. [PMID: 35385832 DOI: 10.1088/1741-2552/ac64c4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 04/05/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Diagnosing epilepsy still requires visual interpretation of electroencephalography and magnetoencephalography (MEG) by specialists, which prevents quantification and standardization of diagnosis. Previous studies proposed automated diagnosis by combining various features from electroencephalography and MEG, such as relative power (Power) and functional connectivity. However, the usefulness of interictal phase-amplitude coupling (PAC) in diagnosing epilepsy is still unknown. We hypothesized that resting-state PAC would be different for patients with epilepsy in the interictal state and for healthy participants such that it would improve discrimination between the groups. METHODS We obtained resting-state MEG and magnetic resonance imaging in 90 patients with epilepsy during their preoperative evaluation and in 90 healthy participants. We used the cortical currents estimated from MEG and magnetic resonance imaging to calculate Power in the δ (1-3 Hz), θ (4-7 Hz), α (8-13 Hz), β (13-30 Hz), low γ (35-55 Hz), and high γ (65-90 Hz) bands and functional connectivity in the θ band. PAC was evaluated using the synchronization index (SI) for eight frequency band pairs: the phases of δ, θ, α, and β and the amplitudes of low and high γ. First, we compared the mean SI values for the patients with epilepsy and the healthy participants. Then, using features such as PAC, Power, functional connectivity, and features extracted by deep learning individually or combined, we tested whether PAC improves discrimination accuracy for the two groups. RESULTS The mean SI values were significantly different for the patients with epilepsy and the healthy participants. The SI value difference was highest for θ/low γ in the temporal lobe. Discrimination accuracy was the highest, at 90%, using the combination of PAC and deep learning. SIGNIFICANCE Abnormal PAC characterized the patients with epilepsy in the interictal state compared with the healthy participants, potentially improving the discrimination of epilepsy.
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Affiliation(s)
- Yuya Fujita
- Institute for Advanced co-creation studies, Osaka University, 2-2 Yamadaoka Suita Osaka Japan, Suita, 565-0871, JAPAN
| | - Takufumi Yanagisawa
- Institute for Advanced co-creation studies, Osaka University, 2-2 Yamadaoka Suita Osaka Japan, Suita, 565-0871, JAPAN
| | - Ryohei Fukuma
- Institute for Advanced co-creation studies, Osaka University, 2-2 Yamadaoka Suita Osaka Japan, Suita, 565-0871, JAPAN
| | - Natsuko Ura
- Institute for Advanced co-creation studies, Osaka University, 2-2 Yamadaoka Suita Osaka Japan, Suita, 565-0871, JAPAN
| | - Satoru Oshino
- Department of Neurosurgery, Osaka University Faculty of Medicine Graduate School of Medicine, 2-2 Yamadaoka, suita, Osaka, Japan, Osaka University Graduate School of Medicine, Dept of Neurosurgery, Osaka, Osaka, 5670871, JAPAN
| | - Haruhiko Kishima
- Department of neurosurgery, Osaka University, 2-2, Yamadaoka, Suita, Suita, Osaka, 5650871, JAPAN
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Jiang L, Wang J, Dai J, Li F, Chen B, He R, Liao Y, Yao D, Dong W, Xu P. Altered temporal variability in brain functional connectivity identified by fuzzy entropy underlines schizophrenia deficits. J Psychiatr Res 2022; 148:315-324. [PMID: 35193035 DOI: 10.1016/j.jpsychires.2022.02.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 01/13/2022] [Accepted: 02/14/2022] [Indexed: 11/18/2022]
Abstract
Investigation of the temporal variability of resting-state brain networks informs our understanding of how neural connectivity aggregates and disassociates over time, further shedding light on the aberrant neural interactions that underlie symptomatology and psychosis development. In the current work, an electroencephalogram-based sliding window analysis was utilized for the first time to measure the nonlinear complexity of dynamic resting-state brain networks of schizophrenia (SZ) patients by applying fuzzy entropy. The results of this study demonstrated the attenuated temporal variability among multiple electrodes that were distributed in the frontal and right parietal lobes for SZ patients when compared with healthy controls (HCs). Meanwhile, a concomitant strengthening of the posterior and peripheral flexible connections that may be attributed to the excessive alertness or sensitivity of SZ patients to the external environment was also revealed. These temporal fluctuation distortions combined reflect an abnormality in the coordination of functional network switching in SZ, which is further the source of worse task performance (i.e., P300 amplitude) and the negative relationship between individual complexity metrics and P300 amplitude. Notably, when using the network metrics as features, multiple linear regressions of P300 amplitudes were also exactly achieved for both the SZ and HC groups. These findings shed light on the pathophysiological mechanisms of SZ from a temporal variability perspective and provide potential biomarkers for quantifying SZ's progressive neurophysiological deterioration.
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Affiliation(s)
- Lin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Jiuju Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Jing Dai
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; Chengdu Mental Health Center, Chengdu, 610036, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China.
| | - Baodan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Runyang He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Yuanyuan Liao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, 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, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Wentian Dong
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China.
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