1
|
Hwang HH, Choi KM, Im CH, Yang C, Kim S, Lee SH. Comparative analysis of resting-state EEG-based multiscale entropy between schizophrenia and bipolar disorder. Prog Neuropsychopharmacol Biol Psychiatry 2024; 134:111048. [PMID: 38825306 DOI: 10.1016/j.pnpbp.2024.111048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 05/14/2024] [Accepted: 05/30/2024] [Indexed: 06/04/2024]
Abstract
BACKGROUND Studies that use nonlinear methods to identify abnormal brain dynamics in patients with psychiatric disorders are limited. This study investigated brain dynamics based on EEG using multiscale entropy (MSE) analysis in patients with schizophrenia (SZ) and bipolar disorder (BD). METHODS The eyes-closed resting-state EEG data were collected from 51 patients with SZ, 51 patients with BD, and 51 healthy controls (HCs). Patients with BD were further categorized into type I (n = 23) and type II (n = 16), and then compared with patients with SZ. A sample entropy-based MSE was evaluated from the bilateral frontal, central, and parieto-occipital regions using 30-s artifact-free EEG data for each individual. Correlation analyses of MSE values and psychiatric symptoms were performed. RESULTS For patients with SZ, higher MSE values were observed at higher-scale factors (i.e., 41-70) across all regions compared with both HCs and patients with BD. Furthermore, there were positive correlations between the MSE values in the left frontal and parieto-occipital regions and PANSS scores. For patients with BD, higher MSE values were observed at middle-scale factors (i.e., 13-40) in the bilateral frontal and central regions compared with HCs. Patients with BD type I exhibited higher MSE values at higher-scale factors across all regions compared with those with BD type II. In BD type I, positive correlations were found between MSE values in all left regions and YMRS scores. CONCLUSIONS Patients with psychiatric disorders exhibited group-dependent MSE characteristics. These results suggest that MSE features may be useful biomarkers that reflect pathophysiological characteristics.
Collapse
Affiliation(s)
- Hyeon-Ho Hwang
- Department of Human-Computer Interaction, Hanyang University, Ansan, Republic of Korea; Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea
| | - Kang-Min Choi
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea; Department of Electronic Engineering, Hanyang University, Seoul, Republic of Korea
| | - Chang-Hwan Im
- Department of Electronic Engineering, Hanyang University, Seoul, Republic of Korea; Department of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Chaeyeon Yang
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea
| | - Sungkean Kim
- Department of Human-Computer Interaction, Hanyang University, Ansan, Republic of Korea.
| | - Seung-Hwan Lee
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea; Department of Psychiatry, Ilsan Paik Hospital, Inje University College of Medicine, Juhwa-ro 170, Ilsanseo-Gu, Goyang 10370, Republic of Korea.
| |
Collapse
|
2
|
Entropy Information of Pulse Dynamics in Three Stages of Pregnancy. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:6542072. [PMID: 36276859 PMCID: PMC9586734 DOI: 10.1155/2022/6542072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 06/09/2022] [Accepted: 09/24/2022] [Indexed: 11/07/2022]
Abstract
The aim of the present study is to use entropy to explore the change of pulse generated by normal pregnant women with gestational. Firstly, the subjects were divided into early (E), middle (M), and late (L) three stages according to gestational age. Then, pulse signals of the Chi position of 90 pregnant women at different gestational ages were collected. Secondly, the four entropies, namely fuzzy entropy (FuEn), approximate entropy (ApEn), sample entropy (SamEn), and permutation entropy (PerEn), were applied to the analysis of the long-term pulse changes of the pregnancy. Finally, the related information about pulse in different stages of pregnancy is given by the analysis of four kinds of entropy. Furthermore, the statistical tests are conducted for further comparison, and the descriptive statistics and the results are presented. In addition, boxplots are employed to show the distribution of four entropies of pregnancy. This work has studied the changes in pulse during pregnancy from quantitative and qualitative aspects. Our results show that entropy improves the diagnostic value of pulse analysis during pregnancy and could be applied to facilitate noninvasive diagnosis of pregnant women's physiological signals in the future.
Collapse
|
3
|
Garcia-Martinez B, Fernandez-Caballero A, Alcaraz R, Martinez-Rodrigo A. Application of Dispersion Entropy for the Detection of Emotions With Electroencephalographic Signals. IEEE Trans Cogn Dev Syst 2022. [DOI: 10.1109/tcds.2021.3099344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Beatriz Garcia-Martinez
- Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingenieros Industriales, Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, Spain
| | - Antonio Fernandez-Caballero
- Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingenieros Industriales, Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, Spain
| | - Raul Alcaraz
- Research Group in Electronic, Biomedical and Telecommunication Engineering, Escuela Politécnica de Cuenca, Universidad de Castilla-La Mancha, Cuenca, Spain
| | - Arturo Martinez-Rodrigo
- Research Group in Electronic, Biomedical and Telecommunication Engineering, Facultad de Comunicación, Instituto de Tecnologías Audiovisuales de Castilla-La Mancha, Universidad de Castilla-La Mancha, Cuenca, Spain
| |
Collapse
|
4
|
Sanchez-Reolid R, Martinez-Saez MC, Garcia-Martinez B, Fernandez-Aguilar L, Segura LR, Latorre JM, Fernandez-Caballero A. Emotion Classification from EEG with a Low-Cost BCI Versus a High-End Equipment. Int J Neural Syst 2022; 32:2250041. [DOI: 10.1142/s0129065722500411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
5
|
García-Martínez B, Fernández-Caballero A, Martínez-Rodrigo A. Entropy and the Emotional Brain: Overview of a Research Field. ARTIF INTELL 2022. [DOI: 10.5772/intechopen.98342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
During the last years, there has been a notable increase in the number of studies focused on the assessment of brain dynamics for the recognition of emotional states by means of nonlinear methodologies. More precisely, different entropy metrics have been applied for the analysis of electroencephalographic recordings for the detection of emotions. In this sense, regularity-based entropy metrics, symbolic predictability-based entropy indices, and different multiscale and multilag variants of the aforementioned methods have been successfully tested in a series of studies for emotion recognition from the EEG recording. This chapter aims to unify all those contributions to this scientific area, summarizing the main discoverings recently achieved in this research field.
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Sun Y, Chen X. Automatic Detection of Epilepsy Based on Entropy Feature Fusion and Convolutional Neural Network. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:1322826. [PMID: 35602093 PMCID: PMC9117030 DOI: 10.1155/2022/1322826] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/04/2022] [Accepted: 04/12/2022] [Indexed: 11/17/2022]
Abstract
Epilepsy is a neurological disorder, caused by various genetic and acquired factors. Electroencephalogram (EEG) is an important means of diagnosis for epilepsy. Aiming at the low efficiency of clinical artificial diagnosis of epilepsy signals, this paper proposes an automatic detection algorithm for epilepsy based on multifeature fusion and convolutional neural network. Firstly, in order to retain the spatial information between multiple adjacent channels, a two-dimensional Eigen matrix is constructed from one-dimensional eigenvectors according to the electrode distribution diagram. According to the feature matrix, sample entropy SE, permutation entropy PE, and fuzzy entropy FE were used for feature extraction. The combined entropy feature is taken as the input information of three-dimensional convolutional neural network, and the automatic detection of epilepsy is realized by convolutional neural network algorithm. Epilepsy detection experiments were performed in CHB-MIT and TUH datasets, respectively. Experimental results show that the performance of the algorithm based on spatial multifeature fusion and convolutional neural network achieves excellent results.
Collapse
Affiliation(s)
- Yongxin Sun
- College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, Jilin 130000, China
- College of Physics and Electronic Information, Baicheng Normal University, Baicheng, Jilin 137000, China
| | - Xiaojuan Chen
- College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, Jilin 130000, China
| |
Collapse
|
8
|
García-Martínez B, Fernández-Caballero A, Martínez-Rodrigo A, Alcaraz R, Novais P. Evaluation of Brain Functional Connectivity from Electroencephalographic Signals Under Different Emotional States. Int J Neural Syst 2022; 32:2250026. [PMID: 35469551 DOI: 10.1142/s0129065722500265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The identification of the emotional states corresponding to the four quadrants of the valence/arousal space has been widely analyzed in the scientific literature by means of multiple techniques. Nevertheless, most of these methods were based on the assessment of each brain region separately, without considering the possible interactions among different areas. In order to study these interconnections, this study computes for the first time the functional connectivity metric called cross-sample entropy for the analysis of the brain synchronization in four groups of emotions from electroencephalographic signals. Outcomes reported a strong synchronization in the interconnections among central, parietal and occipital areas, while the interactions between left frontal and temporal structures with the rest of brain regions presented the lowest coordination. These differences were statistically significant for the four groups of emotions. All emotions were simultaneously classified with a 95.43% of accuracy, overcoming the results reported in previous studies. Moreover, the differences between high and low levels of valence and arousal, taking into account the state of the counterpart dimension, also provided notable findings about the degree of synchronization in the brain within different emotional conditions and the possible implications of these outcomes from a psychophysiological point of view.
Collapse
Affiliation(s)
- Beatriz García-Martínez
- Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingenieros Industriales, Universidad de Castilla-La Mancha, 02071 Albacete, Spain.,Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
| | - Antonio Fernández-Caballero
- Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingenieros Industriales, Universidad de Castilla-La Mancha, 02071 Albacete, Spain.,Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, 02071 Albacete, Spain.,CIBERSAM (Biomedical Research Networking Centre in Mental Health), Madrid, Spain
| | - Arturo Martínez-Rodrigo
- Research Group in Electronic, Biomedical and Telecommunication Engineering, Facultad de Comunicación, Universidad de, Castilla-La Mancha, 16071 Cuenca, Spain.,Instituto de Tecnologías Audiovisuales de, Castilla-La Mancha, Universidad de Castilla-La, Mancha, 16071 Cuenca, Spain
| | - Raúl Alcaraz
- Research Group in Electronic, Biomedical and Telecommunication Engineering, Escuela Politécnica de Cuenca, Universidad, de Castilla-La Mancha, 16071 Cuenca, Spain
| | - Paulo Novais
- Algoritmi Center, Department of Informatics, Universidade do Minho, 4800-058 Guimaräes, Portugal
| |
Collapse
|
9
|
Physical Exercise Effects on University Students’ Attention: An EEG Analysis Approach. ELECTRONICS 2022. [DOI: 10.3390/electronics11050770] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Physically active breaks (AB) are currently being proposed as an interesting tool to improve students’ attention. Reviews and meta-analyses confirm their effect on attention, but also warned about the sparse evidence based on vigilance and university students. Therefore, this pilot study aimed to (a) determine the effects of AB in comparison with passive breaks on university students’ vigilance and (b) to validate an analysis model based on machine learning algorithms in conjunction with a multiparametric model based on electroencephalography (EEG) signal features. Through a counterbalanced within-subject experimental study, six university students (two female; mean age = 25.67, STD = 3.61) had their vigilance performances (i.e., response time in Psycho-Motor Vigilance Task) and EEG measured, before and after a lecture with an AB and another lecture with a passive break. A multiparametric model based on the spectral power, signal entropy and response time has been developed. Furthermore, this model, together with different machine learning algorithms, shows that for the taken signals there are significant differences after the AB lesson, implying an improvement in attention. These differences are most noticeable with the SVM with RBF kernel and ANNs with F1-score of 85% and 88%, respectively. In conclusion, results showed that students performed better on vigilance after the lecture with AB. Although limited, the evidence found could help researchers to be more accurate in their EEG analyses and lecturers and teachers to improve their students’ attentions in a proper way.
Collapse
|
10
|
Abstract
Cyber-Physical System (CPS) applications including human-robot interaction call for automated reasoning for rational decision-making. In the latter context, typically, audio-visual signals are employed. Τhis work considers brain signals for emotion recognition towards an effective human-robot interaction. An ElectroEncephaloGraphy (EEG) signal here is represented by an Intervals’ Number (IN). An IN-based, optimizable parametric k Nearest Neighbor (kNN) classifier scheme for decision-making by fuzzy lattice reasoning (FLR) is proposed, where the conventional distance between two points is replaced by a fuzzy order function (σ) for reasoning-by-analogy. A main advantage of the employment of INs is that no ad hoc feature extraction is required since an IN may represent all-order data statistics, the latter are the features considered implicitly. Four different fuzzy order functions are employed in this work. Experimental results demonstrate comparably the good performance of the proposed techniques.
Collapse
|
11
|
Varone G, Hussain Z, Sheikh Z, Howard A, Boulila W, Mahmud M, Howard N, Morabito FC, Hussain A. Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures. SENSORS 2021; 21:s21020637. [PMID: 33477526 PMCID: PMC7831109 DOI: 10.3390/s21020637] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 01/08/2021] [Accepted: 01/11/2021] [Indexed: 01/24/2023]
Abstract
Transcranial magnetic stimulation (TMS) excites neurons in the cortex, and neural activity can be simultaneously recorded using electroencephalography (EEG). However, TMS-evoked EEG potentials (TEPs) do not only reflect transcranial neural stimulation as they can be contaminated by artifacts. Over the last two decades, significant developments in EEG amplifiers, TMS-compatible technology, customized hardware and open source software have enabled researchers to develop approaches which can substantially reduce TMS-induced artifacts. In TMS-EEG experiments, various physiological and external occurrences have been identified and attempts have been made to minimize or remove them using online techniques. Despite these advances, technological issues and methodological constraints prevent straightforward recordings of early TEPs components. To the best of our knowledge, there is no review on both TMS-EEG artifacts and EEG technologies in the literature to-date. Our survey aims to provide an overview of research studies in this field over the last 40 years. We review TMS-EEG artifacts, their sources and their waveforms and present the state-of-the-art in EEG technologies and front-end characteristics. We also propose a synchronization toolbox for TMS-EEG laboratories. We then review subject preparation frameworks and online artifacts reduction maneuvers for improving data acquisition and conclude by outlining open challenges and future research directions in the field.
Collapse
Affiliation(s)
- Giuseppe Varone
- Department of Medical and Surgical Sciences, Magna Greacia University of Catanzaro, 88100 Catanzaro, Italy;
| | - Zain Hussain
- College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh EH16 4TJ, UK; (Z.H.); (Z.S.)
- Howard Brain Sciences Foundation, Providence, RI 02906, USA;
| | - Zakariya Sheikh
- College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh EH16 4TJ, UK; (Z.H.); (Z.S.)
| | - Adam Howard
- Howard Brain Sciences Foundation, Providence, RI 02906, USA;
| | - Wadii Boulila
- RIADI Laboratory, National School of Computer Sciences, University of Manouba, Manouba 2010, Tunisia;
- IS Department, College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia
| | - Mufti Mahmud
- Department of Computer Science and Medical Technology Innovation Facility, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK;
| | - Newton Howard
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK;
| | | | - Amir Hussain
- School of Computing, Edinburgh Napier University, Edinburgh EH11 4BN, UK;
| |
Collapse
|