1
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Xiong Y, Li J, Wu D, Dong F, Liu J, Jiang L, Cao J, Xu Y. Seizure detection algorithm based on fusion of spatio-temporal network constructed with dispersion index. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104155] [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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2
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Wang WK, Chen I, Hershkovich L, Yang J, Shetty A, Singh G, Jiang Y, Kotla A, Shang JZ, Yerrabelli R, Roghanizad AR, Shandhi MMH, Dunn J. A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22208016. [PMID: 36298367 PMCID: PMC9611376 DOI: 10.3390/s22208016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/23/2022] [Accepted: 10/17/2022] [Indexed: 05/06/2023]
Abstract
Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used for modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges. This review presents the non-deep learning models that are commonly used for time series classification in biomedical applications that can achieve high performance. Objective: We performed a systematic review to characterize the techniques that are used in time series classification of digital clinical measures throughout all the stages of data processing and model building. Methods: We conducted a literature search on PubMed, as well as the Institute of Electrical and Electronics Engineers (IEEE), Web of Science, and SCOPUS databases using a range of search terms to retrieve peer-reviewed articles that report on the academic research about digital clinical measures from a five-year period between June 2016 and June 2021. We identified and categorized the research studies based on the types of classification algorithms and sensor input types. Results: We found 452 papers in total from four different databases: PubMed, IEEE, Web of Science Database, and SCOPUS. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely used machine learning classifiers were the most commonly used technique, and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also the second-best classification technique. Conclusions: In this review paper, summaries of the time series classification models and interpretation methods for biomedical applications are summarized and categorized. While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation, however many different methods have proven successful.
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3
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Development of an EEG Headband for Stress Measurement on Driving Simulators. SENSORS 2022; 22:s22051785. [PMID: 35270931 PMCID: PMC8914656 DOI: 10.3390/s22051785] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 02/16/2022] [Accepted: 02/18/2022] [Indexed: 11/29/2022]
Abstract
In this paper, we designed from scratch, realized, and characterized a six-channel EEG wearable headband for the measurement of stress-related brain activity during driving. The headband transmits data over WiFi to a laptop, and the rechargeable battery life is 10 h of continuous transmission. The characterization manifested a measurement error of 6 μV in reading EEG channels, and the bandwidth was in the range [0.8, 44] Hz, while the resolution was 50 nV exploiting the oversampling technique. Thanks to the full metrological characterization presented in this paper, we provide important information regarding the accuracy of the sensor because, in the literature, commercial EEG sensors are used even if their accuracy is not provided in the manuals. We set up an experiment using the driving simulator available in our laboratory at the University of Udine; the experiment involved ten volunteers who had to drive in three scenarios: manual, autonomous vehicle with a “gentle” approach, and autonomous vehicle with an “aggressive” approach. The aim of the experiment was to assess how autonomous driving algorithms impact EEG brain activity. To our knowledge, this is the first study to compare different autonomous driving algorithms in terms of drivers’ acceptability by means of EEG signals. The obtained results demonstrated that the estimated power of beta waves (related to stress) is higher in the manual with respect to autonomous driving algorithms, either “gentle” or “aggressive”.
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4
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Sauer J, Sonderegger A, Thuillard S, Semmer NK. Social stress in human-machine systems: opportunities and challenges of an experimental research approach. THEORETICAL ISSUES IN ERGONOMICS SCIENCE 2022. [DOI: 10.1080/1463922x.2022.2040062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Juergen Sauer
- Department of Psychology, University of Fribourg, Fribourg, Switzerland
| | - Andreas Sonderegger
- Department of Psychology, University of Fribourg, Fribourg, Switzerland
- Business School, Institute for New Work, Bern University of Applied Sciences, Bern, Switzerland
| | - Simon Thuillard
- Department of Psychology, University of Fribourg, Fribourg, Switzerland
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5
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Javaid H, Kumarnsit E, Chatpun S. Age-Related Alterations in EEG Network Connectivity in Healthy Aging. Brain Sci 2022; 12:brainsci12020218. [PMID: 35203981 PMCID: PMC8870284 DOI: 10.3390/brainsci12020218] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 01/28/2022] [Accepted: 02/01/2022] [Indexed: 02/01/2023] Open
Abstract
Emerging studies have reported that functional brain networks change with increasing age. Graph theory is applied to understand the age-related differences in brain behavior and function, and functional connectivity between the regions is examined using electroencephalography (EEG). The effect of normal aging on functional networks and inter-regional synchronization during the working memory (WM) state is not well known. In this study, we applied graph theory to investigate the effect of aging on network topology in a resting state and during performing a visual WM task to classify aging EEG signals. We recorded EEGs from 20 healthy middle-aged and 20 healthy elderly subjects with their eyes open, eyes closed, and during a visual WM task. EEG signals were used to construct the functional network; nodes are represented by EEG electrodes; and edges denote the functional connectivity. Graph theory matrices including global efficiency, local efficiency, clustering coefficient, characteristic path length, node strength, node betweenness centrality, and assortativity were calculated to analyze the networks. We applied the three classifiers of K-nearest neighbor (KNN), a support vector machine (SVM), and random forest (RF) to classify both groups. The analyses showed the significantly reduced network topology features in the elderly group. Local efficiency, global efficiency, and clustering coefficient were significantly lower in the elderly group with the eyes-open, eyes-closed, and visual WM task states. KNN achieved its highest accuracy of 98.89% during the visual WM task and depicted better classification performance than other classifiers. Our analysis of functional network connectivity and topological characteristics can be used as an appropriate technique to explore normal age-related changes in the human brain.
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Affiliation(s)
- Hamad Javaid
- Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand;
| | - Ekkasit Kumarnsit
- Physiology Program, Division of Health and Applied Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand;
- Biosignal Research Centre for Health, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand
| | - Surapong Chatpun
- Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand;
- Biosignal Research Centre for Health, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand
- Institute of Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand
- Correspondence:
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6
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Wriessnegger SC, Raggam P, Kostoglou K, Müller-Putz GR. Mental State Detection Using Riemannian Geometry on Electroencephalogram Brain Signals. Front Hum Neurosci 2021; 15:746081. [PMID: 34899215 PMCID: PMC8663761 DOI: 10.3389/fnhum.2021.746081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 10/12/2021] [Indexed: 11/16/2022] Open
Abstract
The goal of this study was to implement a Riemannian geometry (RG)-based algorithm to detect high mental workload (MWL) and mental fatigue (MF) using task-induced electroencephalogram (EEG) signals. In order to elicit high MWL and MF, the participants performed a cognitively demanding task in the form of the letter n-back task. We analyzed the time-varying characteristics of the EEG band power (BP) features in the theta and alpha frequency band at different task conditions and cortical areas by employing a RG-based framework. MWL and MF were considered as too high, when the Riemannian distances of the task-run EEG reached or surpassed the threshold of the baseline EEG. The results of this study showed a BP increase in the theta and alpha frequency bands with increasing experiment duration, indicating elevated MWL and MF that impedes/hinders the task performance of the participants. High MWL and MF was detected in 8 out of 20 participants. The Riemannian distances also showed a steady increase toward the threshold with increasing experiment duration, with the most detections occurring toward the end of the experiment. To support our findings, subjective ratings (questionnaires concerning fatigue and workload levels) and behavioral measures (performance accuracies and response times) were also considered.
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Affiliation(s)
- Selina C Wriessnegger
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria.,BioTechMed-Graz, Graz, Austria
| | - Philipp Raggam
- Research Group Neuroinformatics, Faculty of Computer Science, University of Vienna, Vienna, Austria.,Department of Neurology and Stroke, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Kyriaki Kostoglou
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Gernot R Müller-Putz
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria.,BioTechMed-Graz, Graz, Austria
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7
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Sobhani V, Izadi K, Mokari EM, Hatef B. Classification of Body Position During Muslim Prayer Using the Convolutional Neural Network. INT J PATTERN RECOGN 2021. [DOI: 10.1142/s0218001421540288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Background: Muslim prayer (Namaz) is the most important obligatory religious duty in Islam that is regularly performed five times per day at specific prescribed times by Muslims. Due to the fact that change of body position affects brain activity, Namaz can be considered as a suitable model to assess the effect of quick changes of the body position on brain activity measured by electroencephalography (EEG). Methods: Forty Muslim participants performed a four-cycle Namaz while their brain activity was being recorded using a 14-channel EEG recorder. The brain connectivity (as defined by a mutual correlation between EEG channels in this study) in different frequency bands (delta, theta, alpha, beta, and gamma) was measured in various positions of Namaz including standing, bowing, prostration, and sitting. Results: The results indicated that the delta band demonstrates the most changes in cross-correlation between the recorded channels, and finally, the accuracy of 73.8% was obtained in the data classification.
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Affiliation(s)
- Vahid Sobhani
- Exercise Physiology Research Center, Life Style Institute, Baqiyatallah University of Medical Science, Tehran, Iran
| | - Koorosh Izadi
- Neuroscience Research Center, Baqiyatallah University of Medical Science, Tehran, Iran
| | - Ehsan Manshadi Mokari
- Neuroscience Research Center, Baqiyatallah University of Medical Science, Tehran, Iran
| | - Boshra Hatef
- Neuroscience Research Center, Baqiyatallah University of Medical Science, Tehran, Iran
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8
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Bagheri M, Power SD. Investigating hierarchical and ensemble classification approaches to mitigate the negative effect of varying stress state on EEG-based detection of mental workload level - and vice versa. BRAIN-COMPUTER INTERFACES 2021. [DOI: 10.1080/2326263x.2021.1948756] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Mahsa Bagheri
- Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, Canada
| | - Sarah D. Power
- Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, Canada
- Faculty of Medicine, Memorial University of Newfoundland, St. John’s, Canada
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9
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EEG-based multi-level stress classification with and without smoothing filter. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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10
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Katmah R, Al-Shargie F, Tariq U, Babiloni F, Al-Mughairbi F, Al-Nashash H. A Review on Mental Stress Assessment Methods Using EEG Signals. SENSORS (BASEL, SWITZERLAND) 2021; 21:5043. [PMID: 34372280 PMCID: PMC8347831 DOI: 10.3390/s21155043] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 07/13/2021] [Accepted: 07/19/2021] [Indexed: 01/19/2023]
Abstract
Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contains rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Accordingly, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.
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Affiliation(s)
- Rateb Katmah
- Biomedical Engineering Graduate Program, American University of Sharjah, Sharjah 26666, United Arab Emirates;
| | - Fares Al-Shargie
- Department of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates; (U.T.); (H.A.-N.)
| | - Usman Tariq
- Department of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates; (U.T.); (H.A.-N.)
| | - Fabio Babiloni
- Department of Molecular Medicine, University of Sapienza Rome, 00185 Rome, Italy;
- College Computer Science and Technology, University Hangzhou Dianzi, Hangzhou 310018, China
| | - Fadwa Al-Mughairbi
- College of Medicines and Health Sciences, United Arab Emirates University, Al-Ain 15551, United Arab Emirates;
| | - Hasan Al-Nashash
- Department of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates; (U.T.); (H.A.-N.)
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11
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Perez-Valero E, Vaquero-Blasco MA, Lopez-Gordo MA, Morillas C. Quantitative Assessment of Stress Through EEG During a Virtual Reality Stress-Relax Session. Front Comput Neurosci 2021; 15:684423. [PMID: 34335216 PMCID: PMC8317646 DOI: 10.3389/fncom.2021.684423] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 06/24/2021] [Indexed: 11/13/2022] Open
Abstract
Recent studies have addressed stress level classification via electroencephalography (EEG) and machine learning. These works typically use EEG-based features, like power spectral density (PSD), to develop stress classifiers. Nonetheless, these classifiers are usually limited to the discrimination of two (stress and no stress) or three (low, medium, and high) stress levels. In this study we propose an alternative for quantitative stress assessment based on EEG and regression algorithms. To this aim, we conducted a group of 23 participants (mean age 22.65 ± 5.48) over a stress-relax experience while monitoring their EEG. First, we stressed the participants via the Montreal imaging stress task (MIST), and then we led them through a 360-degree virtual reality (VR) relaxation experience. Throughout the session, the participants reported their self-perceived stress level (SPSL) via surveys. Subsequently, we extracted spectral features from the EEG of the participants and we developed individual models based on regression algorithms to predict their SPSL. We evaluated stress regression performance in terms of the mean squared percentage error (MSPE) and the correlation coefficient (R2). The results yielded from this evaluation (MSPE = 10.62 ± 2.12, R2 = 0.92 ± 0.02) suggest that our approach predicted the stress level of the participants with remarkable performance. These results may have a positive impact in diverse areas that could benefit from stress level quantitative prediction. These areas include research fields like neuromarketing, and training of professionals such as surgeons, industrial workers, or firefighters, that often face stressful situations.
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Affiliation(s)
- Eduardo Perez-Valero
- Department of Computer Architecture and Technology, University of Granada, Granada, Spain.,Research Centre for Information and Communications Technologies, University of Granada, Granada, Spain
| | - Miguel A Vaquero-Blasco
- Research Centre for Information and Communications Technologies, University of Granada, Granada, Spain.,Department of Signal Theory, Telematics and Communications, University of Granada, Granada, Spain
| | - Miguel A Lopez-Gordo
- Research Centre for Information and Communications Technologies, University of Granada, Granada, Spain.,Department of Signal Theory, Telematics and Communications, University of Granada, Granada, Spain
| | - Christian Morillas
- Department of Computer Architecture and Technology, University of Granada, Granada, Spain.,Research Centre for Information and Communications Technologies, University of Granada, Granada, Spain
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12
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Mosquera Navarro R, Castrillón OD, Parra Osorio L, Oliveira T, Novais P, Valencia JF. Improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers. PeerJ Comput Sci 2021; 7:e511. [PMID: 34141875 PMCID: PMC8176537 DOI: 10.7717/peerj-cs.511] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 04/06/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Psychosocial risks, also present in educational processes, are stress factors particularly critical in state-schools, affecting the efficacy, stress, and job satisfaction of the teachers. This study proposes an intelligent algorithm to improve the prediction of psychosocial risk, as a tool for the generation of health and risk prevention assistance programs. METHODS The proposed approach, Physical Surface Tension-Neural Net (PST-NN), applied the theory of superficial tension in liquids to an artificial neural network (ANN), in order to model four risk levels (low, medium, high and very high psychosocial risk). The model was trained and tested using the results of tests for measurement of the psychosocial risk levels of 5,443 teachers. Psychosocial, and also physiological and musculoskeletal symptoms, factors were included as inputs of the model. The classification efficiency of the PST-NN approach was evaluated by using the sensitivity, specificity, accuracy and ROC curve metrics, and compared against other techniques as the Decision Tree model, Naïve Bayes, ANN, Support Vector Machines, Robust Linear Regression and the Logistic Regression Model. RESULTS The modification of the ANN model, by the adaptation of a layer that includes concepts related to the theory of physical surface tension, improved the separation of the subjects according to the risk level group, as a function of the mass and perimeter outputs. Indeed, the PST-NN model showed better performance to classify psychosocial risk level on state-school teachers than the linear, probabilistic and logistic models included in this study, obtaining an average accuracy value of 97.31%. CONCLUSIONS The introduction of physical models, such as the physical surface tension, can improve the classification performance of ANN. Particularly, the PST-NN model can be used to predict and classify psychosocial risk levels among state-school teachers at work. This model could help to early identification of psychosocial risk and to the development of programs to prevent it.
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Affiliation(s)
- Rodolfo Mosquera Navarro
- Departamento de Ingeniería Industrial, Universidad Nacional de Colombia, Manizales, Caldas, Colombia
- Grupo Nuevas tecnologías trabajo y gestión, Universidad de San Buenaventura - Cali, Cali, Valle del Cauca, Colombia
| | - Omar Danilo Castrillón
- Departamento de Ingeniería Industrial, Universidad Nacional de Colombia, Manizales, Caldas, Colombia
| | - Liliana Parra Osorio
- Centro de Investigaciones Socio jurídicas, Facultad de Derecho, Universidad Libre, Bogotá, Cundinamarca, Colombia
| | - Tiago Oliveira
- Algoritmi Center, Universidade do Minho, Minho, Braga, Portugal
| | - Paulo Novais
- Department of Informatics/Algoritmi Center, Universidade do Minho, Minho, Braga, Portugal
| | - José Fernando Valencia
- Department of Ciencias y Tecnologías de la Información, Universidad de San Buenaventura - Cali, Cali, Valle del Cauca, Colombia
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13
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Gu Y, Hua L. A Novel Smart Motor Imagery Intention Human-Computer Interaction Model Using Extreme Learning Machine and EEG Signals. Front Neurosci 2021; 15:685119. [PMID: 34025347 PMCID: PMC8134549 DOI: 10.3389/fnins.2021.685119] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 04/08/2021] [Indexed: 11/15/2022] Open
Abstract
The brain is the central nervous system that governs human activities. However, in modern society, more and more diseases threaten the health of the brain and nerves and spinal cord, making the human brain unable to conduct normal information interaction with the outside world. The rehabilitation training of the brain-computer interface can promote the nerve repair of the sensorimotor cortex in patients with brain diseases. Therefore, the research of brain-computer interface for motor imaging is of great significance for patients with brain diseases to restore motor function. Due to the characteristics of non-stationary, nonlinear, and individual differences of EEG signals, there are still many difficulties in the analysis and classification of EEG signals at this stage. In this study, the Extreme Learning Machine (ELM) model was used to classify motor-imaging EEG signals, identify the user’s intention, and control external devices. Considering that single-modal features cannot represent the core information, this study uses a fusion feature that combines temporal and spatial features as the final feature data. The fusion features are input to the trained ELM classifier, and the final classification result is obtained. Two sets of BCI competition data in the BCI competition public database are used to verify the validity of the model. The experimental results show that the ELM model has achieved a classification accuracy of 0.7832 in the classification task of Data Sets IIb, which is higher than other comparison algorithms, and shows universal applicability among different subjects. In addition, the average recognition rate of this model in the Data Sets IIIa classification task reaches 0.8347, which has obvious advantages compared with the comparative classification algorithm. The classification effect is smaller than the classification effect obtained by the champion algorithm of the same project, which has certain reference value.
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Affiliation(s)
- Yi Gu
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
| | - Lei Hua
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
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14
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Bozorgmehr A, Thielmann A, Weltermann B. Chronic stress in practice assistants: An analytic approach comparing four machine learning classifiers with a standard logistic regression model. PLoS One 2021; 16:e0250842. [PMID: 33945572 PMCID: PMC8096078 DOI: 10.1371/journal.pone.0250842] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 04/15/2021] [Indexed: 12/20/2022] Open
Abstract
Background Occupational stress is associated with adverse outcomes for medical professionals and patients. In our cross-sectional study with 136 general practices, 26.4% of 550 practice assistants showed high chronic stress. As machine learning strategies offer the opportunity to improve understanding of chronic stress by exploiting complex interactions between variables, we used data from our previous study to derive the best analytic model for chronic stress: four common machine learning (ML) approaches are compared to a classical statistical procedure. Methods We applied four machine learning classifiers (random forest, support vector machine, K-nearest neighbors’, and artificial neural network) and logistic regression as standard approach to analyze factors contributing to chronic stress in practice assistants. Chronic stress had been measured by the standardized, self-administered TICS-SSCS questionnaire. The performance of these models was compared in terms of predictive accuracy based on the ‘operating area under the curve’ (AUC), sensitivity, and positive predictive value. Findings Compared to the standard logistic regression model (AUC 0.636, 95% CI 0.490–0.674), all machine learning models improved prediction: random forest +20.8% (AUC 0.844, 95% CI 0.684–0.843), artificial neural network +12.4% (AUC 0.760, 95% CI 0.605–0.777), support vector machine +15.1% (AUC 0.787, 95% CI 0.634–0.802), and K-nearest neighbours +7.1% (AUC 0.707, 95% CI 0.556–0.735). As best prediction model, random forest showed a sensitivity of 99% and a positive predictive value of 79%. Using the variable frequencies at the decision nodes of the random forest model, the following five work characteristics influence chronic stress: too much work, high demand to concentrate, time pressure, complicated tasks, and insufficient support by practice leaders. Conclusions Regarding chronic stress prediction, machine learning classifiers, especially random forest, provided more accurate prediction compared to classical logistic regression. Interventions to reduce chronic stress in practice personnel should primarily address the identified workplace characteristics.
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Affiliation(s)
- Arezoo Bozorgmehr
- Institute of General Practice and Family Medicine, University Hospital Bonn, University of Bonn, Bonn, Germany
- * E-mail:
| | - Anika Thielmann
- Institute of General Practice and Family Medicine, University Hospital Bonn, University of Bonn, Bonn, Germany
- Institute for General Medicine, University Hospital Essen, University of Duisburg Essen, Essen, Germany
| | - Birgitta Weltermann
- Institute of General Practice and Family Medicine, University Hospital Bonn, University of Bonn, Bonn, Germany
- Institute for General Medicine, University Hospital Essen, University of Duisburg Essen, Essen, Germany
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15
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Perez-Valero E, Lopez-Gordo MA, Morillas C, Pelayo F, Vaquero-Blasco MA. A Review of Automated Techniques for Assisting the Early Detection of Alzheimer's Disease with a Focus on EEG. J Alzheimers Dis 2021; 80:1363-1376. [PMID: 33682717 DOI: 10.3233/jad-201455] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In this paper, we review state-of-the-art approaches that apply signal processing (SP) and machine learning (ML) to automate the detection of Alzheimer's disease (AD) and its prodromal stages. In the first part of the document, we describe the economic and social implications of the disease, traditional diagnosis techniques, and the fundaments of automated AD detection. Then, we present electroencephalography (EEG) as an appropriate alternative for the early detection of AD, owing to its reduced cost, portability, and non-invasiveness. We also describe the main time and frequency domain EEG features that are employed in AD detection. Subsequently, we examine some of the main studies of the last decade that aim to provide an automatic detection of AD and its previous stages by means of SP and ML. In these studies, brain data was acquired using multiple medical techniques such as magnetic resonance imaging, positron emission tomography, and EEG. The main aspects of each approach, namely feature extraction, classification model, validation approach, and performance metrics, are compiled and discussed. Lastly, a set of conclusions and recommendations for future research on AD automatic detection are drawn in the final section of the paper.
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Affiliation(s)
- Eduardo Perez-Valero
- Research Centre for Information and Communications Technologies (CITIC), University of Granada, Granada, Spain.,Department of Computer Architecture and Technology, University of Granada, Granada, Spain
| | - Miguel A Lopez-Gordo
- Department of Signal Theory, Telematics and Communications, University of Granada, Granada,Spain.,Nicolo Association, Churriana de la Vega, Spain
| | - Christian Morillas
- Research Centre for Information and Communications Technologies (CITIC), University of Granada, Granada, Spain.,Department of Computer Architecture and Technology, University of Granada, Granada, Spain
| | - Francisco Pelayo
- Research Centre for Information and Communications Technologies (CITIC), University of Granada, Granada, Spain.,Department of Computer Architecture and Technology, University of Granada, Granada, Spain
| | - Miguel A Vaquero-Blasco
- Research Centre for Information and Communications Technologies (CITIC), University of Granada, Granada, Spain.,Department of Signal Theory, Telematics and Communications, University of Granada, Granada,Spain
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Xia K, Ni T, Yin H, Chen B. Cross-Domain Classification Model With Knowledge Utilization Maximization for Recognition of Epileptic EEG Signals. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:53-61. [PMID: 32078557 DOI: 10.1109/tcbb.2020.2973978] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Conventional classification models for epileptic EEG signal recognition need sufficient labeled samples as training dataset. In addition, when training and testing EEG signal samples are collected from different distributions, for example, due to differences in patient groups or acquisition devices, such methods generally cannot perform well. In this paper, a cross-domain classification model with knowledge utilization maximization called CDC-KUM is presented, which takes advantage of the data global structure provided by the labeled samples in the related domain and unlabeled samples in the current domain. Through mapping the data into kernel space, the pairwise constraint regularization term is combined together the predictive differences of the labeled data in the source domain. Meanwhile, the soft clustering regularization term using quadratic weights and Gini-Simpson diversity is applied to exploit the distribution information of unlabeled data in the target domain. Experimental results show that CDC-KUM model outperformed several traditional non-transfer and transfer classification methods for recognition of epileptic EEG signals.
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17
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Savareh BA, Bashiri A, Hatef MM, Hatef B. Prediction of salivary cortisol level by electroencephalography features. ACTA ACUST UNITED AC 2020; 66:275-284. [PMID: 34062630 DOI: 10.1515/bmt-2020-0005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Accepted: 11/23/2020] [Indexed: 11/15/2022]
Abstract
Change in cortisol affects brain EEG signals. So, the identification of the significant EEG features which are sensitized to cortisol concentration was the aim of the present study. From 468 participated healthy subjects, the salivary samples were taken to test the cortisol concentration and EEG signal recording was done simultaneously. Then, the subjects were categorized into three classes based on the salivary cortisol concentration (<5, 5-15 and >15 nmol/l). Some linear and nonlinear features extracted and finally, in order to investigate the relationship between cortisol level and EEG features, the following steps were taken on features in sequence: Genetic Algorithm, Neighboring Component Analysis, polyfit, artificial neural network and support vector machine classification. Two classifications were considered as following: state 1 categorized the subjects into three groups (three classes) and the second state put them into two groups (group 1: class 1 and 3, group 2: class 2). The best classification was done using ANN in the second state with the accuracy=94.1% while it was 92.7% in the first state. EEG features carefully predicted the cortisol level. This result is applicable to design the intelligence brain computer machines to control stress and brain performance.
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Affiliation(s)
- Behrouz Alizadeh Savareh
- National Agency for Strategic Research in Medical Education, Tehran, Iran.,Department of Health Information Management, School of Management and Medical Information Sciences, Health Human Resources Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Azadeh Bashiri
- Department of Health Information Management, School of Management and Medical Information Sciences, Health Human Resources Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammad Mehdi Hatef
- Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Islamic Republic of Iran
| | - Boshra Hatef
- Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Islamic Republic of Iran
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18
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Modified Support Vector Machine for Detecting Stress Level Using EEG Signals. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:8860841. [PMID: 32802030 PMCID: PMC7416233 DOI: 10.1155/2020/8860841] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 06/20/2020] [Accepted: 07/08/2020] [Indexed: 11/17/2022]
Abstract
Stress is categorized as a condition of mental strain or pressure approaches because of upsetting or requesting conditions. There are various sources of stress initiation. Researchers consider human cerebrum as the primary wellspring of stress. To study how each individual encounters stress in different forms, researchers conduct surveys and monitor it. The paper presents the fusion of 5 algorithms to enhance the accuracy for detection of mental stress using EEG signals. The Whale Optimization Algorithm has been modified to select the optimal kernel in the SVM classifier for stress detection. An integrated set of algorithms (NLM, DCT, and MBPSO) has been used for preprocessing, feature extraction, and selection. The proposed algorithm has been tested on EEG signals collected from 14 subjects to identify the stress level. The proposed approach was validated using accuracy, sensitivity, specificity, and F1 score with values of 96.36%, 96.84%, 90.8%, and 97.96% and was found to be better than the existing ones. The algorithm may be useful to psychiatrists and health consultants for diagnosing the stress level.
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19
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Nabil D, Benali R, Bereksi Reguig F. Epileptic seizure recognition using EEG wavelet decomposition based on nonlinear and statistical features with support vector machine classification. ACTA ACUST UNITED AC 2020; 65:133-148. [PMID: 31536031 DOI: 10.1515/bmt-2018-0246] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 06/13/2019] [Indexed: 01/09/2023]
Abstract
Epileptic seizure (ES) is a neurological brain dysfunction. ES can be detected using the electroencephalogram (EEG) signal. However, visual inspection of ES using long-time EEG recordings is a difficult, time-consuming and a costly procedure. Thus, automatic epilepsy recognition is of primary importance. In this paper, a new method is proposed for automatic ES recognition using short-time EEG recordings. The method is based on first decomposing the EEG signals on sub-signals using discrete wavelet transform. Then, from the obtained sub-signals, different non-linear parameters such as approximate entropy (ApEn), largest Lyapunov exponents (LLE) and statistical parameters are determined. These parameters along with phase entropies, calculated through higher order spectrum analysis, are used as an input vector of a multi-class support vector machine (MSVM) for ES recognition. The proposed method is evaluated using the standard EEG database developed by the Department of Epileptology, University of Bonn, Germany. The evaluation is carried out through a large number of classification experiments. Different statistical metrics namely Sensitivity (Se), Specificity (Sp) and classification accuracy (Ac) are calculated and compared to those obtained in the scientific research literature. The obtained results show that the proposed method achieves high accuracies, which are as good as the best existing state-of-the-art methods studied using the same EEG database.
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Affiliation(s)
- Dib Nabil
- Biomedical Engineering Laboratory, Faculty of Technology, Abou Bekr Belkaid University, Tlemcen 13048, Algeria
| | - Radhwane Benali
- Biomedical Engineering Laboratory, Faculty of Technology, Abou Bekr Belkaid University, Tlemcen 13048, Algeria
| | - Fethi Bereksi Reguig
- Biomedical Engineering Laboratory, Faculty of Technology, Abou Bekr Belkaid University, Tlemcen 13048, Algeria
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Khosla A, Khandnor P, Chand T. A comparative analysis of signal processing and classification methods for different applications based on EEG signals. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.02.002] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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21
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22
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Sahani M, Dash P. Fault location estimation for series-compensated double-circuit transmission line using parameter optimized variational mode decomposition and weighted P-norm random vector functional link network. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105860] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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