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Xie Z, Fong R, Fox AP. Towards a potent and rapidly reversible Dexmedetomidine-based general anesthetic. PLoS One 2023; 18:e0291827. [PMID: 37751454 PMCID: PMC10522005 DOI: 10.1371/journal.pone.0291827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 09/06/2023] [Indexed: 09/28/2023] Open
Abstract
IN CONCLUSION Our results suggest that Dex supplemented with a low dose of a second agent creates a potent anesthetic that is rapidly reversed by atipamezole and caffeine.
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Affiliation(s)
- Zheng Xie
- Department of Anesthesia and Critical Care, The University of Chicago, Chicago, IL, United States of America
| | - Robert Fong
- Department of Anesthesia and Critical Care, The University of Chicago, Chicago, IL, United States of America
| | - Aaron P. Fox
- Department of Neurobiology, Pharmacology and Physiology, The University of Chicago, Chicago, IL, United States of America
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2
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Hermans T, Smets L, Lemmens K, Dereymaeker A, Jansen K, Naulaers G, Zappasodi F, Van Huffel S, Comani S, De Vos M. A multi-task and multi-channel convolutional neural network for semi-supervised neonatal artefact detection. J Neural Eng 2023; 20. [PMID: 36791462 DOI: 10.1088/1741-2552/acbc4b] [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: 09/20/2022] [Accepted: 02/15/2023] [Indexed: 02/17/2023]
Abstract
Objective. Automated artefact detection in the neonatal electroencephalogram (EEG) is crucial for reliable automated EEG analysis, but limited availability of expert artefact annotations challenges the development of deep learning models for artefact detection. This paper proposes a semi-supervised deep learning approach for artefact detection in neonatal EEG that requires few labelled data by training a multi-task convolutional neural network (CNN).Approach. An unsupervised and a supervised objective were jointly optimised by combining an autoencoder and an artefact classifier in one multi-output model that processes multi-channel EEG inputs. The proposed semi-supervised multi-task training strategy was compared to a classical supervised strategy and other existing state-of-the-art models. The models were trained and tested separately on two different datasets, which contained partially annotated multi-channel neonatal EEG. Models were evaluated using the F1-statistic and the relevance of the method was investigated in the context of a functional brain age (FBA) prediction model.Main results. The proposed multi-task and multi-channel CNN methods outperformed state-of-the-art methods, reaching F1 scores of 86.2% and 95.7% on two separate datasets. The proposed semi-supervised multi-task training strategy was shown to be superior to a classical supervised training strategy when the amount of labels in the dataset was artificially reduced. Finally, we found that the error of a brain age prediction model correlated with the amount of automatically detected artefacts in the EEG segment.Significance. Our results show that the proposed semi-supervised multi-task training strategy can train CNNs successfully even when the amount of labels in the dataset is limited. Therefore, this method is a promising semi-supervised technique for developing deep learning models with scarcely labelled data. Moreover, a correlation between the error of FBA estimates and the amount of detected artefacts in the corresponding EEG segments indicates the relevance of artefact detection for robust automated EEG analysis.
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Affiliation(s)
- Tim Hermans
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Laura Smets
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.,Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Katrien Lemmens
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium.,Neonatal Intensive Care Unit, UZ Leuven, Leuven, Belgium
| | - Anneleen Dereymaeker
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium.,Neonatal Intensive Care Unit, UZ Leuven, Leuven, Belgium
| | - Katrien Jansen
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium.,Child Neurology, UZ Leuven, Leuven, Belgium
| | - Gunnar Naulaers
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium.,Neonatal Intensive Care Unit, UZ Leuven, Leuven, Belgium
| | - Filippo Zappasodi
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy.,Behavioral Imaging and Neural Dynamics Center, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy.,Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Sabine Van Huffel
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Silvia Comani
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy.,Behavioral Imaging and Neural Dynamics Center, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Maarten De Vos
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.,Department of Development and Regeneration, KU Leuven, Leuven, Belgium
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3
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Tamburro G, Jansen K, Lemmens K, Dereymaeker A, Naulaers G, De Vos M, Comani S. Automated detection and removal of flat line segments and large amplitude fluctuations in neonatal electroencephalography. PeerJ 2022; 10:e13734. [PMID: 35846889 PMCID: PMC9285485 DOI: 10.7717/peerj.13734] [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: 02/21/2022] [Accepted: 06/24/2022] [Indexed: 01/17/2023] Open
Abstract
Background Artefact removal in neonatal electroencephalography (EEG) by visual inspection generally depends on the expertise of the operator, is time consuming and is not a consistent pre-processing step to the pipeline for the automated EEG analysis. Therefore, there is the need for the automated detection and removal of artefacts in neonatal EEG, especially of distinct and predominant artefacts such as flat line segments (mainly caused by instrumental error where contact between electrodes and head box is lost) and large amplitude fluctuations (related to neonatal movements). Method A threshold-based algorithm for the automated detection and removal of flat line segments and large amplitude fluctuations in neonatal EEG of infants at term-equivalent age is developed. The algorithm applies thresholds to the absolute second difference, absolute amplitude, absolute first difference and the ratio between the frequency content above 50 Hz and the frequency content across all frequencies. Results The algorithm reaches a median accuracy of 0.91, a median hit rate of 0.91 and a median false discovery rate of 0.37. Also, a significant improvement (≈10%) in the performance of a four-stage sleep classifier is observed after artefact removal with the proposed algorithm as compared to before its application. Significance An automated artefact removal method contributes to the pipeline of automated EEG analysis. The proposed algorithm has shown to have good performance and to be effective in neonatal EEG applications.
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Affiliation(s)
- Gabriella Tamburro
- Department of Neuroscience, Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti-Pescara, Chieti, Italy,BIND – Behavioral Imaging and Neural Dynamics Center, University “G. d’Annunzio” of Chieti-Pescara, Chieti, Italy
| | - Katrien Jansen
- Department of Development and Regeneration, UZ Leuven, Leuven, Belgium
| | - Katrien Lemmens
- Department of Development and Regeneration, UZ Leuven, Leuven, Belgium
| | | | - Gunnar Naulaers
- Department of Development and Regeneration, UZ Leuven, Leuven, Belgium
| | - Maarten De Vos
- Department of Development and Regeneration, UZ Leuven, Leuven, Belgium,Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Silvia Comani
- Department of Neuroscience, Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti-Pescara, Chieti, Italy,BIND – Behavioral Imaging and Neural Dynamics Center, University “G. d’Annunzio” of Chieti-Pescara, Chieti, Italy
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4
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Chiu NT, Huwiler S, Ferster ML, Karlen W, Wu HT, Lustenberger C. Get rid of the beat in mobile EEG applications: A framework towards automated cardiogenic artifact detection and removal in single-channel EEG. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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5
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Tamburro G, Croce P, Zappasodi F, Comani S. Automated Detection and Removal of Cardiac and Pulse Interferences from Neonatal EEG Signals. SENSORS (BASEL, SWITZERLAND) 2021; 21:6364. [PMID: 34640681 PMCID: PMC8512476 DOI: 10.3390/s21196364] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 09/10/2021] [Accepted: 09/20/2021] [Indexed: 12/03/2022]
Abstract
Electrical cardiac and pulsatile interference is very difficult to remove from electroencephalographic (EEG) signals, especially if recorded in neonates, for which a small number of EEG channels is used. Several methods were proposed, including Blind Source Separation (BSS) methods that required the use of artificial cardiac-related signals to improve the separation of artefactual components. To optimize the separation of cardiac-related artefactual components, we propose a method based on Independent Component Analysis (ICA) that exploits specific features of the real electrocardiographic (ECG) signals that were simultaneously recorded with the neonatal EEG. A total of forty EEG segments from 19-channel neonatal EEG recordings with and without seizures were used to test and validate the performance of our method. We observed a significant reduction in the number of independent components (ICs) containing cardiac-related interferences, with a consequent improvement in the automated classification of the separated ICs. The comparison with the expert labeling of the ICs separately containing electrical cardiac and pulsatile interference led to an accuracy = 0.99, a false omission rate = 0.01 and a sensitivity = 0.93, outperforming existing methods. Furthermore, we verified that true brain activity was preserved in neonatal EEG signals reconstructed after the removal of artefactual ICs, demonstrating the effectiveness of our method and its safe applicability in a clinical context.
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Affiliation(s)
- Gabriella Tamburro
- Behavioral Imaging and Neural Dynamics Center, G. d’Annunzio University of Chieti–Pescara, 66100 Chieti, Italy;
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti–Pescara, 66100 Chieti, Italy; (P.C.); (F.Z.)
| | - Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti–Pescara, 66100 Chieti, Italy; (P.C.); (F.Z.)
| | - Filippo Zappasodi
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti–Pescara, 66100 Chieti, Italy; (P.C.); (F.Z.)
- Institute for Advanced Biomedical Technologies, G. d’Annunzio University of Chieti–Pescara, 66100 Chieti, Italy
| | - Silvia Comani
- Behavioral Imaging and Neural Dynamics Center, G. d’Annunzio University of Chieti–Pescara, 66100 Chieti, Italy;
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti–Pescara, 66100 Chieti, Italy; (P.C.); (F.Z.)
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6
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Wang J, Cao J, Hu D, Jiang T, Gao F. Eye Blink Artifact Detection With Novel Optimized Multi-Dimensional Electroencephalogram Features. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1494-1503. [PMID: 34310312 DOI: 10.1109/tnsre.2021.3099232] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Accurate eye blink artifact detection is essential for electroencephalogram (EEG) analysis and auxiliary analysis of nervous system diseases, especially in the presence of the frontal epileptiform discharges. In this paper, we develop a novel eye blink artifact detection algorithm based on optimally selected multi-dimensional EEG features. Specific efforts have been paid to filtering the frontal epileptiform discharges, where an unsupervised learning exploiting the EEG signal physiological characteristics and smooth nonlinear energy operator (SNEO) based on the K-means clustering has been firstly proposed. Multiple statistical EEG features derived from the frontal electrodes and other electrodes are then extracted to characterize eye blink artifacts. Discriminative feature selection scheme based on the variance filtering and Relief algorithms has been respectively studied, and the average correlation coefficient (ACC) is applied for feature optimization evaluation. The eye blink artifact detection is finally achieved based on the support vector machine (SVM) trained on the optimized EEG features. The effectiveness of the proposed algorithm is demonstrated by experiments carried out on the EEG database of 11 subjects recorded from the Children's Hospital, Zhejiang University School of Medicine (CHZU). Comparisons to several state-of-the-art (SOTA) eye blink artifact detection methods are also presented.
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7
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Fox AP, Wagner KR, Towle VL, Xie KG, Xie Z. Caffeine reverses the unconsciousness produced by light anesthesia in the continued presence of isoflurane in rats. PLoS One 2020; 15:e0241818. [PMID: 33152041 PMCID: PMC7643991 DOI: 10.1371/journal.pone.0241818] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 10/20/2020] [Indexed: 12/17/2022] Open
Abstract
Currently no drugs are employed clinically to reverse the unconsciousness induced by general anesthetics. Our previous studies showed that caffeine, when given near the end of an anesthesia session, accelerated emergence from isoflurane anesthesia, likely caused by caffeine’s ability to elevate intracellular cAMP levels and to block adenosine receptors. These earlier studies showed that caffeine did not rouse either rats or humans from deep anesthesia (≥ 1 minimum alveolar concentration, MAC). In this current crossover study, we examined whether caffeine reversed the unconsciousness produced by light anesthesia (< 1 MAC) in the continued presence of isoflurane. The primary endpoint of this study was to measure isoflurane levels at the time of recovery of righting reflex, which was a proxy for consciousness. Rats were deeply anesthetized with 2% isoflurane (~1.5 MAC) for 20 minutes. Subsequently, isoflurane was reduced to 1.2% for 10 minutes, then by 0.2% every 10 min; animals were monitored until the recovery of righting reflex occurred, in the continued presence of isoflurane. Respiration rate, heart rate and electroencephalogram (EEG) were monitored. Our results show that caffeine-treated rats recovered their righting reflex at a significantly higher inspired isoflurane concentration, corresponding to light anesthesia, than the same rats treated with saline (control). Respiration rate and heart rate increased initially after caffeine injection but were then unchanged for the rest of the anesthesia session. Deep anesthesia is correlated with burst suppression in EEG recordings. Our data showed that caffeine transiently reduced the burst suppression time produced by deep anesthesia, suggesting that caffeine altered neuronal circuit function but not to a point where it caused arousal. In contrast, under light anesthesia, caffeine shifted the EEG power to high frequency beta and gamma bands. These data suggest that caffeine may represent a clinically viable drug to reverse the unconsciousness produced by light anesthesia.
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Affiliation(s)
- Aaron P. Fox
- Department of Neurobiology, Pharmacology and Physiology, The University of Chicago, Chicago, Illinois, United States of America
| | - Kyle R. Wagner
- Department of Anesthesia and Critical Care, The University of Chicago, Chicago, Illinois, United States of America
| | - Vernon L. Towle
- Department of Neurology, The University of Chicago, Chicago, Illinois, United States of America
| | - Kelvin G. Xie
- Department of Neurobiology, Pharmacology and Physiology, The University of Chicago, Chicago, Illinois, United States of America
| | - Zheng Xie
- Department of Anesthesia and Critical Care, The University of Chicago, Chicago, Illinois, United States of America
- * E-mail:
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8
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Neonatal intensive care decision support systems using artificial intelligence techniques: a systematic review. Artif Intell Rev 2018. [DOI: 10.1007/s10462-018-9635-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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9
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Asgari S, Moshirvaziri H, Scalzo F, Ramezan-Arab N. Quantitative measures of EEG for prediction of outcome in cardiac arrest subjects treated with hypothermia: a literature review. J Clin Monit Comput 2018; 32:977-992. [DOI: 10.1007/s10877-018-0118-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2017] [Accepted: 02/22/2018] [Indexed: 12/14/2022]
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10
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Hong KS, Khan MJ. Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review. Front Neurorobot 2017; 11:35. [PMID: 28790910 PMCID: PMC5522881 DOI: 10.3389/fnbot.2017.00035] [Citation(s) in RCA: 116] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Accepted: 07/03/2017] [Indexed: 12/11/2022] Open
Abstract
In this article, non-invasive hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain-computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.
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Affiliation(s)
- Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Busan, South Korea.,Department of Cogno-Mechatronics Engineering, Pusan National University, Busan, South Korea
| | - Muhammad Jawad Khan
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
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Islam MK, Rastegarnia A, Yang Z. Methods for artifact detection and removal from scalp EEG: A review. Neurophysiol Clin 2016; 46:287-305. [PMID: 27751622 DOI: 10.1016/j.neucli.2016.07.002] [Citation(s) in RCA: 147] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Revised: 05/29/2016] [Accepted: 07/07/2016] [Indexed: 11/29/2022] Open
Abstract
Electroencephalography (EEG) is the most popular brain activity recording technique used in wide range of applications. One of the commonly faced problems in EEG recordings is the presence of artifacts that come from sources other than brain and contaminate the acquired signals significantly. Therefore, much research over the past 15 years has focused on identifying ways for handling such artifacts in the preprocessing stage. However, this is still an active area of research as no single existing artifact detection/removal method is complete or universal. This article presents an extensive review of the existing state-of-the-art artifact detection and removal methods from scalp EEG for all potential EEG-based applications and analyses the pros and cons of each method. First, a general overview of the different artifact types that are found in scalp EEG and their effect on particular applications are presented. In addition, the methods are compared based on their ability to remove certain types of artifacts and their suitability in relevant applications (only functional comparison is provided not performance evaluation of methods). Finally, the future direction and expected challenges of current research is discussed. Therefore, this review is expected to be helpful for interested researchers who will develop and/or apply artifact handling algorithm/technique in future for their applications as well as for those willing to improve the existing algorithms or propose a new solution in this particular area of research.
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Affiliation(s)
- Md Kafiul Islam
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - Amir Rastegarnia
- Department of Electrical Engineering, University of Malayer, Malayer, Iran.
| | - Zhi Yang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
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12
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13
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Enhanced Automatic Wavelet Independent Component Analysis for Electroencephalographic Artifact Removal. ENTROPY 2014. [DOI: 10.3390/e16126553] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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14
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Ventouras EC, Margariti A, Chondraki P, Kalatzis I, Economou NT, Tsekou H, Paparrigopoulos T, Ktonas P. EEG-based investigation of brain connectivity changes in psychotic patients undergoing the primitive expression form of dance therapy: a methodological pilot study. Cogn Neurodyn 2014; 9:231-48. [PMID: 25852781 DOI: 10.1007/s11571-014-9319-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2014] [Revised: 10/22/2014] [Accepted: 11/05/2014] [Indexed: 11/27/2022] Open
Abstract
Primitive expression (PE) is a form of dance therapy (DT) that involves an interaction of ethologically and socially based forms which are supplied for re-enactment. There exist very few studies of DT applications including in their protocol the measurement of neurophysiological parameters. The present pilot study investigates the use of the correlation coefficient (ρ) and mutual information (MI), and of novel measures extracted from ρ and MI, on electroencephalographic (EEG) data recorded in patients with schizophrenia while they undergo PE DT, in order to expand the set of neurophysiology-based approaches for quantifying possible DT effects, using parameters that might provide insights about any potential brain connectivity changes in these patients during the PE DT process. Indication is provided for an acute potentiation effect, apparent at late-stage PE DT, on the inter-hemispheric connectivity in frontal areas, as well as for attenuation of the inter-hemispheric connectivity of left frontal and right central areas and for potentiation of the intra-hemispheric connectivity of frontal and central areas, bilaterally, in the transition from early to late-stage PE DT. This pilot study indicates that by using EEG connectivity measures based on ρ and MI, the set of useful neurophysiology-based approaches for quantifying possible DT effects is expanded. In the framework of the present study, the causes of the observed connectivity changes cannot be attributed with certainty to PE DT, but indications are provided that these measures may contribute to a detailed assessment of neurophysiological mechanisms possibly being affected by this therapeutic process.
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Affiliation(s)
- Errikos-Chaim Ventouras
- Department of Biomedical Engineering, Technological Educational Institution of Athens, Agiou Spyridonos Str., Egaleo, Athens, 12210 Greece
| | - Alexia Margariti
- 1st Psychiatric Clinic, Department of Psychiatry, Medical School, Eginition Hospital, University of Athens, 74 Vas. Sophias Ave., Athens, 11528 Greece ; Department of Theater Studies, University of Peloponnese, 21, Vas. Konstantinou Str., Nafplion, 21460 Greece
| | - Paraskevi Chondraki
- 1st Psychiatric Clinic, Department of Psychiatry, Medical School, Eginition Hospital, University of Athens, 74 Vas. Sophias Ave., Athens, 11528 Greece
| | - Ioannis Kalatzis
- Department of Biomedical Engineering, Technological Educational Institution of Athens, Agiou Spyridonos Str., Egaleo, Athens, 12210 Greece
| | - Nicholas-Tiberio Economou
- 1st Psychiatric Clinic, Department of Psychiatry, Medical School, Eginition Hospital, University of Athens, 74 Vas. Sophias Ave., Athens, 11528 Greece
| | - Hara Tsekou
- 1st Psychiatric Clinic, Department of Psychiatry, Medical School, Eginition Hospital, University of Athens, 74 Vas. Sophias Ave., Athens, 11528 Greece
| | - Thomas Paparrigopoulos
- 1st Psychiatric Clinic, Department of Psychiatry, Medical School, Eginition Hospital, University of Athens, 74 Vas. Sophias Ave., Athens, 11528 Greece
| | - Periklis Ktonas
- 1st Psychiatric Clinic, Department of Psychiatry, Medical School, Eginition Hospital, University of Athens, 74 Vas. Sophias Ave., Athens, 11528 Greece
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