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Arheix-Parras S, Glize B, Guehl D, Python G. Electrophysiological Changes in Patients with Post-stroke Aphasia: A Systematic Review. Brain Topogr 2023; 36:135-171. [PMID: 36749552 DOI: 10.1007/s10548-023-00941-4] [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: 07/16/2022] [Accepted: 01/19/2023] [Indexed: 02/08/2023]
Abstract
Background Magnetoencephalography (MEG) and electroencephalography (EEG) record two main types of data: continuous measurements at rest or during sleep, and event-related potentials/evoked magnetic fields (ERPs/EMFs) that involve specific and repetitive tasks. In this systematic review, we summarized longitudinal studies on recovery from post-stroke aphasia that used continuous or event-related temporal imaging (EEG or MEG). Methods We searched PubMed and Scopus for English articles published from 1950 to May 31, 2022. Results 34 studies were included in this review: 11 were non-interventional studies and 23 were clinical trials that used specific rehabilitation methods, neuromodulation, or drugs. The results of the non-interventional studies suggested that poor language recovery was associated with slow-wave activity persisting over time. The results of some clinical trials indicated that behavioral improvements were correlated with significant modulation of the N400 component. Discussion Compared with continuous EEG, ERP/EMF may more reliably identify biomarkers of therapy-induced effects. Electrophysiology should be used more often to explore language processes that are impaired after a stroke, as it may highlight treatment challenges for patients with post-stroke aphasia.
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Affiliation(s)
- Sophie Arheix-Parras
- ACTIVE team, Bordeaux Population Health, INSERM UMR 1219, university of Bordeaux, 33000, Bordeaux, France. .,Institut Universitaire des Sciences de la Réadaptation, University of Bordeaux, 33000, Bordeaux, France. .,Department of physical medicine and rehabilitation, CHU de Bordeaux, 33000, Bordeaux, France.
| | - Bertrand Glize
- ACTIVE team, Bordeaux Population Health, INSERM UMR 1219, university of Bordeaux, 33000, Bordeaux, France.,Institut Universitaire des Sciences de la Réadaptation, University of Bordeaux, 33000, Bordeaux, France.,Department of physical medicine and rehabilitation, CHU de Bordeaux, 33000, Bordeaux, France
| | - Dominique Guehl
- Pole des neurosciences cliniques, CHU de Bordeaux, 33000, Bordeaux, France.,IMN CNRS UMR 5293, CNRS, University of Bordeaux, 33000, Bordeaux, France
| | - Grégoire Python
- Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland.,Department of Clinical Neurosciences, Lausanne University Hospital (CHUV), Lausanne, Switzerland
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Vajargah KF, Benis SG, Golshan HM. Detection of the quality of vital signals by the Monte Carlo Markov Chain (MCMC) method and noise deleting. Health Inf Sci Syst 2021; 9:26. [PMID: 34295461 PMCID: PMC8249561 DOI: 10.1007/s13755-021-00157-5] [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: 01/03/2021] [Accepted: 06/05/2021] [Indexed: 10/21/2022] Open
Abstract
Vital signal renovation plays an important role in a wide range of applications, including signal analysis and diagnosing diseases through it. Therefore, it is salient to get the main content of the vital signal. In this research, a new approach to the problem of noise removal from vital signals is presented based on random optimization through Monte Carlo Markov Chain (MCMC) sampling. For this purpose, the problem of noise omission from the vital signal is described as a Bayesian squared minimization problem, and considering a non-parametric random approach to solve this problem, the Monte Carlo Markov Chain noise omission approach is flexibly adapted to the noise detection domain in vital signals. To test the performance of the proposed method, four types of vital signals have been used: Medical images, ECG electrocardiogram signals, EEG brain signals as well as ENG nerve and muscle signals. The results of the experiments show that the use of sampling technique based on Gaussian distribution and, retrieving the signal based on the weighted average in the selected samples allows a more accurate estimate of the ideal signal. This more accurate estimation minimizes the difference between the actual and the retrieved signals. As a result, in addition to reducing the mean error squares, the signal-to-noise ratio increases.
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Affiliation(s)
| | - Sara Ghaniyari Benis
- Department of Statistics, Tehran North Branch, Islamic Azad University, Tehran, Iran
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