3,821 research outputs found
Exploring the alterations in the distribution of neural network weights in dementia due to alzheimer’s disease
Producción CientíficaAlzheimer’s disease (AD) is a neurodegenerative disorder which has become an outstanding social problem. The main objective of this study was to evaluate the alterations that dementia due to AD elicits in the distribution of functional network weights. Functional connectivity networks were obtained using the orthogonalized Amplitude Envelope Correlation (AEC), computed from source-reconstructed resting-state eletroencephalographic (EEG) data in a population formed by 45 cognitive healthy elderly controls, 69 mild cognitive impaired (MCI) patients and 81 AD patients. Our results indicated that AD induces a progressive alteration of network weights distribution; specifically, the Shannon entropy (SE) of the weights distribution showed statistically significant between-group differences (p < 0.05, Kruskal-Wallis test, False Discovery Rate corrected). Furthermore, an in-depth analysis of network weights distributions was performed in delta, alpha, and beta-1 frequency bands to discriminate the weight ranges showing statistical differences in SE. Our results showed that lower and higher weights were more affected by the disease, whereas mid-range connections remained unchanged. These findings support the importance of performing detailed analyses of the network weights distribution to further understand the impact of AD progression on functional brain activity.Ministerio de Ciencia e Innovación-Agencia Estatal de Investigación, Fondo Europeo de Desarrollo Regional (FEDER) - (project PGC2018-098214- A-I00)Comisión Europea y Fondo Europeo de Desarrollo Regional (FEDER) - (Cooperation Programme Interreg V-A Spain-Portugal POCTEP 2014–20200
Prediction of the Outcome in Cardiac Arrest Patients Undergoing Hypothermia Using EEG Wavelet Entropy
Cardiac arrest (CA) is the leading cause of death in the United States. Induction of hypothermia has been found to improve the functional recovery of CA patients after resuscitation. However, there is no clear guideline for the clinicians yet to determine the prognosis of the CA when patients are treated with hypothermia. The present work aimed at the development of a prognostic marker for the CA patients undergoing hypothermia. A quantitative measure of the complexity of Electroencephalogram (EEG) signals, called wavelet sub-band entropy, was employed to predict the patients’ outcomes. We hypothesized that the EEG signals of the patients who survived would demonstrate more complexity and consequently higher values of wavelet sub-band entropies.
A dataset of 16-channel EEG signals collected from CA patients undergoing hypothermia at Long Beach Memorial Medical Center was used to test the hypothesis. Following preprocessing of the signals and implementation of the wavelet transform, the wavelet sub-band entropies were calculated for different frequency bands and EEG channels. Then the values of wavelet sub-band entropies were compared among two groups of patients: survived vs. non-survived. Our results revealed that the brain high frequency oscillations (between 64-100 Hz) captured from the inferior frontal lobes are significantly more complex in the CA patients who survived (pvalue ≤ 0.02). Given that the non-invasive measurement of EEG is part of the standard clinical assessment for CA patients, the results of this study can enhance the management of the CA patients treated with hypothermia
Dynamic Complexity and Causality Analysis of Scalp EEG for Detection of Cognitive Deficits
This dissertation explores the potential of scalp electroencephalography (EEG) for the detection and evaluation of neurological deficits due to moderate/severe traumatic brain injury (TBI), mild cognitive impairment (MCI), and early Alzheimer’s disease (AD). Neurological disorders often cannot be accurately diagnosed without the use of advanced imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Non-quantitative task-based examinations are also used. None of these techniques, however, are typically performed in the primary care setting. Furthermore, the time and expense involved often deters physicians from performing them, leading to potential worse prognoses for patients.
If feasible, screening for cognitive deficits using scalp EEG would provide a fast, inexpensive, and less invasive alternative for evaluation of TBI post injury and detection of MCI and early AD. In this work various measures of EEG complexity and causality are explored as means of detecting cognitive deficits. Complexity measures include eventrelated Tsallis entropy, multiscale entropy, inter-regional transfer entropy delays, and regional variation in common spectral features, and graphical analysis of EEG inter-channel coherence. Causality analysis based on nonlinear state space reconstruction is explored in case studies of intensive care unit (ICU) signal reconstruction and detection of cognitive deficits via EEG reconstruction models. Significant contributions in this work include: (1) innovative entropy-based methods for analyzing event-related EEG data; (2) recommendations regarding differences in MCI/AD of common spectral and complexity features for different scalp regions and protocol conditions; (3) development of novel artificial neural network techniques for multivariate signal reconstruction; and (4) novel EEG biomarkers for detection of dementia
EEG-Based Mental States Identification
In this thesis, we focus on the identification of mental states described according to the definition of awareness and wakefulness. Using algorithmic methods, we show that it is possible to differentiate between two brain states based on the brain electrical activity collected by EEG. We begin by explaining the overall theoretical framework which enabled us to develop the detection of brain states. It starts with data acquisition. Following that, we analyse the pre-processing of the data before the extraction of features. Finally, we go on to statistically evaluate the results. In order to achieve this task, we propose four experiments. We will first focus on exploring different brain states for patients in Intensive Care Unit (ICU) such as coma and quasi-brain-death states. To distinguish these states, we use a signal processing method based on the EEG signal phase. A phase synchrony index based on Shannon entropy was used to separate the two states. Statistical validation revealed a significant difference between the two via delta-alpha and theta-alpha frequency couplings. Next, we studied the neuronal mechanisms which is used to understand consciousness. We did that by using dipole modelling. This method was applied to local-global experiment and the paradigm of auditory deviance with two hierarchical levels. A modulation of this experiment is generated by a sedative Propofol to study the effect on conscious states. This experiment was analysed in greater detail using the Imaging Method to do the source localisation. We analysed three different time-windows. The first window corresponds to the local effect during the initial response of the brain. We assume that this input is related to auditory
areas and activates the temporal lobe. The second window is at the interface between the local effect and the global effect. In here we are especially interested in the interaction between these two effects during the second window. Finally, the third window will enable us to study the overall effect. We hypothesize a global activation of neural networks corresponding to consciousness as described by the global workspace theory. The third experiment focused on brain states high-level athletes experience during a cognitive task. Two different groups of cyclists, endurances and sprinters,
were asked to do a Stroop task for 30 minutes. We studied the influence of the task and the potential differences in brain activity between the two groups. We found through the frequency analysis that the brain activity between the two groups can be distinguished, but was not modified by the cognitive task.
Finally, we studied the influence of the sensorimotor loop on the brain. A physical task was applied, consisting in lifting a weight with two measurements, where the lifting arm can also be in fatigued state. Using sources reconstruction from EEG, we studied the impact of weight-lifting and the physical fatigue upon neuronal activities and the neuronal origins of these effects. We found that only
weight has an effect, whereas fatigue effect is not significant. We conclude with a discussion of the mechanisms of consciousness analysed via algorithmic methods and some future work for the possibility to distinguish better between different cognitive states
Aerospace medicine and biology: A continuing bibliography with indexes (supplement 299)
This bibliography lists 96 reports, articles, and other documents introduced into the NASA scientific and technical information system in June, 1987
Discriminative power of EEG-based biomarkers in major depressive disorder: A systematic review
Currently, the diagnosis of major depressive disorder (MDD) and its subtypes is mainly based on subjective assessments and self-reported measures. However, objective criteria as Electroencephalography (EEG) features would be helpful in detecting depressive states at early stages to prevent the worsening of the symptoms. Scientific community has widely investigated the effectiveness of EEG-based measures to discriminate between depressed and healthy subjects, with the aim to better understand the mechanisms behind the disorder and find biomarkers useful for diagnosis. This work offers a comprehensive review of the extant literature concerning the EEG-based biomarkers for MDD and its subtypes, and identify possible future directions for this line of research. Scopus, PubMed and Web of Science databases were researched following PRISMA’s guidelines. The initial papers’ screening was based on titles and abstracts; then full texts of the identified articles were examined, and a synthesis of findings was developed using tables and thematic analysis. After screening 1871 articles, 76 studies were identified as relevant and included in the systematic review. Reviewed markers include EEG frequency bands power, EEG asymmetry, ERP components, non-linear and functional connectivity measures. Results were discussed in relations to the different EEG measures assessed in the studies. Findings confirmed the effectiveness of those measures in discriminating between healthy and depressed subjects. However, the review highlights that the causal link between EEG measures and depressive subtypes needs to be further investigated and points out that some methodological issues need to be solved to enhance future research in this field
Intelligent Biosignal Processing in Wearable and Implantable Sensors
This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine
Autonomic impairment of patients in coma with different Glasgow coma score assessed with heart rate variability
Primary objective: The objective of this study is to assess the functional state of the autonomic nervous system in healthy individuals and in individuals in coma using measures of heart rate variability (HRV) and to evaluate its efficiency in predicting mortality. Design and Methods: Retrospective group comparison study of patients in coma classified into two subgroups, according to their Glasgow coma score, with a healthy control group. HRV indices were calculated from 7 min of artefact-free electrocardiograms using the Hilbert–Huang method in the spectral range 0.02–0.6 Hz. A special procedure was applied to avoid confounding factors. Stepwise multiple regression logistic analysis (SMLRA) and ROC analysis evaluated predictions. Results: Progressive reduction of HRV was confirmed and was associated with deepening of coma and a mortality score model that included three spectral HRV indices of absolute power values of very low, low and very high frequency bands (0.4-0.6 Hz). The SMLRA model showed sensitivity of 95.65%, specificity of 95.83%, positive predictive value of 95.65%, and overall efficiency of 95.74%. Conclusions: HRV is a reliable method to assess the integrity of the neural control of the caudal brainstem centres on the hearts of patients in coma and to predict patient mortality
Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 145
This bibliography lists 301 reports, articles, and other documents introduced into the NASA scientific and technical information system in August 1975
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