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

Aerospace Medicine and Biology: Cumulative index, 1979

This publication is a cumulative index to the abstracts contained in the Supplements 190 through 201 of 'Aerospace Medicine and Biology: A Continuing Bibliography.' It includes three indexes-subject, personal author, and corporate source

Estudio de características frecuenciales de los potenciales de error para el control en continuo mediante interfaces cerebro-máquina

El registro, análisis y procesado de las señales eléctricas generadas por el cerebro tiene aplicaciones en diversos ámbitos como la medicina, la rehabilitación o el entretenimiento. En los últimos años el campo de las interfaces cerebro-computador(BCI) ha experimentado grandes avances incluyendo el control multi-dimensional de dispositivos. En este contexto, desde la Universidad de Zaragoza se ha trabajado en la utilización de información relacionada con los errores para proporcionar información de retro-alimentación durante el uso de la BCI. En particular, se han utilizado los potenciales de error, un tipo de potencial evocado (ERP) que aparece cuando ocurre un evento no esperado. Las interfaces cerebro-computador, incluyendo aquellas basados en potenciales de error, utilizan información en el dominio del tiempo y requieren una fase de calibración previa al control de un dispositivo. Esto implica una gran dificultad para el desarrollo de esta tecnología ya que la señal cerebral depende tanto del usuario, como del día o de la tarea a realizar. Aunque se ha demostrado que los potenciales de error son estables a lo largo del tiempo, trabajos recientes señalan que existen diferencias en la respuesta cerebral en función de la tarea a realizar, en función de la dificultad al evaluar la tarea. Otra dificultad asociada a este tipo de señales es la necesidad de tener un evento muy marcado en el tiempo, o trigger, para elicitar el potencial. Esto dificulta el uso de estos potenciales en situaciones de control realistas como por ejemplo un robot móvil. En este caso, no está claro cuándo el usuario va a percibir un error y si se va a generar el potencial de error correspondiente. Los objetivos de esta tesis de Máster son analizar la posibilidad de eliminar el trigger de este tipo de señales 1) estudiando un nuevo tipo de características en el dominio de la frecuencia y analizando si estas últimas son más robustas ante variaciones en la latencia de respuesta del potencial de error; y 2) evaluando la capacidad de estas características para proporcionar información de retro-alimentación durante el control en continuo de un dispositivo. Para ello, este trabajo se divide en tres partes: 1) Estudio y comparación de la generalización de las características temporales y frecuenciales de los potenciales de error cuando se hace transferencia entre tareas en protocolos con un marcador bien definido, es decir, acciones discretas. Refiriéndose con transferencia a entrenar un clasificador con las características extraídas de una tarea y emplearlo para reconocer eventos en una tarea distinta. 2) Diseño de un protocolo (en pantalla) para el estudio de los potenciales en continuo (acciones continuas donde no existe marcador del evento, o si lo existe no se conoce dónde está). Adquisición de datos de EEG con varios sujetos. Procesamiento de datos para analizar la presencia de potenciales de error y su detección en continuo. 3) Diseño de un protocolo experimental para el control en línea de un robot móvil mediante el uso de potenciales de error y su clasificación en continuo. Experimentación preliminar con varios sujetos y análisis de los resultados obtenidos

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 255)

This bibliography lists 278 reports, articles and other documents introduced into the NASA scientific and technical information system in January 1984

Aerospace medicine and Biology: A continuing bibliography with indexes, supplement 177

This bibliography lists 112 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1978

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 317)

This bibliography lists 182 reports, articles and other documents introduced into the NASA scientific and technical information system in November, 1988

The diagnosis of depression using psychometric instruments and quantitative measures of electroencephalographic activity

Depressive disorders are among the most common and familiar of all psychiatric disorders, ironically, individuals suffering from depressive disorders are likely to never be diagnosed or treated. With the depressive disorders now being considered the source of an emerging public health crisis, a variety of public and private sector agencies have sought to address the issues of under-diagnosis and under-treatment. Despite their best efforts, about half of individuals with depressive disorders are not accurately diagnosed. The absence of a gold standard biological marker that can be used adjunctively with psychometric diagnostic instruments may be a factor that hinders primary practice physicians from formulating accurate diagnoses. Though differential regional cerebral bloodflow (rCBF) is known to discriminate between depressives and non-depressives in laboratory settings, the technology required to use this measure is hazardous and very expensive. Using less expensive EEG technology, one neurophysiological correlate of depression has already been identified - Alpha wave activation asymmetry in the frontal lobes. Other features of the human EEG, particularly coherence and phase, were hypothesized as potential biological markers of depression. A 19-channel QEEG recording using the International 10/20 system for electrode placement was used to obtain data from depressed individuals. An analysis of the qeEG records revealed coherence anomalies at the electrode pair F7-F8 in the theta and beta bandpasses. Phase anomalies were found at the F1-F2 site in the alpha and beta bandpasses. Single-band analysis of amplitude topography revealed excessive amplitude in the frontal region at the Ihz, 2hz, and 3hz frequencies

The role of spindles activity in the consolidation of neutral and emotional stimuli

Literature suggests that sleep plays a role in memory consolidation, including emotional memories, but it is still argued the underlying mechanism of this process. In this regard, spindles are sleep wave components whose role has been associated with memory consolidation, although researchers still report conflicting results. This research aims to understand if sleep spindle activity has a role in memory consolidation and whether it acts differently on neutral and emotional stimuli.Literature suggests that sleep plays a role in memory consolidation, including emotional memories, but it is still argued the underlying mechanism of this process. In this regard, spindles are sleep wave components whose role has been associated with memory consolidation, although researchers still report conflicting results. This research aims to understand if sleep spindle activity has a role in memory consolidation and whether it acts differently on neutral and emotional stimuli

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 267, January 1985

This publication is a cumulative index to the abstracts contained in the Supplements 255 through 266 of Aerospace Medicine and Biology: A Continuing Bibliography. It includes seven indexes--subject, personal author, corporate source, foreign technology, contract number, report number, and accession number

Towards ai-based interactive game intervention to monitor concentration levels in children with attention deficit

—Preliminary results to a new approach for neurocognitive training on academic engagement and monitoring of attention levels in children with learning difficulties is presented. Machine Learning (ML) techniques and a Brain-Computer Interface (BCI) are used to develop an interactive AI-based game for educational therapy to monitor the progress of children’s concentration levels during specific cognitive tasks. Our approach resorts to data acquisition of brainwaves of children using electroencephalography (EEG) to classify concentration levels through model calibration. The real-time brainwave patterns are inputs to our game interface to monitor concentration levels. When the concentration drops, the educational game can personalize to the user by changing the challenge of the training or providing some new visual or auditory stimuli to the user in order to reduce the attention loss. To understand concentration level patterns, we collected brainwave data from children at various primary schools in Brazil who have intellectual disabilities e.g. autism spectrum disorder and attention deficit hyperactivity disorder. Preliminary results show that we successfully benchmarked (96%) the brainwave patterns acquired by using various classical ML techniques. The result obtained through the automatic classification of brainwaves will be fundamental to further develop our full approach. Positive feedback from questionnaires was obtained for both, the AI-based game and the engagement and motivation during the training sessions