View clinical trials related to Consciousness, Loss of.
Filter by:This observational study aims to describe the incidence of episodes of disconnected consciousness (including near-death experience (NDE)) and episodes of connected consciousness in patients admitted to the resuscitation room, who survived a critical condition and who meet at least one of these criteria during their stay in the resuscitation room: (1) deep sedation, (2) intubation, (3) cardiopulmonary resuscitation, or (4) (non-drug-induced) Glasgow Coma Scale score = 3. We also investigate the potential (neuro)physiological markers and biomarkers. In order to help determine the potential risk factors of such episodes, cognitive factors such as dissociative propensity are also investigated. Unexpected visual and auditory stimuli will be displayed. In addition, we assess the evolution of memory, as well as short- and long-term consequences on quality of life, anxiety, and attitudes towards care. Memory of patients who did not meet the above-mentioned criteria are also investigated. A group of 15 healthy participants will be invited to test the stimuli display. Finally, (neuro)physiological parameters of a subsample of dying patients are also investigated.
Early prediction of outcomes after acute brain injury (ABI) remains a major unsolved problem. Presently, physicians make predictions using clinical examination, traditional scoring systems, and statistical models. In this study, we will use a novel technique, "SeeMe," to objectively assess the level of consciousness in patients suffering from comas following ABI. SeeMe is a program that quantifies total facial motion over time and compares the response after a spoken command (i.e. "open your eyes") to a pre-stimulus baseline.
The current evaluations of the levels of consciousness during anesthesia have limited precision. This can produce negative clinical consequences such as intraoperative awareness or neurological damage due to under- or over-infusion of anesthesia, respectively. The study's objective is to determine and classify biomarkers of electrical and hemodynamical brain activity associated with the levels of consciousness between wakefulness and anesthesia. For this purpose, a parietal electroencephalography (EEG) and a functional near-infrared spectroscopy (fNIRS) measurement paradigm will be used, as well as machine-learning. Volunteering patients (n = 25), who will be subject to an endoscopy procedure, will be measured during the infusion of anesthesia with propofol. EEG and fNIRS parameters will then be related to the Modified Ramsay clinical scale of consciousness.
The electroencephalography (EEG) is a noninvasive medical technique for monitoring and recording the electrical activity of brain. The Hilbert-Huang Transformation (HHT) was proposed to decompose EEG signal into intrinsic mode functions (IMF). HHT can obtain instantaneous frequency data and work well for nonstationary and nonlinear data. We applied this method in perioperative EEG signal analysis in order to find the energy shift and quantify the energy change during general anesthesia. Ketamine was a depolarized sedative which was wildly used in anesthesia. We are trying to find the energy change after ketamine injection, and the interaction between different oscillations in EEG. The whole brain mapping for ketamine and other sedatives interaction is the next step.
Novel technology enables it to monitor noninvasively the vital signs of a patient. Such a monitoring is immediately required to improve patient safety and to reduce hospital readmissions. In this study, novel bed- and wearable sensors are studied for this purpose.