View clinical trials related to Electroencephalogram.
Filter by:Purpose：Construct a perioperative EEG database for elderly patients under general anesthesia, and explore the correlation between their EEG spectrum characteristics and the occurrence and severity of postoperative delirium. Content：This study aims to investigate patients undergoing elective orthopedic surgery, analyze the changes in perioperative EEG spectrum, correlate with the occurrence and severity of POD, and explore the relationship between perioperative EEG changes and the development of postoperative delirium. Method: Patients aged over 60 years old who are scheduled for orthopedic surgery and plan to stay in hospital for more than two days were selected. All patients underwent preoperative MMSE and 3D-CAM assessments. Subsequently, anesthetic depth monitor electrodes were applied to the occipital or frontal-temporal regions of the patient to collect electroencephalograms of the occipital lobe during conscious and quiet states, the frontal lobe during general anesthesia, and the recovery room. During PACU, scores were calculated based on the CAM-ICU scale evaluation. During the first 5 days after surgery, patients were evaluated every day between 13:00 and 20:00 using 3D-CAM, or at any time when they showed obvious symptoms of delirium, unless they were discharged or taking sedatives (RASS < -3). Ten minutes after the end of the evaluation, patients' electroencephalograms were monitored in a conscious and quiet state, or in a state of obvious delirium. Patients were divided into a delirium group and a non-delirium group based on whether they developed delirium after surgery. The characteristics of electroencephalograms before, during, and after surgery were analyzed in both groups of patients.Research significance:The results of this study may provide objective indicators and theoretical basis for monitoring and diagnosing the occurrence and development of POD, which can help clinical doctors identify patients with increased delirium risk in the early stage, and adjust the plan in a timely manner to change the triggering risk factors of POD.
Study the therapeutic effect and potential neural mechanisms of cerebellar iTBS mode transcranial magnetic stimulation on Alzheimer's disease patients through MRI and EEG.
This study aims to prospectively evaluate the relationship between changes in EEG and hormonal responses induced by endotracheal intubation and surgical incision following general anesthesia.
Transcutaneous electrical acupoint stimulation (TEAS) was reported to benefit the patients undergoing surgeries by reducing anesthetics consumption and decreasing anesthesia related adverse effects. Electroencephalogram (EEG) and EEG-related indicators are important indicators reflecting the conscious state of the brain, and different anesthetic drugs and anesthesia depths cause different EEG characteristic changes. The mechanism by which TEAS improves postoperative delirium (POD) is not clear, and whether changes in EEG characteristic parameters is involved needs to be further explored. Therefore, this study aims to observe the effect of TEAS at Neiguan and Shenmen acupoint on POD in elderly patients undergoing abdominal surgery, and to explore the EEG related mechanism underlying TEAS improving POD.
Parkinson's disease (PD) is a progressive and disabling neurodegenerative disease, clinically characterized by motor and non-motor symptoms. The potential of the "Transcranial direct current stimulation" (tDCS) for symptomatic improvement in these patients has been demonstrated, but the factors associated with the best therapeutic response are not known. The electroencephalogram (EEG) is considered as a diagnostic and prognostic biomarker of PD, and has been used in recent studies associated with machine-learning methods to identify predictors of responses in neurological and psychiatric conditions. Using connectivity-based prediction and machine-learning, the investigators intend to identify and compare characteristics related to baseline resting EEG between PD responders and non-responders to tDCS treatment. The recruited participants will be randomized to treatment with active tDCS associated with dual-task motor therapy or motor therapy with visual cues. A resting-state electroencephalography (EEG) will be recorded prior to the start of the treatment. The investigators will determine clinical improvement labels used for machine learning classification, in baseline and posttreatment assessments and will use three different methods to categorize the data into two classes (low or high improvement): Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Extreme Learning Machine (ELM). The functional label will be based on the Timed Up and Go Test recorded at baseline and posttreament of tDCS treatment.