Visual analysis of electroencephalography (EEG) background and reactivity during therapeutic hypothermia provides important outcome information, but is time-consuming and not always consistent between reviewers. Automated EEG analysis may help quantify the brain damage. Forty-six comatose patients in therapeutic hypothermia, after cardiac arrest, were included in the study. EEG background was quantified with burst-suppression ratio (BSR) and approximate entropy, both used to monitor anesthesia. Reactivity was detected through change in the power spectrum of signal before and after stimulation. Automatic results obtained almost perfect agreement (discontinuity) to substantial agreement (background reactivity) with a visual score from EEG-certified neurologists. Burst-suppression ratio was more suited to distinguish continuous EEG background from burst-suppression than approximate entropy in this specific population. Automatic EEG background and reactivity measures were significantly related to good and poor outcome. We conclude that quantitative EEG measurements can provide promising information regarding current state of the patient and clinical outcome, but further work is needed before routine application in a clinical setting.
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http://dx.doi.org/10.1177/1550059413509616 | DOI Listing |
Eur J Orthod
December 2024
Department of General Surgery and Medical-Surgical Specialties, Section of Orthodontics, University of Catania, Policlinico Universitario 'Gaspare Rodolico-San Marco', Via Santa Sofia 78, 95123, Catania, Italy.
Background/objectives: Evidence suggests nasal airflow resistance reduces after rapid maxillary expansion (RME). However, the medium-term effects of RME on upper airway (UA) airflow characteristics when normal craniofacial development is considered are still unclear. This retrospective cohort study used computer fluid dynamics (CFD) to evaluate the medium-term changes in the UA airflow (pressure and velocity) after RME in two distinct age-based cohorts.
View Article and Find Full Text PDFEClinicalMedicine
December 2024
Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Background: Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by physical exam, which can be intermittent and subjective. Reliable, continuous methods are needed. We hypothesized that our computer vision method to track movement, pose artificial intelligence (AI), could predict neurologic changes in the neonatal intensive care unit (NICU).
View Article and Find Full Text PDFBackground: Long QT Syndrome Type-2 (LQT2) is due to loss-of-function variants. encodes K 11.1 that forms a delayed-rectifier potassium channel in the brain and heart.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.
Background: The diagnosis and treatment of epilepsy continue to face numerous challenges, highlighting the urgent need for the development of rapid, accurate, and non-invasive methods for seizure detection. In recent years, advancements in the analysis of electroencephalogram (EEG) signals have garnered widespread attention, particularly in the area of seizure recognition.
Methods: A novel hybrid deep learning approach that combines feature fusion for efficient seizure detection is proposed in this study.
J Affect Disord
January 2025
Center for Functional Neurosurgery, Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address:
Background: Parkinson's disease (PD) is primarily characterized by motor symptoms, but patients also experience a relatively high prevalence of non-motor symptoms, including emotional and cognitive impairments. While the subthalamic nucleus (STN) is a common target for deep brain stimulation to treat motor symptoms in PD, its role in emotion processing is still under investigation. This study examines the subthalamic neural oscillatory activities during facial emotion processing and its association with affective characteristics.
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