ABSTRACT
Alzheimer′s disease (AD) is a neurological disorder of unknown etiology characterized by memory loss and severe intellectual impairment. Transcranial direct current stimulation (tDCS) acts on the brains of AD patients in a non-invasive manner by reducing neurotoxic substances, promoting synaptic plasticity, producing long-term after-effects, weakening neuroinflammation, and reducing oxidative stress in brain tissue, among other mechanisms that affect patients′ memory functions. Therefore, tDCS has become an important tool to improve the memory of AD patients. And the effect of this treatment on memory improvement is influenced by factors such as stimulation site, current level, and stimulation duration.
ABSTRACT
Epilepsy is a serious chronic neurological disorder that can be detected by analysing the brain signals generated by brain neurons, with electroencephalography (EEG) becoming a key tool in the diagnosis of epilepsy. The application of specific methods for processing and analysing EEG signals is important in exploring the working mechanisms of the brain and in the diagnosis of neurological disorders of the brain. The article describes the application of EEG signals in epilepsy treatment through feature extraction, feature classification and other related analysis methods. The article presents an overview of recent research advances through the use of principal component analysis, independent component analysis, wavelet transform, linear discriminant analysis, support vector machine, artificial neural network and decision tree. It provides some reference for the detection and classification of seizures and future research directions.
ABSTRACT
<p><b>BACKGROUND</b>Intra-cerebral hemorrhage (ICH) is a devastating complication that can result from superficial temporal artery-middle cerebral artery (STA-MCA) bypass in patients undergoing treatment for steno-occlusive cerebrovascular disease (CVD). There is a clinical need to find the possible risk factors to prevent ICH, as it is a significant cause of mortality and morbidity. The aim of the study was to investigate the factors associated with delayed ICH after STA-MCA bypass in patients with steno-occlusive CVDs.</p><p><b>METHODS</b>We retrospectively analyzed the records of 163 patients seen from 2002 to 2011 with STA-MCA bypass for steno-occlusive cerebrovascular diseases at the Department of Neurosurgery, Xuan Wu Hospital, Beijing. Demographic and clinical data, including age, gender, vascular risk factors, preoperative syndrome, preoperative National Institutes of Health Stroke Scale (NIHSS), ipsilateral ischemic lesions, classification of steno-occlusive CVDs, donor branches of STA, graft patency, postoperative hypertension, and postoperative-increased MCA velocity were recorded and analyzed. Binary Logistic regression served to identify factors associated with delayed ICH after STA-MCA bypass.</p><p><b>RESULTS</b>We identified 8 (4.9%) patients with delayed ICH after STA-MCA bypass. Patients with hypertension, preoperative stroke, ipsilateral ischemic lesions, postoperative hypertension and postoperative-increased MCA velocity were significantly more prone to experiencing delayed ICH after STA-MCA bypass. Logistic regression analysis shows ipsilateral ischemic lesions, postoperative hypertension, and postoperative-increased MCA velocity remained independent predictors for delayed ICH after STA-MCA bypass.</p><p><b>CONCLUSION</b>Despite the varied associated factors in patients with steno-occlusive CVDs, ipsilateral ischemic lesions, postoperative hypertension, and postoperative-increased MCA velocity could be associated with delayed ICH after STAMCA bypass.</p>