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Many brain disorders are diagnosed by analysing the EEG signals. EEG refers to the recording of the brain's spontaneous electrical activity over a short period of time. In this paper an efficient approach for detecting the presence of epileptic seizures in EEG signals is presented. Epilepsy is a disease due to temporary alternation in brain functions due to abnormal electrical activity of a group...
Bacterial meningitis is still a life-threatening disease, and early diagnosis of pathogen can be crucial to improving survival rate. Using the surface-enhanced Raman scattering (SERS) platform developed by our group, the pathogens can be differentiated on the basis of their SERS spectra which are believed to related to their surface chemical components. We collected the SERS spectra of ten pathogens:...
The electrocorticogram(ECoG) is proved to have high signal-to-noise ratio(SNR), which makes it better fitting for BCIs. And this paper represents a kind of classification method of ECoG signals for motor imagery tasks(left finger and tongue). Band power(BP) with the frequency band of [8 30] was extracted as the feature, and the linear discriminant analysis(LDA), k-nearest neighbor(kNN) rules and linear...
The prediction of outcome in newborns with hypoxic ischemic encephalopathy (HIE) is a problematic task. Here, the ability of a combination of clinical, heart rate and EEG measures to predict outcome at 2 years is investigated. One hour of EEG and ECG recordings were obtained from newborns 24 hours after birth. Each newborn was reassessed at 24 months to investigate their neurodevelopmental outcome...
High usability myo-controlled devices require robust classification schemes during dynamic contractions. Therefore, this study investigates the impact of the training data set on the performance of several pattern recognition algorithms during dynamic contractions. It is shown that combined with a threshold to detect the onset of the contraction, current pattern recognition algorithms used on static...
The study of the intestinal interdigestive motor migratory complex (IMMC) is relevant in gastroenterology because most of the gastrointestinal pathologies are reflected in anomalies of the IMMC. The aim of this work is to develop an automatic classifier to discriminate among the different intestinal contractile activity degrees (quiescence, irregular, and maximum contractile activity) that compound...
Objective: To realize the automatic classification between melancholic and healthy persons by extracting the disease features from the melancholic's EEG signals. Methods: 1. Extracting the features from the EEG signals of melancholic and healthy persons; 2. Obtaining the characteristic parameters such as the maximum, minimum, mean and standard deviation of EEG power spectrum amplitude; 3. Training...
Brain Computer Interface one of hopeful interface technologies between humans and machines. Electroencephalogram-based Brain Computer Interfaces have become a hot spot in the research of neural engineering, rehabilitation, and brain science. The artifacts are disturbance that can occur during the signal acquisition and that can alter the analysis of the signals themselves. Detecting artifacts produced...
Optimizing a classifier is a subject of great interest in the research area. A lot of methods inspired of biological metaphors are proposed for this task. This paper present a new algorithm based on the natural immune metaphors which select a proper subset of features and optimal parameters of a support vector machines (SVM) classifier. The designed optimization method is validated for ERP assessment...
In this study we have investigated the classification of old myocardial infarction through the analysis of 192 lead body surface potential maps (BSPM). Following an analysis of the most prominent features based on a signal to noise ratio ranking criterion the top 6 features were selected. These features were subsequently used as inputs to a series of supervised classification models in the form of...
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