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An active paradigm was employed to produce reliable and prominent target response in an auditory brain computer interface (BCI), in which subject's voluntary recognition of the property of a target human voice enhances the discriminability between target and non-target EEG response. Furthermore, to adaptively decide the optimal number of trials being averaged for SVM classification, a statistical...
To realize brain computer interface, a recording electroencephalogram (EEG) and determining whether or not P300 is evoked by the presented stimulus have become increasingly important. Using the machine learning method for this classification is effective, but constructing feature vectors with all data points might result in very high-dimensional data. Because such redundant features are undesirable...
Two effective ECG beat classification methods based on signal decomposition were compared in terms of effective feature selection and noise tolerance. The HOS-DWT-FFBNN method associated with the linear correlation based filter (LCBF) provides imposing capability to select the more representative features than the IC reordering method OWSL associated with the ICA-SVM method. Both methods are insensitive...
In this study, heartbeat time series are classified using support vector machines (SVMs). Statistical methods and signal analysis techniques are used to extract features from the signals. The SVM classifier is favorably compared to other neural network-based classification approaches by performing leave-one-out cross validation. The performance of the SVM with respect to other state-of-the-art classifiers...
In this study, we discuss the use of support vector machine (SVM) learning to classify heart rate signals. Each signal is represented by an attribute vector containing a set of statistical measures for the respective signal. At first, the SVM classifier is trained by data (attribute vectors) with known ground truth. Then, the classifier learnt parameters can be used for the categorization of new signals...
In this study, we discuss the use of support vector machine (SVM) learning to classify heart rate signals. Each signal is represented by an attribute vector containing a set of statistical measures for the respective signal. At first, the SVM classifier is trained by data (attribute vectors) with known ground truth. Then, the classifier learnt parameters can be used for the categorization of new signals...
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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