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Presented paper describes a system of biomedical signal classifiers with preliminary feature extraction stage based on matched wavelets analysis, where two structures of classifier using Neural Networks (NN) and Support Vector Machine (SVM) are applied. As a pilot study the rules extraction algorithm applied for two of mentioned machine learning approaches (NN & SVM) was used. This was made to...
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...
Premature ventricular contraction (PVC) beats are of great importance in evaluating and predicting life threatening ventricular arrhythmias. The aim of this study is to improve the diagnosis level of detection of PVC arrhythmia from ECG signals. This improvement is based on an appropriate choice of features for the selected task. We extracted fourteen features including, temporal, morphological features...
In this work, we proposed a method for a binary classification in an EEG-based brain computer interface (BCI) with wavelet packet transform and neural networks. For feature extraction, we introduced a new method which combined the slow cortical potentials (SCPs) and the specific energy from the time-frequency domain in beta-band via the wavelet packet transform. A 3-layer perceptron established by...
The aim of this paper is to make an overall comparison between neural networks and support vector machines as two different types of artificial intelligence techniques applied to classify the most three widespread pathological factors as the consequences of vocal fold inflammation, known as vocal fold edema, nodules and polyp. As the analogous effects of these three pathological factors in changing...
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