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A detection approach integrating R-R interval features extraction and classification for congestive heart failure (CHF) is presented in this paper. In R-R interval features extraction, we use empirical mode decomposition (EMD) to decompose each subject's R-R interval signal into several intrinsic mode functions (IMF), and use singular value decomposition (SVD) to extract the ranked singular values...
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...
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 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...
Noninvasive diagnosis method of coronary artery disease is proposed based on the instantaneous frequency estimate of diastolic murmurs and support vector machine(SVM) classifier. Hilbert-Huang transform is studied to analyze the instantaneous frequency of diastolic murmurs. The weighted instantaneous frequency is introduced. Using statistical quantities of weighted instantaneous frequency forms feature...
A robust method for removal of artifacts such as eye blinks and electrocardiogram (ECG) from the electroencephalograms (EEGs) has been developed in this paper. The proposed hybrid method fuses support vector machines (SVMs) based classification and blind source separation (BSS) based on independent component analysis (ICA). The carefully chosen features for the classifier mainly represent the data...
We expand the idea to develop new bio-signal processing tools that could predict possibility of future risk of abnormalities in ECG signals. The goal is to detect an inherent defect hidden in an ECG signal using wavelet analysis and support vector machines. We apply singular value decomposition analysis of spectral energy distribution in time-frequency plane to extract features, which is essentially...
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