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Nowadays, probabilistic neural networks have been frequently used to pattern discrimination in biological signals despite of non-stationary and individual characteristics of human subjects. In this study, a new approach was proposed to pattern classification for electrocardiography (ECG) signals based on Gaussian mixture model and logarithmic linearization. The objective of this study was to identify...
Analyzing cardiovascular activity under abnormal heart beat is an intricate and vital job to the medical experts and complicated to novice persons. Electrocardiogram is a way to measure or diagnose abnormal heart rhythms to spot heart disease in human beings. These streaming medical signals can be well analyzed or diagnosed only with the prior knowledge. This paper deals with ECG signal analysis based...
Biosignal is a noninvasive measurement of the status of internal organism, such as electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG), etc. With machine learning techniques, these biosignals are normally classified into one of a number of disease categories. Hence, they are ideally suited to support clinician in making diagnostic decision. However, if a given biosignal is...
ECG morphology features, as a kind of significant diagnosis feature, are widely used by experienced cardiologists and highlighted in professional medical textbooks. Fail to utilize it should be one of the most important reasons for the underperformance of automatic ECG classification. In this paper, a framework for ECG morphology features recognition is presented. 1-nearest-neighbor with dynamic time...
Sleep-wake stages discrimination is an important task in the study of cardiorespiratory diseases. Usually this is done by processing physiological signals such as electroencephalogram (EEG) that are, exclusively, recorded in hospitals using polysomnography (PSG) systems. In this paper, we report a simple automatic sleep-wake stages classifier using only RR series obtained from electrocardiogram (ECG)...
The PhysioNet Challenge 2009 addresses the prediction of acute hypotensive episodes (AHEs), which are serious clinical events since they could result in multiple organ failure and eventually in death. This objective is pursued with two different events: (a) event 1: the separation of records with critical AHE (subgroup H1) in the forecast window (FW), the one-hour period immediately following a specified...
In this paper we propose a new method for time series pattern classification. It is based on the generative modeling using Autoregressive(AR) model and optimizing the boundaries between these models using the large margin concepts. The developed model captures the correlations in the time series data. Multi-class classification can be performed directly without performing binary classification. The...
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