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The problem of patient disorder classification and prediction from biological signals is addressed. We approach the problem from the perspective of nonlinear dynamical systems. Explored signals are ECG and EEG. We propose a combination of linear and nonlinear features for classification of four different types of heart rhythms through heart rate variability analysis. Classification accuracy is evaluated...
Sleep is a circadian rhythm essential for human life. Many events occur in the body during this state. In the past, significant efforts have been made to provide clinicians with reliable and less intrusive tools to automatically classify the sleep stages and detect apnea events. A few systems are available in the market to accomplish this task. However, sleep specialists may not have full confidence...
This paper addresses the feature selection problem by using a discriminant and redundancy based method to select a feature subset with high discriminatory power between the classes of newborn heart rate variability (HRV) corresponding to seizure and non-seizure. The proposed method combines the Fast Correlation Based Filter (FCBF) criteria for redundancy analysis with the area under the Receiver Operating...
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