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Advances in monitoring cardiovascular signals. Contribution of nonlinear signal processing
2011 / IEEE / 978-1-4577-1589-1
This item was taken from the IEEE Conference ' Advances in monitoring cardiovascular signals. Contribution of nonlinear signal processing ' Monitoring procedures are the basis to evaluate the clinical state of patients and to assess changes in their status, thus providing necessary interventions in time. To obtain this important objective it is necessary to integrate technological development with systems performing biomedical knowledge extraction and classification. Methods extracting non linear characteristics from HRV signal are presented and discussed to stress that integrated and multiparametric signal processing approaches can contribute to new diagnostic and classification indices. Examples report heart rate variability analysis in long periods in patients with cardiovascular disease. Fetal ECG monitoring is another example. In this case, coupling nonlinear parameters and linear time and frequency techniques increases diagnostic power and reliability of the monitoring. The paper shows that integrated signal analysis is very helpful to describe pathophysiological mechanisms involved in the cardiovascular and neural system control. It is a reliable basis to set up knowledge-based monitoring systems.
Linear Frequency Technique
Integrated Signal Analysis
Neural System Control
Heart Rate Variability
Time Series Analysis
Medical Signal Processing
Knowledge-based Monitoring Systems
Cardiovascular Signal Monitoring
Nonlinear Signal Processing
Biomedical Knowledge Extraction
Linear Time Technique
Fetal Ecg Monitoring
Heart Rate Variability Analysis