Cardiac health diagnosis using data fusion of cardiovascular and haemodynamic signals
The electrocardiogram (ECG) is a representative signal containing useful information about the condition of the heart. The shape and size of the PQRST wave, the rr interval, etc., may help to identify the nature of disease afflicting the heart. However, human observer cannot directly monitor these subtle details and it is difficult to evaluate the cardiac health using ECG alone. Hence, the fusion of ECG, blood pressure, saturated oxygen content and respiratory data for achieving improved clinical diagnosis of patients in cardiac care units. In this study, a computer based analysis and display of the heterogeneous signals for the detection of life threatening states is demonstrated using fuzzy logic based data fusion. And to evaluate the severity of the disease a new parameter, deterioration index is proposed and results are tabulated for various cases.
data fusion electrocardiogram fuzzy logic multiple sensor systems arterial blood pressure pulmonary oedema