Detection of Congestive Heart Failure using Renyi entropy

Congestive Heart Failure (CHF) is a disease caused by the inability of the heart to supply the needs of the body in terms of oxygen and perfusion. Detection and diagnosis of CHF is difficult and requires a battery of tests, which include the electrocardiogram (ECG). Automated processing of the ECG signal and in particular heart rate variability (HRV) analysis holds great promise for diagnosis of CHF and more generally in assessing cardiac health, especially for personalized mobile health. However, recording the full 12-lead ECG is a relatively invasive procedure and for that reason it is of interest to determine what can be deduced from the much less intensive measurement of heart rate (RR interval) alone. In addition to calculating SDNN and, RMSSD, which when combined gave an accuracy of 78.8% with the Nearest Neighbour classifier. The best Renyi entropy result was an accuracy of 66.7% using Nearest Neighbour. Combining the best Renyi entropy results with SDNN and RMSSD led to an overall accuracy of 87.9% with sensitivity of 80% and specificity of 94.4%. In this work we have shown that applying Renyi entropy in addition to standard time domain measures identified CHF with higher accuracy than using time domain measures only. In addition, Renyi entropy exponents provide further information about the time signal characteristics that may be important in clinical decision making.