A Systems Approach to Cardiac Health Diagnosis

With this paper we explore systems engineering as a systematic way of designing a cardiac health visualization system. From a biomedical perspective, the system is based on the well-known fact that Heart Rate (HR) signals indicate the activity of autonomous nervous system and therefore such signals are used to investigate the cardiac health of patients. HR signals are highly nonlinear and non-stationary in nature. Hence, we have extracted the salient features using nonlinear signal processing techniques. In this work, we have analysed 285 subjects from eight different cardiac classes. The features extracted are: Normalized bispectrum entropies (P1 and P2), Approximate Entropy (ApEn), Sample Entropy (SampEn) and Recurrence Entropy (REN). Furthermore, we propose a Cardiac Integrated Index (CII) using different entropies. This one value CII can be used to differentiate normal and abnormal cardiac classes. During the work on signal analysis we realized that proposing and testing of algorithms is done in the requirements phase of the systems design. This is an important realization, because it puts the work on algorithms in perspective to all the design steps necessary to build a physical solution for the important problem of cardiac monitoring.