Clinical analyses of beat-to-beat fluctuations in cardiovascular signals, often referred to as heart rate variability (HRV), have mainly been applied to establish, characterise and quantify autonomic neuropathy in patients with diabetes mellitus and other systemic, neurologic and heart diseases. It is important to identify patients with an autonomic imbalance, because of the risk for the development of malignant cardiac arrhythmia and sudden death. Autonomic dysfunction may be life threatening, but individual organs can function without autonomic innervation, e.g., the transplanted heart.
The first aim of this project is to continue the development of robust algorithms for analysis of cardiovascular signals. This includes the application of wavelet-based methods for analysis, correction for different factors that also must be accounted for during the interpretation of recorded data, and also to determine reference values for future studies. The second aim is to develop a diagnosis system based on pattern analysis, which may result in an improved diagnosis of autonomic dysregulation on an individual basis. The third aim is to develop a system for real-time analysis of cardiovascular signals. Here the focus will be on bed-side applications in intensive-care units, e.g., assessment of the risk for malignant cardiac arrhythmia. The developed methods for signal analysis will be applied in different clinical applications in close collaboration with different medical researchers.