Signal Processing and Modelling for the Complex CardiorespiratoryInteractions

Sleep studies are considered a paradigmatic example of the complex cardiorespiratory interactions, with an important impact also for clinical implications: diagnosis and treatment control, not only for the classical common sleep disturbances, but also for obstructive sleep apnea (OSA) or central apnea (CA) which are often associated with an increased risk of cardiovascular pathologies, such as hypertension, cardiac ischemia, stroke, etc. This paper presents an approach which aims at the integration of different signals (cardiovascular variability signals, EEG, respiration), different sources and modalities and different scales also. In particular, cardiovascular variability signals are reputed fundamental tools for a better understanding of autonomic behaviour in the control of cardiorespiratory parameters, as well as for a deeper investigation of central, autonomic and peripheral synchronizations. Possibility of objectively measuring sleep fragmentation and correlation with microarousals, as well as recognizing reliably OSA and CA episodes by using different algorithms of signal processing (batch, time-variant, time-frequency and wavelet analysis), may provide a unique insight of the complex pathophysiology involved, as well as a list of parameters of clinical and social relevance, because a measure of quality of sleep is clearly correlated with a measure of quality of life