In this work, a detection and classification method for sleep apnea and hypopnea, using photopletysmography (PPG) and peripheral oxygen saturation (2) signals, is proposed. The detector consists of two parts: one that detects reductions in amplitude fluctuation of PPG (DAP)and one that detects oxygen desaturations. To further differentiate among sleep disordered breathing events (SDBE), the pulse rate variability (PRV) was extracted from the PPG signal, and then used to extract features that enhance the sympatho-vagal arousals during apneas and hypopneas. A classification was performed to discriminate between central and obstructive events, apneas and hypopneas. The algorithms were tested on 96 overnight signals recorded at the UZ Leuven hospital, annotated by clinical experts, and from patients without any kind of co-morbidity. An accuracy of 75.1% for the detection of apneas and hypopneas, in one-minute segments,was reached. The classification of the detected events showed 92.6% accuracy in separating central from obstructive apnea, 83.7% for central apnea and central hypopnea and 82.7% for obstructive apnea and obstructive hypopnea. The low implementation cost showed a potential for the proposed method of being used as screening device, in ambulatory scenarios.@en

Objective: the aim of this study is to evaluate whether noninvasive autonomic activity assessment could represent a potential tool for the stratification of asthmatic subjects based on symptoms control, using only 10-min electrocardiographic and respiratory signals. Methods: several heart rate variability (HRV) derived indexes, which are regarded as surrogates of autonomic activity, were evaluated in a group of asthmatic patients classified based on their symptomatology control. The effect of respiration on HRV was mitigated by means of orthogonal subspace projection. The most relevant features were used for training different classifiers. Results: similar classification performance was obtained when using HRV or clinical features, with just a 10% decrease in accuracy when using the HRV features (80% vs. 70%). This classification performance is equivalent to that achieved in new patients using the current asthma control tests. Conclusion: results suggest that the noninvasive assessment of autonomic activity could represent an added value for the monitoring of asthmatic subjects outside the clinic, using less cumbersome equipment, and therefore being suitable for an objective asthma self-monitoring. Significance: This study provides evidence on the usefulness of noninvasive autonomic activity assessment for asthma control stratification, supporting it as a potential complement to the current clinical practice.@en