Gabriel Juliá-Serdá

TL;DR: A diagnostic system for sleep apnea based on oxygen saturation and RR intervals obtained from the EKG (electrocardiogram) is proposed with the goal to detect and quantify minute long segments of sleep with breathing pauses to provide a global score indicating the presence of clinically significant apnea integrating the segment based apnea detection.

TL;DR: In conclusion, symbolic dynamics, coupled with significant clinical and physical variables can help to prioritize polysomnographies in patients with a high probability of apnea and is a well established low cost and robust technique.

TL;DR: An algorithm for automatic structure selection and hyper parameterization of a one dimension CNN was developed to detect OSA events using only the SpO2 signal, and the best model achieved an average accuracy, sensitivity, and specificity of 94%, 92%, and 96%, respectively.

TL;DR: The system outperforms, using a relatively small set of features, previously existing studies in the context of sleep apnea, and concludes that working with dimensions around 7–8 and delays about 4–5, and using for the threshold distance the Fixed Amount of Nearest Neighbours (FAN) method with 5% of neighbours, yield the best results.

TL;DR: A forward feature selection technique is applied in order to know for one thing, what cepstrum parameters can extract better information about the influence of breath sleep disorder on the heart rhythm, and on the other hand, trying to detect apneas based on the RR series obtained from the electrocardiogram (EKG) as discussed by the authors.