Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

Continuidad De Cuidados y Comunicación interniveles entre equipos de emergencias sanitarias y Atención Primaria.

The gradual incorporation of the nurses to the extrahospital emergency teams give them a holistic aspect in the field of care. And this is not possible without addressing the possibility of continuity of care and communication with other levels of care. All efforts in this regard and made speeches themselves as nursing, "Refer" (Nic 8100) and "Exchange of information on health care" (Nic 7960), is the conceptual framework of this work, which objetives are to quantify and exposing the proceedings in this line taken by the nurses of emergency team.