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.

Insulin receptor substrate-1 expression is increased in circulating leukocytes of patients with acute coronary syndrome.

The mechanisms underlying the increased risk of cardiovascular disease associated with diabetes mellitus (DM) are not fully defined. Insulin resistance in human metabolic syndrome patients is associated with decreased expression of the insulin receptor substrate-2- (Irs2-) AKT2 axis in mononuclear leukocytes (MLs). Moreover, acute coronary syndrome (ACS) has been linked through genome-wide association studies to the 2q36-q37.3 locus, which contains the Irs1 gene. Here, we investigated the expression of insulin-signaling pathway genes in MLs from patients with DM, ACS, and ACS plus DM. Quantitative real-time PCR expression studies showed no differences in the mRNA levels of Irs2, Akt2, and Akt1 among all patients. However, Irs1 mRNA expression was significantly increased in patients with ACS-diabetics and nondiabetics-compared with diabetic patients without ACS (P < .02 and P < .005, resp.). The present study reveals for the first time an association between increased Irs1 mRNA levels in MLs of patients with ACS which is not related to DM.