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.

Are the health messages in schoolbooks based on scientific evidence? A descriptive study.

BACKGROUND Most textbooks contains messages relating to health. This profuse information requires analysis with regards to the quality of such information. The objective was to identify the scientific evidence on which the health messages in textbooks are based. METHODS The degree of evidence on which such messages are based was identified and the messages were subsequently classified into three categories: Messages with high, medium or low levels of evidence; Messages with an unknown level of evidence; and Messages with no known evidence. RESULTS 844 messages were studied. Of this total, 61% were classified as messages with an unknown level of evidence. Less than 15% fell into the category where the level of evidence was known and less than 6% were classified as possessing high levels of evidence. More than 70% of the messages relating to "Balanced Diets and Malnutrition", "Food Hygiene", "Tobacco", "Sexual behaviour and AIDS" and "Rest and ergonomics" are based on an unknown level of evidence. "Oral health" registered the highest percentage of messages based on a high level of evidence (37.5%), followed by "Pregnancy and newly born infants" (35%). Of the total, 24.6% are not based on any known evidence. Two of the messages appeared to contravene known evidence. CONCLUSION Many of the messages included in school textbooks are not based on scientific evidence. Standards must be established to facilitate the production of texts that include messages that are based on the best available evidence and which can improve children's health more effectively.