Association between symptoms of sleep-disordered breathing and speech in children with craniofacial malformations

Association between habitual snoring, middle ear disease, and speech problems in young children with non-syndromic cleft palate anomalies

The purpose of this study was to investigate the association between habitual snoring (HS), middle ear disease (MED), and speech problems in children with cleft palate. This cross-sectional study included children aged 2.0-7.9 years with non-syndromic cleft palate anomalies. Parents completed the Pediatric Sleep Questionnaire and a questionnaire about MED. Audiograms and speech assessment were also conducted. Ninety-five children were enrolled; 15.2% of families reported HS, 97.6% MED, and 17.1% speech problems. HS (37.5% vs 10.3%, P = 0.007) and early episodes of MED (92.3% vs 58.2%, P = 0.021) were more likely to be reported for children with isolated cleft palate when compared to those with cleft lip and palate. Children with cleft lip and palate had a higher frequency of MED with effusion compared to those with Robin sequence (86.4% vs 57.1%, P = 0.049). The odds ratio for HS in children with ≥1 episode of MED in the last year was 7.37 (95% confidence interval 1.55-35.15, P = 0.012). There was a trend for children with speech problems reported by parents to have HS (30.8% vs 11.5%, P= 0.076). Anatomical factors play a role in the frequency of upper airway symptoms in children with cleft palate. A recent history of at least one episode of MED was associated with an increased frequency of HS.

Review on Biomedical Sensors, Technologies and Algorithms for Diagnosis of Sleep Disordered Breathing: Comprehensive Survey

This paper provides a comprehensive review of available technologies for measurements of vital physiology related parameters that cause sleep disordered breathing (SDB). SDB is a chronic disease that may lead to several health problems and increase the risk of high blood pressure and even heart attack. Therefore, the diagnosis of SDB at an early stage is very important. The essential primary step before diagnosis is measurement. Vital health parameters related to SBD might be measured through invasive or non-invasive methods. Nowadays, with respect to increase in aging population, improvement in home health management systems is needed more than even a decade ago. Moreover, traditional health parameter measurement techniques such as polysomnography are not comfortable and introduce additional costs to the consumers. Therefore, in modern advanced self-health management devices, electronics and communication science are combined to provide appliances that can be used for SDB diagnosis, by monitoring a patient's physiological parameters with more comfort and accuracy. Additionally, development in machine learning algorithms provides accurate methods of analysing measured signals. This paper provides a comprehensive review of measurement approaches, data transmission, and communication networks, alongside machine learning algorithms for sleep stage classification, to diagnose SDB.