Prediagnosis of Obstructive Sleep Apnea via Multiclass MTS

Obstructive sleep apnea (OSA) has become an important public health concern. Polysomnography (PSG) is traditionally considered an established and effective diagnostic tool providing information on the severity of OSA and the degree of sleep fragmentation. However, the numerous steps in the PSG test to diagnose OSA are costly and time consuming. This study aimed to apply the multiclass Mahalanobis-Taguchi system (MMTS) based on anthropometric information and questionnaire data to predict OSA. Implementation results showed that MMTS had an accuracy of 84.38% on the OSA prediction and achieved better performance compared to other approaches such as logistic regression, neural networks, support vector machine, C4.5 decision tree, and rough set. Therefore, MMTS can assist doctors in prediagnosis of OSA before running the PSG test, thereby enabling the more effective use of medical resources

Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome

Sleep apnea-hypopnea syndrome (SAHS) is a chronic and highly prevalent disease considered a major health problem in industrialized countries. The gold standard diagnostic methodology is in-laboratory nocturnal polysomnography (PSG), which is complex, costly, and time consuming. In order to overcome these limitations, novel and simplified diagnostic alternatives are demanded. Sleep scientists carried out an exhaustive research during the last decades focused on the design of automated expert systems derived from artificial intelligence able to help sleep specialists in their daily practice. Among automated pattern recognition techniques, artificial neural networks (ANNs) have demonstrated to be efficient and accurate algorithms in order to implement computer-aided diagnosis systems aimed at assisting physicians in the management of SAHS. In this regard, several applications of ANNs have been developed, such as classification of patients suspected of suffering from SAHS, apnea-hypopnea index (AHI) prediction, detection and quantification of respiratory events, apneic events classification, automated sleep staging and arousal detection, alertness monitoring systems, and airflow pressure optimization in positive airway pressure (PAP) devices to fit patients’ needs. In the present research, current applications of ANNs in the framework of SAHS management are thoroughly reviewed

Cardiovascular Exercise Participation and Obstructive Sleep Apnea among Adults Over Normal Weight in the United States

Obstructive sleep apnea (OSA) is a type of sleep apnea that is common, complicated, and a major contributor to cardiovascular diseases, neurocognitive impairment, and mortality. This disease has additional negative impacts on patients\u27 lives by contributing to daytime sleepiness and low productivity at work as well as absenteeism and work-related injuries. Several studies have been conducted to assess the relationship between cardiovascular exercises and OSA; however, a definite conclusion is lacking. The purpose of this quantitative cross-sectional study was to assess the relationship between cardiovascular exercise participation and OSA by examining the relationship between total cardiovascular exercise participation per week and OSA as well as the relationship between body mass index (BMI) and OSA among adults over normal weight in the United States. Secondary data from the National Sleep Research Resource (NSRR) were used for analyses. Logistic regression was used to test the hypotheses. The Social-Ecological Model (SEM) guided the study. The findings of the study suggested that doing moderate cardiovascular exercise participation per week (0.1 and 200 minutes) had no relationship with OSA while doing higher cardiovascular exercise participation (\u3e200 minutes) per week had relationship with OSA by increasing the odds (AOR = 2.1, CI: 1.048-4.060) of having severe OSA. BMI had no relationship with OSA. Individuals with OSA and a higher BMI could use the findings of this study to participate in an exercise program that might benefit their health and decrease the risk of exacerbated symptoms which could lead to an improved quality of life and decreased burden associated with OSA