Obstructive sleep apnoea syndrome as a cause of road traffic accidents

Aguiar, Margarida; Valença, João; Felizardo, M.; Caeiro, F.; Moreira, Susana; Staats, Richard; Almeida, A. A. Bugalho de

doi:10.1016/s2173-5115(09)70121-6

Cited by 3 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…18,19 Untreated or unrecognised OSA has been shown to contributeto cardiovascular diseases, 5 stroke, 21,22 diabetes mellitus 21 and depression 23 . Since OSA may cause excessive during the day, cognitive dysfunction, impaired work performance and increased risk of mobile accidents, 24 this may result in negative economic consequences, 22 reduced quality of life, as well asa higher risk of morbsidity and mortality. 22 Early detection and management of OSA patients aredependent on a wealth of knowledge and awareness of the future dental professionals on OSA.…”

Section: Introductionmentioning

confidence: 99%

Knowledge and Attitude of Obstructive Sleep Apnea among Dental Students in North East Penisular Malaysia

Adnan

Kassim

Rasid

et al. 2022

Bangladesh J Med Sci

View full text Add to dashboard Cite

Background: Obstructive sleep apnea (OSA) is sleep-disordered breathing that can cause morbidity and mortality.Dental students’ knowledge of sleep apnea will help them identify patients with the condition in the future. Objective: This study was conducted to determine the knowledge and attitude of OSA among undergraduate dental students in Universiti Sains Malaysia (USM). Methodology: A total of 97 undergraduate dental students participated in this study. This cross-sectional study used a self-administered and validated Obstructive Sleep Apnea Knowledge and Attitude (OSAKA) questionnaire. The questionnaire consists of 18 questions of knowledge and five questions of attitude towards OSA. Results: The mean total score of knowledge is 7.5 (SD 3.77). Meanwhile, the mean total score of attitude is 12.0 (SD 2.22). Most of the students (74.2%) knew that majority of patients with OSA snored. However, only 14.4% knew uvulopalatopharyngoplasty is the curative treatment for OSA. Regarding attitudes, 88.7% of the students agreed that OSA as an important clinical disease. Only 7.2% were confident in identifying OSA patients, while 15.5% were confident in managing OSA. Conclusion: The level of OSA knowledge and awareness among dental students in USM was relatively low. Early education and training on OSA in dental schools are crucial to improve this state of affairs. Bangladesh Journal of Medical Science Vol. 21 No. 02 April’22 Page : 384-390

show abstract

Section: Introductionmentioning

confidence: 99%

Knowledge and Attitude of Obstructive Sleep Apnea among Dental Students in North East Penisular Malaysia

Adnan

Kassim

Rasid

et al. 2022

Bangladesh J Med Sci

View full text Add to dashboard Cite

show abstract

“…Exhaustion and extreme drowsiness are some the most conventional which play an important role in one's efficiency reduction during the day and are followed by irreparable outcomes and even death in some cases. Other symptoms include: High blood pressure, cardiovascular disorders, brain stroke, depression feeling, increasing the risk of driving accidents, lower efficiency and poor performance, lack of motivation and stimulant factor, concentration disability, low tolerance poor endurance and inadequacy in problem-solving [4,[8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Developing an apnea-hypopnea diagnostic model using SVM

Kohzadi

Safdari

Sadeghniiat-Haghighi

2021

Front Health Inform

View full text Add to dashboard Cite

Introduction: Among sleep-related disorders, Sleep apnea has been under more attention and it’s the most common respiratory disorder in which respiration ceases frequently which can lead to serious health disorders and even mortality. Polysomnography is the standard method for diagnosing this disease at the moment which is costly and time-consuming. The aim of the present study was to analyze essential signals for the diagnosis of sleep apnea.Method: This analytical–descriptive was conducted on 50 patients (11 normal, 13 mild, 17 moderate and 9 severe patients) in the sleep clinic of Imam Khomeini hospital. Initially, data pre-processing was carried out in two steps(and Moving Average algorihtm). Next, using the SVD method, 12 features were extracted for airflow. Finally, to classify data, SVM with Quadratic, Polynomia and RBF kernels were trained and tested.Results: After applying different kernel functions on SVM, the RBF kernel showed the most efficient performance. After running the RBF kernel function ten times, the mean accuracy obtained for normal, apnea, and hypopnea modes were 92.74%, 91.70%, 93.26%.Conclusion: The results indicate that in online applications or applications in which volume and time calculations and the result are important simultaneously, patients could be diagnosed with acceptable accuracy using machine learning algorithms.

show abstract