Sleep Apnea Monitoring for Smart Healthcare

Vocational safety problems have been causing millions of workers dead even with related safety policies launched. Sleep apnea is one of the main causes that renders insufficient sleep and thus becomes a high potential risk of working accidents. To assess sleep apnea, sleep stage monitoring and classification is the main and accurate way. Therefore, in this paper, a new classifier is designed for the sleep stage classification among awake, light sleep and deep sleep. A new kernel is designed for the sleep apnea classification. Results show that the proposed method can achieve an accuracy up to 97% and _~18% higher than the previous related works. Such a high accuracy ensures the efficient diagnosis of sleep apnea.