Description

Apnea is a prevalent sleep disorder which has detrimental impacts on human health and quality of life. Accurate automatic algorithms for the detection of sleep apnea are needed for analyzing long-term sleep data and monitoring and management of its side effects and consequences. Hence detection of sleep apnea is compulsory so that it could be treated. This process established an algorithm that a short term event extraction from Electrocardiography (ECG) signal and combining neural network methodologies for automated detection of sleep apnea. So the process shows the importance of choosing the right classifier for a specific problem as well as choosing and using the best features for a better accuracy. These promising early-stage results may lead to complementary studies to improve the classifiers for a possible real-world application. The performance of the proposed model used for the detection of sleep apnea and it generates the result in the terms of metrics like, accuracy, precision, recall and f1-score..