Detection of Sleep Apnea Cancer Mutual Symptoms Using Deep Learning Techniques

摘要

Sleep Apnea is due to a respiratory condition, which is linked to a sleep issue that often contributes to reduction in airflow and often sometimes fully prevents airflow. In addition, this issue needs the individual to be checked overnight to determine the amount of oxygen in the blood. Several experiments carried out on animals like-mice the research reveals that sleep apnea can also contribute to cancer. The findings suggest that the development of cancer cells is advancing rapidly as mice is put in low- oxygen conditions. In this paper a deep learning algorithm for evaluating different variables including throat muscles, it usually collapses whilst a sleep, triggering both gasping and snoring as body searches for oxygen. This paper provides a description of the apnea deep learning paradigm related to dynamic cancer impermanence. Deep Learning (DL) technology, primarily the modified Fusion Convolution Neural Network (MFCNN), is used as a function detector to learn the features of the high-order association between observable data and associated marks. Preparing and segmenting the data model is a critical move toward training into deep learning. During the last stage of the MFCNN, a completely linked layer is attached to the output layer and builds the required number of outputs for sleep apnea occurrences during investigate the ECG data, data section whether apnea happens or not and challenges associated with complex impermanence from cancer.