Analysis of Sleep Apnea Considering Biosignals from Peripheral Capillary Oxygen Saturation Level and Electrocardiogram Data

Sleep apnea is a disorder, where the obstructed upper airway affects breathing during sleep time. This ailment causes disruptions in sleep, leading \sleep deprivation. Age has a great impact on sleep deprivation due to apnea. Furtherance, it has been observed that as age progresses, severity of sleep apnea increases. This apnea minute’s increases, as age progresses, found in literature, is proved with the analysis performed in this paper. Apnea detection has been done using combined data of ECG and SpO2 for the users at different ages, and tested with various machine learning algorithms like K-nearest neighbor, Logistic regression, Random forests, and Ada-boost. From the results, it is observed that old age people are affected from sleep apnea more than 100% than youngsters. Also, the analysis has also proved that the sample time and combination of SpO2 and ECG influence the accuracy.