Sleep apnea syndrome detection and classification of severity level from ECG and SpO2 signals

Background: Sleep apnea-hypopnea syndrome (SAHS) is a sleep and respiratory disorder which is characterized by breaks in breathing or instances of superficial or uncommon breathing during sleep. SAHS diagnosis is commonly performed using Polysomnography (PSG). However, this technique is very complex and time-consuming procedure due to the need of many physiological variables and use of multiple sensors attached to the patients the whole night. Moreover, PSG is also inconvenient as an expert human observer is required to work overnight and relies on a doctor’s experience. In order to improve the diagnosis efficiency, reduce the complexity and diagnosis time and ensure a more accurate diagnosis, a quantitative and objective method is required. Methods: In this paper, an automatic sleep apnea syndrome and severity classification based on a simultaneously recorded electrocardiograph (ECG) and oxygen saturation (SpO2) signals has been proposed. Various features from the successive R waves (RR) intervals of ECG, and a statistical feature from the SpO2 were extracted as indicators of sleep apnea for training the support vector machine (SVM) model. Results: An accuracy of 99.1%, specificity of 98.1% and sensitivity of 100% has been achieved using the simultaneously recorded combination of two Bio-signal features and found to be better compared to other proposed techniques. Conclusions: Using the combined features is inherently more robust, as in the event of either channel being poor quality, the system can continue to make an analysis based on the other channel and achieve better accuracy compared to using either signal alone.