Contactless Camera-Based Detection of Oxygen Desaturation Events and ODI Estimation During Sleep in SAS Patients

Recurrent nocturnal breathing cessation leads to the reduction of the blood oxygen level and eventually to oxygen desaturation. Oxygen desaturation events are traditionally detected during a polysomnography in a sleep laboratory. In this work, a contactless camera-based oxygen desaturation detection and oxygen desaturation index (ODI) estimation method based on the analysis of multispectral videos is proposed. The method is based on the extraction and analysis of remote photoplethysmography (rPPG) signals at wavelengths of 780 nm and 940 nm from the forehead and a breath temperature signal via far-infrared (FIR) thermography from the subnasal region. A manual feature extraction is designed to extract relevant medical and physiological parameters out of the aforementioned signals in order to design a Feed-Forward Neural Network (FFNN)-based classifier, which classifies between periods with and without desaturation events. For the evaluation of the proposed method, a patient dataset involving 23 symptomatic sleep apnea patients is collected. The classification accuracy between desaturation events and periods without a desaturation based on the leave-one-patient-out cross-validation (LOPOCV) metric is 95.4 %. The ODI stage estimation resulted in a correct estimation in 22 out of 23 patients for a two-stage ODI classification and in a correct estimation in 21 out of 23 patients for a four-stage ODI classification.