A person who suffers from obstructive sleep apnea – halts in breathing that lasts for 10 seconds or more during the night and lowers oxygen levels in the blood – is usually the last one to know.
Although the lack of oxygen alerts the brain and causes breathing to resume, the OSA victim usually is unaware of it and she – or more commonly he – wakes up in the morning exhausted, and even endangering themselves on the road or at work. OSA also increases the risk of developing diabetes and cardiovascular disease.
But the spouse sleeping nearby is aware of it because the apnea victim snores loudly and gasps for breath.
The prevalence of this common syndrome increases with age and affects more than one in five individuals in the general adult population – particularly males and those who are overweight.
An international research team led by the Technion–Israel Institute of Technology in Haifa has developed an innovative, artificial-intelligence-based technology for monitoring OSA.
The study was led by Dr. Joachim Behar, a member of the Technion’s Faculty of Biomedical Engineering. “Sleep apnea can be treated effectively, but many sufferers remain undiagnosed,” said Behar. The growing awareness for the high prevalence of OSA, coupled with the dramatic proportion of undiagnosed individuals motivates the elaboration of a simple but accurate screening test. This study assesses, for the first time, the performance of oximetry combined with demographic information as a screening tool for identifying OSA in a representative (non-referred) population sample.
His and his research team’s just published their findings in The Lancet Group’s open-access journal EClinicalMedicine. The article was entitled “Feasibility of Single-Channel Oximetry for Mass Screening of Obstructive Sleep Apnea.”
“We provide strong evidence on a representative population sample that oximetry biomarkers combined with few demographic information, the OxyDOSA model, is an effective screening tool for OSA,” they wrote in the journal article. “Our results suggest that sleep questionnaires should be used with caution for OSA screening as they fail to identify many moderate and even some severe cases. The OxyDOSA model will need to be further validated on data recorded using overnight portable oximetry.”
The technology used to diagnose the syndrome in sleep labs, called polysomnography, records brain waves and the oxygen level in the blood, as well as the heart rate, breathing and eye and leg movements during sleep.
Although polysomnography is effective in diagnosing the condition, it is not widely available because of its prohibitive costs. OSA diagnosis may also be carried out with home monitoring equipment, though this option is not without cost, nor is it easily accessible to the general population at risk. Less expensive diagnostic methods, based on questionnaires and upper-respiratory morphology, are not accurate enough.
The technology that Behar and his team developed is based on data from 887 subjects from the general adult population in Sao Paulo, Brazil. The technology received this data and, using artificial intelligence, succeeded in differentiating between OSA sufferers, and those who do not have it.
The diagnosis was made on the basis of integrating biomarkers obtained from the patients that include oxygen saturation (pulse oximetry) during sleep, demographic information (such as age, height, and weight) and anthropometric information such as neck dimension. The system was able to successfully identify all-important clinical cases of medium or severe OSA. Standardized sleep apnea diagnosis questionnaires, by comparison, missed more than 15% of severe cases. The use of pulse oximetry only detected all severe cases but failed to identify some of the medium OSA cases.
“This means that the model we developed is a reliable and effective tool for identifying sleep apnea in large populations,” Behar said. In the future, with the development of a suitable mobile application, the model will make it possible for anyone with a smartwatch or bracelet that includes an oximeter to perform an accurate self-examination for OSA.”
The model the team developed is called OxyDOSA. Behar heads the Artificial Intelligence in Medicine Laboratory (AIMLab) in the Technion’s Faculty of Biomedical Engineering. AIMLab research is focused on the use of artificial intelligence in medicine within the context of physiological time series analysis recorded from portable monitors and wearable devices.
Behar earned his doctorate in biosignals processing and machine learning from the University of Oxford, under the supervision of Prof. Gari D. Clifford and Dr. Julien Oster. He is a two-time winner of the MIT-Physionet-Computing in Cardiology Competition in Biosignal Processing.