Detection of sleep disordered breathing by automated ECG analysis

Sleep related breathing disorders are a highly prevalent disease associated with increased risk of cardiovascular complications like chronic arterial hypertension, myocardial infarction or stroke. Gold standard diagnostics (polysomnography) are complex and expensive; the need for simplified diagnostics is therefore obvious. As the ECG can be easily conducted during the night, the detection of sleep related breathing disorders by ECG analysis provides an easy and cheap approach. Using a combination of well known biosignals processing algorithms, we trained the algorithm on 35 pre-scored overnight recordings. We then applied the algorithm on 35 control recordings, achieving a diagnostic accuracy of 77%. We believe that with further improvements in ECG analysis this algorithm can be used for screening diagnostics of obstructive sleep apnea.