Sleep apnoea diagnosis using respiratory effort-based signals - a comparative study.

A measure of the respiratory effort during a sleep study is an important contributor to the diagnosis of sleep apnoea. A common way of measuring respiratory effort is with bands with stretch sensors placed around the chest and/or abdomen. An alternative, and more convenient method from the patient's perspective, is via the ECG derived respiration (EDR) signal which provides an estimate of the respiratory effort at each heartbeat. In this study we performed a side-by-side comparison of the discrimination information for identifying epochs of sleep apnoea contained in the chest respiratory effort signal and three methods of calculating the EDR signal. Using simultaneously recorded chest band and ECG signals extracted from overnight polysomnogram (PSG) data from 8 subjects (4 controls, 4 apnoeas. MIT PhysioNet Apnea-ECG database), we extracted identical features from the two sensors and used the features to train a linear discriminant classifier to classify one-minute epochs as being apneic or normal. Ground truth labelling of each epoch was achieved with an expert using the full PSG as a reference. Our cross validation results revealed that the full respiratory effort signal resulted in an accuracy of 87% in correctly identifying the epoch label. When the respiratory signal was resampled at each heartbeat (as occurs with the EDR signal) the accuracy was 86%, suggesting that the sampling process inherent to the EDR signal does not materially affect its discrimination ability. The best EDR method was based on the calculating the QRS area for every heart and achieved an accuracy of 81%. Our results suggest that, while there is some information loss in the EDR estimation process, the EDR signal is a convenient and useful signal for sleep apnoea diagnosis.