Variations in the accuracy of the ECG based detection of obstructive sleep apnoea (OSA) for different numbers of ECG leads and categories of OSA events.
This study investigates the finding that there is a more pronounced change to ECG physiological predictors during apnoea events compared to hypopnoea events and therefore accurate detection of hypopnoea events is likely to be more challenging than detection of apnoea events. The relevant statistical analysis was conducted by generating logistic regression models from the two data sets: the first one containing only the apnoea events and controls and the second data set containing only the hypopnoea events and controls. The discriminating ability of the model from the apnoea data set (AUC = 0.903, CI = 0.888 - 0.920) was significantly superior compared to the model from the hypopnoea data set (AUC = 0.842, CI = 0.817-0.866). The second study objective investigated whether regression models comprising the OSA predictors derived from the two ECG signals performed better than models that involved parameters of a single ECG. The optimised two signal ECG model (AUC = 0.878 and CI = 0.864 - 0.893) outperformed the best single ECG model (AUC = 0.843, CI = 0.826 - 0.860), suggesting that improved results can be achieved using an additional ECG lead.
Nilsen
Kris
K
Electronic and Electrical Engineering Department, The University of Melbourne, Australia. k.nilsen@ee.unimelb.edu.au
Zilberg
Eugene
E
Burton
David
D
Khandoker
Ahsan H
AH
Palaniswami
Marimuthu
M
D000465:Algorithms; D019540:Area Under Curve; D016022:Case-Control Studies; D004562:Electrocardiography / Q000295:instrumentation* / Q000379:methods*; D006339:Heart Rate; D006801:Humans; D015233:Models, Statistical; D012372:ROC Curve; D012044:Regression Analysis; D015203:Reproducibility of Results; D012107:Research Design; D012119:Respiration; D012815:Signal Processing, Computer-Assisted; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis* / Q000503:physiopathology*