Fig. 3: ROC curves showing the AD detection results for the physiological features without sleep staging.
From: Wearable sleep recording augmented by artificial intelligence for Alzheimer’s disease screening
These results show the AD detection performance when using as features the frequency bands for the different channels (both mean and STD) but aggregating them over the whole night without sleep staging. The transparent lines represent the ROC curves of all ten classifiers trained for each prediction task, while the non-transparent lines represent the ROC curves obtained using the mean predictions with ten repeats. Their AUC is reported in the legend. The curves show the performances using (a), the raw PSG data, and (b), the raw wearable data. AD Alzheimer’s disease, AUC area under the curve, OSA obstructive sleep apnea, PSG polysomnography, ROC receiver operating characteristic.
