Sleep/wake classification via remote PPG signals.
Journal:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Published Date:
Jul 1, 2019
Abstract
This paper proposes a remote sleep/wake classification method by combining vision-based heart rate (HR) estimation and convolutional neural network (CNN). Instead of inputting the estimated HR with low temporal resolution, remote PPG (Photoplethysmogram) signals, which contain high-temporal-resolution HR information, are input into the CNN. To reduce noise in the remote PPG signals, we propose a dynamic HR filter. Evaluation results show that the dynamic HR filter works more effectively in comparison with the static filter, which helps improve the area under the ROC curve (AUC) to 0.70, which is almost as good as the reference 0.71 for HR from a wearable sensor.