Adaptive Sleep/Wake Classification Based on Cardiorespiratory Signals for Wearable Devices

In this paper we describe a method to classify online sleep/wake states of humans based on cardiorespiratory signals for wearable applications. The method is designed to be embedded in a portable microcontroller device and to cope with the resulting tight power restrictions. The method uses a Fast Fourier Transform as the main feature extraction method and an adaptive feed-forward Artificial Neural Network as a classifier. Results show that when the network is trained on a single user, it can correctly classify on average 95.4% of unseen data from the same user. The accuracy of the method in multi-user conditions is lower (89.4%). This is still comparable to actigraphy methods, but our method classifies wake periods considerably better.

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