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

• Presented at: Biomedical Circuits and Systems Conference, BioCAS 2007, Montreal, 27.-30.11.2007
• Published in: Proceedings of the IEEE Biomedical Circuits and Systems Conference, BioCAS 2007, p. 203-206
• Piscataway, NJ: IEEE Press, 2007

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 and weight restrictions. The method uses a Fast Fourier Transform for feature extraction 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, but still comparable to actigraphy methods.

Keywords: biomedical signal analysis ; wearable computing ; sleep and ; wake classification ; electrocardiography ; respiratory ; effort ; neural classifier

Reference

• LIS-CONF-2007-004
• doi:10.1109/BIOCAS.2007.4463344
• View record in Web of Science

• URL: http://www.biocas2007.org/

Record created on 2007-07-02, modified on 2012-03-20