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

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.


Published in:
Proceedings of the IEEE Biomedical Circuits and Systems Conference, BioCAS 2007, 203-206
Presented at:
Biomedical Circuits and Systems Conference, BioCAS 2007, Montreal, 27.-30.11.2007
Year:
2007
Publisher:
Piscataway, NJ, IEEE Press
Keywords:
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 Record created 2007-07-02, last modified 2018-03-17

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