Classifying sleep-wake stages through recurrent neural networks using pulse oximetry signals

TL;DR

This study demonstrates that recurrent neural networks can classify sleep-wake stages accurately using pulse oximetry signals, offering a less invasive alternative to traditional methods.

Contribution

The paper introduces a novel application of bidirectional GRU-based RNNs for sleep stage classification using only pulse oximetry data.

Findings

Achieved 90.13% accuracy in sleep stage classification.

Performance comparable to state-of-the-art methods using more complex signals.

Demonstrated effective sleep monitoring with non-invasive pulse oximetry.

Abstract

The regulation of the autonomic nervous system changes with the sleep stages causing variations in the physiological variables. We exploit these changes with the aim of classifying the sleep stages in awake or asleep using pulse oximeter signals. We applied a recurrent neural network to heart rate and peripheral oxygen saturation signals to classify the sleep stage every 30 seconds. The network architecture consists of two stacked layers of bidirectional gated recurrent units (GRUs) and a softmax layer to classify the output. In this paper, we used 5000 patients from the Sleep Heart Health Study dataset. 2500 patients were used to train the network, and two subsets of 1250 were used to validate and test the trained models. In the test stage, the best result obtained was 90.13% accuracy, 94.13% sensitivity, 80.26% specificity, 92.05% precision, and 84.68% negative predictive value. Further, the Cohen's Kappa coefficient was 0.74 and the average absolute error percentage to the actual sleep time was 8.9%. The performance of the proposed network is comparable with the state-of-the-art algorithms when they use much more informative signals (except those with EEG).