Title: Sleep Stage Classification Based on Multi-level Feature Learning and Recurrent Neural Networks via Wearable Device,
Abstract: This paper proposes a practical approach for automatic sleep stage
classification based on a multi-level feature learning framework and Recurrent
Neural Network (RNN) classifier using heart rate and wrist actigraphy derived
from a wearable device. The feature learning framework is designed to extract
low- and mid-level features. Low-level features capture temporal and frequency
domain properties and mid-level features learn compositions and structural
information of signals. Since sleep staging is a sequential problem with
long-term dependencies, we take advantage of RNNs with Bidirectional Long
Short-Term Memory (BLSTM) architectures for sequence data learning. To simulate
the actual situation of daily sleep, experiments are conducted with a resting
group in which sleep is recorded in resting state, and a comprehensive group in
which both resting sleep and non-resting sleep are included.We evaluate the
algorithm based on an eight-fold cross validation to classify five sleep stages
(W, N1, N2, N3, and REM). The proposed algorithm achieves weighted precision,
recall and F1 score of 58.0%, 60.3%, and 58.2% in the resting group and 58.5%,
61.1%, and 58.5% in the comprehensive group, respectively. Various comparison
experiments demonstrate the effectiveness of feature learning and BLSTM. We
further explore the influence of depth and width of RNNs on performance. Our
method is specially proposed for wearable devices and is expected to be
applicable for long-term sleep monitoring at home. Without using too much prior
domain knowledge, our method has the potential to generalize sleep disorder
detection.