SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

Abstract

Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography epochs one at a time. In this paper, we tackle the task as a sequence-to-sequence classification problem that receives a sequence of multiple epochs as input and classifies all of their labels at once. For this purpose, we propose a hierarchical recurrent neural network named SeqSleepNet (source code is available at http://github.com/pquochuy/SeqSleepNet). At the epoch processing level, the network consists of a filterbank layer tailored to learn frequency-domain filters for preprocessing and an attention-based recurrent layer designed for short-term sequential modeling. At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modeling of sequential epochs. The classification is then carried out on the output vectors at every time step of the top recurrent layer to produce the sequence of output labels. Despite being hierarchical, we present a strategy to train the network in an end-to-end fashion. We show that the proposed network outperforms the state-of-the-art approaches, achieving an overall accuracy, macro F1-score, and Cohen's kappa of 87.1%, 83.3%, and 0.815 on a publicly available dataset with 200 subjects.

Fig. 1

Illustration of the classification schemes used for automatic sleep staging. (a) one-to-one, (b) many-to-one, (c) one-to-many, and (d) the proposed many-to-many.

Fig. 2

Illustration of SeqSleepNet, the proposed end-to-end hierarchical RNN for sequence-to-sequence sleep staging.

Fig. 3

Illustration of the developed baselines. In (b), conv. (n,w,s) denotes a convolutional layer with n 1-D filters of size w and stride s. max pool. (w,s) denotes a 1-D max pooling layer with kernel size w and stride s. fc (n) represents a fully connected layer with n hidden units. Finally, bi-LSTM (n,m) represents a bidirectional LSTM cell with size of its forward and backward hidden state vectors of n and m, respectively. Further details of these parameters can be found in [9].

Fig. 4

(a) The confusion matrix of SeqSleepNet-20 (C₁), (b) the confusion matrix of the E2E-ARNN baseline (C₂), and (c) the difference of two confusion matrices C₁ – C₂.

Fig. 5

Output hypnogram (a) produced by the proposed SeqSleepNet (L = 20) for subject 22 of the MASS dataset compared to the ground-truth (b). The errors are marked by the × symbol. The posterior probability distribution over different sleep stages is shown in (c).

Fig. 6

Attention weight learned by SeqSleepNet (L = 20) for specific epochs of different sleep stages. Note that we generated the spectrograms with finer temporal resolution (2-second window with 90% overlap) for visualization purpose.