Abstract
Correctly identifying sleep stages is important in diagnosing and treating sleep disorders. This paper proposes a joint classification-and-prediction framework based on convolutional neural networks (CNNs) for automatic sleep staging, and, subsequently, introduces a simple yet efficient CNN architecture to power the framework. Given a single input epoch, the novel framework jointly determines its label (classification) and its neighboring epochs’ labels (prediction) in the contextual output. While the proposed framework is orthogonal to the widely adopted classification schemes, which take one or multiple epochs as contextual inputs and produce a single classification decision on the target epoch, we demonstrate its advantages in several ways. First, it leverages the dependency among consecutive sleep epochs while surpassing the problems experienced with the common classification schemes. Second, even with a single model, the framework has the capacity to produce multiple decisions, which are essential in obtaining a good performance as in ensemble-of-models methods, with very little induced computational overhead. Probabilistic aggregation techniques are then proposed to leverage the availability of multiple decisions. To illustrate the efficacy of the proposed framework, we conducted experiments on two public datasets: Sleep-EDF Expanded (Sleep-EDF), which consists of 20 subjects, and Montreal Archive of Sleep Studies (MASS) dataset, which consists of 200 subjects. The proposed framework yields an overall classification accuracy of 82.3% and 83.6%, respectively. We also show that the proposed framework not only is superior to the baselines based on the common classification schemes but also outperforms existing deep-learning approaches. To our knowledge, this is the first work going beyond the standard single-output classification to consider multitask neural networks for automatic sleep staging. This framework provides avenues for further studies of different neural-network architectures for automatic sleep staging.
Highlights
Identifying the sleep stages from overnight Polysomnography (PSG) recordings plays an important role in diagnosing and treating sleep disorders, which affects millions of people [1], [2]
We present and employ a simple and efficient convolutional neural networks (CNNs) coupled with a multi-task softmax layer and the multi-task loss function to conduct joint classification and prediction. (iii) We further propose two probabilistic aggregation methods, namely additive and multiplicative voting, to leverage
Sleep-EDF dataset [42], [43] consists of two subsets: (1) Sleep Cassette (SC) subset consisting of 20 subjects aged 2534 aiming at studying the age effects on sleep in healthy subjects and (2) Sleep Telemetry (ST) subject consisting of 22 Caucasian subjects for study temazepam effects on sleep
Summary
Identifying the sleep stages from overnight Polysomnography (PSG) recordings plays an important role in diagnosing and treating sleep disorders, which affects millions of people [1], [2]. This task has been done manually by experts via visual inspection which is tedious, time-consuming, and is prone to subjective error. Automatic sleep stage classification [3], that performs as well as manual. De Vos are with the Institute of Biomedical Engineering, University of Oxford, Oxford OX3 7DQ, United Kingdom
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