Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification

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.

Machine‐learning‐derived sleep–wake staging from around‐the‐ear electroencephalogram outperforms manual scoring and actigraphy

“…This post‐processing step was chosen instead of other multi‐epoch approaches (such as those discussed in Phan, Andreotti, Cooray, Chén, & Vos, ) because the performance of this solution was very similar, but allows for a relatively simple description.…”

Deep Transfer Learning for Single-Channel Automatic Sleep Staging with Channel Mismatch

“…On the other hand, as EOG signals contain rich information from multiple sources, including ocular activity, frontal EEG activity, and EMG from cranial and eye muscles, they are promising candidates for a single-modality sleep staging system. Despite their potential, EOG signals have been mainly used as a complement for EEG signals in multimodality sleep staging studies [1], [12]. Only a few studies have exploited standalone EOG signals for single-modality sleep staging [5], [21].…”

“…The overall performance is reported using accuracy, macro F1-score (MF1), Cohen's kappa (κ), sensitivity, and specificity. Note that, as Sleep-EDF was used differently in previous works [12], [16], for compatible comparison, we only included those with a similar experimental setting (i.e. conducting independent testing with single FPz-Cz channel and in-bed data only [12]).…”

“…Note that, as Sleep-EDF was used differently in previous works [12], [16], for compatible comparison, we only included those with a similar experimental setting (i.e. conducting independent testing with single FPz-Cz channel and in-bed data only [12]). A more comprehensive comparison can be found in [12].…”

“…conducting independent testing with single FPz-Cz channel and in-bed data only [12]). A more comprehensive comparison can be found in [12].…”