Why the study?
Sleep disorder symptoms may correlate with specific sleep stages, making simultaneous identification important for diagnosis, but automated recognition using multimodal sensory data requires effective methods.
Does a distributed multimodal and multilabel decision-making system improve the automatic recognition of sleep stages and disorders from multimodal sensory data compared to single-modality approaches?
Does a distributed multimodal and multilabel decision-making system improve the automatic recognition of sleep stages and disorders from multimodal sensory data compared to single-modality approaches?
A novel distributed multimodal and multilabel deep learning system significantly improves the automatic detection of sleep stages and disorders from combined EEG, ECG, and EMG data.
Answers are grounded in this paper and cite their sources.
May support multimodal ML refinement for sleep analysis; hypothesis-generating and requires prospective validation before clinical use.
Sleep scoring involves the inspection of multimodal recordings of sleep data to detect potential sleep disorders. Given that symptoms of sleep disorders may be correlated with specific sleep stages, the diagnosis is typically supported by the simultaneous identification of a sleep stage and a sleep disorder. This paper investigates the automatic recognition of sleep stages and disorders from multimodal sensory data (EEG, ECG, and EMG). We propose a new distributed multimodal and multilabel decision-making system (MML-DMS). It comprises several interconnected classifier modules, including deep convolutional neural networks (CNNs) and shallow perceptron neural networks (NNs). Each module works with a different data modality and data label. The flow of information between the MML-DMS modules provides the final identification of the sleep stage and sleep disorder. We show that the fused multilabel and multimodal method improves the diagnostic performance compared to single-label and single-modality approaches. We tested the proposed MML-DMS on the PhysioNet CAP Sleep Database, with VGG16 CNN structures, achieving an average classification accuracy of 94.34% and F1 score of 0.92 for sleep stage detection (six stages) and an average classification accuracy of 99.09% and F1 score of 0.99 for sleep disorder detection (eight disorders). A comparison with related studies indicates that the proposed approach significantly improves upon the existing state-of-the-art approaches.
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Cheng et al. (2023) studied this question.
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