Simultaneous Sleep Stage and Sleep Disorder Detection from Multimodal Sensors Using Deep Learning

Abstract

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.

Non-Invasive Blood Pressure Sensing via Machine Learning

“…The maximum depth of a tree is used to control over-fitting as higher depth will make the model more complex and more likely to overfit; the value 0 is only accepted in a loss-guided growing policy while large values bring an aggressive consumption of memory. Any positive value is admissible, with typical values in [3,10]; in this work, trial and error was used to modify the upper bound of the range to obtain better results. The subsample is, instead, the fraction of observations to be randomly sampled for each tree and is useful to prevent overfitting; in fact, lower values make the algorithm more conservative while too small values might lead to under-fitting.…”

“…As a matter of fact, the monitoring of the health of individuals is also made possible by the spread of artificial intelligence in healthcare [8][9][10].…”