OBJECTIVE: Automatic sleep stage scoring is of great importance for investigating sleep architecture during infancy. In this work, we introduce a novel multichannel approach based on deep learning networks and hidden Markov models (HMM) to improve th...
BACKGROUND: The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals.
In this work we examine some of the problems associated with the development of machine learning models with the objective to achieve robust generalization capabilities on common-task multiple-database scenarios. Referred to as the "database variabil...
IEEE journal of biomedical and health informatics
Mar 3, 2020
Sleep staging is to score the sleep state of a subject into different sleep stages such as Wake and Rapid Eye Movement (REM). It plays an indispensable role in the diagnosis and treatment of sleep disorders. As manual sleep staging through well-train...
BACKGROUND: Experimental investigation of sleep-wake dynamics in animals is an important part of pharmaceutical development. Typically, it involves recording of electroencephalogram, electromyogram, locomotor activity, and electrooculogram. Visual id...
Medical & biological engineering & computing
Mar 2, 2020
A series of short events, called A-phases, can be observed in the human electroencephalogram (EEG) during Non-Rapid Eye Movement (NREM) sleep. These events can be classified in three groups (A1, A2, and A3) according to their spectral contents, and a...
IEEE journal of biomedical and health informatics
Dec 19, 2019
The identification of sleep stages is essential in the diagnostics of sleep disorders, among which obstructive sleep apnea (OSA) is one of the most prevalent. However, manual scoring of sleep stages is time-consuming, subjective, and costly. To overc...
BACKGROUND: Polysomnography (PSG) is not recommended as a diagnostic tool in insomnia. However, this consensual approach might be tempered in the light of two ongoing transformations in sleep research: big data and artificial intelligence (AI).
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