Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of p...
Sleep apnea-hypopnea event detection has been widely studied using various biosignals and algorithms. However, most minute-by-minute analysis techniques have difficulty detecting accurate event start/end positions. Furthermore, they require hand-engi...
PURPOSE: Breathing sounds during sleep are altered and characterized by various acoustic specificities in patients with sleep disordered breathing (SDB). This study aimed to identify acoustic biomarkers indicative of the severity of SDB by analyzing ...
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Jan 31, 2018
OBJECTIVE: Limitations of the manual scoring of polysomnograms, which include data from electroencephalogram (EEG), electro-oculogram (EOG), electrocardiogram (ECG) and electromyogram (EMG) channels have long been recognized. Manual staging is resour...
The concurrent usage of actigraphy and heart rate variability (HRV) for sleep efficiency quantification is still matter of investigation. This study compared chest (CACT) and wrist (WACT) actigraphy (actigraphs positioned on chest and wrist, respecti...
OBJECTIVES: The goal of this study is to evaluate the results of machine learning methods for the classification of OSA severity of patients with suspected sleep disorder breathing as normal, mild, moderate and severe based on non-polysomnographic va...
OBJECTIVES: In older adults, traditional metrics derived from polysomnography (PSG) are not well correlated with subjective sleep quality. Little is known about whether the association between PSG and subjective sleep quality changes with age, or whe...
BACKGROUND: Type 1 narcolepsy (NT1) is characterized by symptoms believed to represent Rapid Eye Movement (REM) sleep stage dissociations, occurrences where features of wake and REM sleep are intermingled, resulting in a mixed state. We hypothesized ...
IEEE journal of biomedical and health informatics
Jan 9, 2017
Effective sleep analysis is hampered by the lack of automated tools catering to disordered sleep patterns and cumbersome monitoring hardware. In this paper, we apply deep learning on a set of 57 EEG features extracted from a maximum of two EEG channe...
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