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Sleep

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Obstructive Sleep Apnea Detection Scheme Based on Manually Generated Features and Parallel Heterogeneous Deep Learning Model Under IoMT.

IEEE journal of biomedical and health informatics
Obstructive sleep apnea (OSA) syndrome is a common sleep disorder and a key cause of cardiovascular and cerebrovascular diseases that seriously affect the lives and health of people. The development of Internet of Medical Things (IoMT) has enabled th...

Compact Wideband Double-Slot Microstrip Feed Engraved TEM Horn Strip Antennas on a Multilayer Substrate Board for in Bed Resting Body Positions Determination Based on Artificial Intelligence.

Sensors (Basel, Switzerland)
In this paper, a horn-shaped strip antenna exponentially tapered carved on a multilayer dielectric substrate for an indoor body position tracking system is proposed. The performance of the proposed antenna was verified by testing it as a tracking sta...

Sleep Staging Framework with Physiologically Harmonized Sub-Networks.

Methods (San Diego, Calif.)
Sleep screening is an important tool for both healthcare and neuroscientific research. Automatic sleep scoring is an alternative to the time-consuming gold-standard manual scoring procedure. Recently there have seen promising results on automatic sta...

Staging study of single-channel sleep EEG signals based on data augmentation.

Frontiers in public health
INTRODUCTION: Accurate sleep staging is an essential basis for sleep quality assessment and plays an important role in sleep quality research. However, the occupancy of different sleep stages is unbalanced throughout the sleep process, which makes th...

Pediatric Automatic Sleep Staging: A Comparative Study of State-of-the-Art Deep Learning Methods.

IEEE transactions on bio-medical engineering
BACKGROUND: Despite the tremendous prog- ress recently made towards automatic sleep staging in adults, it is currently unknown if the most advanced algorithms generalize to the pediatric population, which displays distinctive characteristics in overn...

Sleep prevents catastrophic forgetting in spiking neural networks by forming a joint synaptic weight representation.

PLoS computational biology
Artificial neural networks overwrite previously learned tasks when trained sequentially, a phenomenon known as catastrophic forgetting. In contrast, the brain learns continuously, and typically learns best when new training is interleaved with period...

Depth-Camera-Based Under-Blanket Sleep Posture Classification Using Anatomical Landmark-Guided Deep Learning Model.

International journal of environmental research and public health
Emerging sleep health technologies will have an impact on monitoring patients with sleep disorders. This study proposes a new deep learning model architecture that improves the under-blanket sleep posture classification accuracy by leveraging the ana...

Automated Analysis of Sleep Study Parameters Using Signal Processing and Artificial Intelligence.

International journal of environmental research and public health
An automated sleep stage categorization can readily face noise-contaminated EEG recordings, just as other signal processing applications. Therefore, the denoising of the contaminated signals is inevitable to ensure a reliable analysis of the EEG sign...

An Improved Neural Network Based on SENet for Sleep Stage Classification.

IEEE journal of biomedical and health informatics
Sleep staging is an important step in analyzing sleep quality. Traditional manual analysis by psychologists is time-consuming. In this paper, we propose an automatic sleep staging model with an improved attention module and hidden Markov model (HMM)....

Sleep classification using Consumer Sleep Technologies and AI: A review of the current landscape.

Sleep medicine
Classifying sleep stages in real-time represents considerable potential, for instance in enabling interactive noise masking in noisy environments when persons are in a state of light sleep or to support clinical staff in analyzing sleep patterns etc....