AIMC Topic: Polysomnography
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Beyond accuracy: a framework for evaluating algorithmic bias and performance, applied to automated sleep scoring.
Scientific reports
Jul 1, 2025
Recent advancements in artificial intelligence (AI) have significantly improved sleep-scoring algorithms, bringing their performance close to the theoretical limit of approximately 80%, which aligns with inter-scorer agreement levels. While this sugg...
Automated sleep staging model for older adults based on CWT and deep learning.
Scientific reports
Jul 1, 2025
Sleep staging plays a crucial role in the diagnosis and treatment of sleep disorders. Traditional sleep staging requires manual classification by professional technicians based on the characteristic features of each sleep stage. This process is time-...
Adaptive weighted dual MAML: Proposing a novel method for the automated diagnosis of partial sleep deprivation.
PloS one
Jun 13, 2025
INTRODUCTION: Sleep disorders significantly disrupt normal sleep patterns and pose serious health risks. Traditional diagnostic methods, such as questionnaires and polysomnography, often require extensive time and are susceptible to errors. This high...
A skin-interfaced wireless wearable device and data analytics approach for sleep-stage and disorder detection.
Proceedings of the National Academy of Sciences of the United States of America
Jun 6, 2025
Accurate identification of sleep stages and disorders is crucial for maintaining health, preventing chronic conditions, and improving diagnosis and treatment. Direct respiratory measurements, as key biomarkers, are missing in traditional wrist- or fi...
Apnea detection using wrist actigraphy in patients with heterogeneous sleep disorders.
Scientific reports
May 22, 2025
Obstructive sleep apnea (OSA) and related hypoxia are well-established cardiovascular and neurocognitive risk factors. Current multi-sensor diagnostic approaches are intrusive and prone to misdiagnosis when simplified. This study introduces an enhanc...
Time-series visual representations for sleep stages classification.
PloS one
May 21, 2025
Polysomnography is the standard method for sleep stage classification; however, it is costly and requires controlled environments, which can disrupt natural sleep patterns. Smartwatches offer a practical, non-invasive, and cost-effective alternative ...
Robust performances of a nocturnal long-term ECG algorithm for the evaluation of sleep apnea syndrome: A pilot study.
PloS one
May 16, 2025
Obstructive sleep apnea-hypopnea syndrome (OSAHS) is one of the most common sleep disorders affecting nearly one billion of the global adult population, making it a major public health issue. Even if in-lab polysomnography (PSG) remains the gold stan...
MHFNet: A Multimodal Hybrid-Embedding Fusion Network for Automatic Sleep Staging.
IEEE journal of biomedical and health informatics
May 6, 2025
Scoring sleep stages is essential for evaluating the status of sleep continuity and comprehending its structure. Despite previous attempts, automating sleep scoring remains challenging. First, most existing works did not fuse local and global tempora...
FlexibleSleepNet:A Model for Automatic Sleep Stage Classification Based on Multi-Channel Polysomnography.
IEEE journal of biomedical and health informatics
May 6, 2025
In the task of automatic sleep stage classification, deep learning models often face the challenge of balancing temporal-spatial feature extraction with computational complexity. To address this issue, this study introduces FlexibleSleepNet, a lightw...
Data-driven sleep structure deciphering based on cardiorespiratory signals.
Computer methods and programs in biomedicine
Apr 23, 2025
BACKGROUND AND OBJECTIVE: Cardiorespiratory signals provide a novel perspective for understanding sleep structure through the physiological mechanism of cardiopulmonary coupling. This mechanism divides the coupling spectrum into high-frequency (HF) a...
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