Papers Sleep apnea detection
“Sleep apnea detection” 태그가 달린 논문 23편 · 필터 해제
Deep Learning Approaches for Sleep Apnea Classification from Polysomnographic EEG Signals
Sleep apnea diagnosis via polysomnography remains resource intensive and relies on time consuming manual data analysis and scoring. Recent work has demonstrated that central nervous system effects of sleep apnea events c…
Sleep apnea detectionSleep Apnea Detection on a Wireless Multimodal Wearable Device Without Oxygen Flow Using a Mamba-based Deep Learning Approach
Objectives: We present and evaluate a Mamba-based deep-learning model for diagnosis and event-level characterization of sleep disordered breathing based on signals from the ANNE One, a non-intrusive dual-module wireless …
Sleep apnea detectionExploring the Efficacy of Convolutional Neural Networks in Sleep Apnea Detection from Single Channel EEG
Sleep apnea, a prevalent sleep disorder, involves repeated episodes of breathing interruptions during sleep, leading to various health complications, including cognitive impairments, high blood pressure, heart disease, s…
Sleep apnea detectionA Review on Multisensor Data Fusion for Wearable Health Monitoring
The growing demand for accurate, continuous, and non-invasive health monitoring has propelled multi-sensor data fusion to the forefront of healthcare technology. This review aims to provide an overview of the development…
Arrhythmia DetectionAtrial Fibrillation DetectionAutonomous DrivingSleep apnea detectionThermal Imaging and Radar for Remote Sleep Monitoring of Breathing and Apnea
Polysomnography (PSG), the current gold standard method for monitoring and detecting sleep disorders, is cumbersome and costly. At-home testing solutions, known as home sleep apnea testing (HSAT), exist. However, they ar…
DiagnosticSleep apnea detectionMultimodal Sleep Apnea Detection with Missing or Noisy Modalities
Polysomnography (PSG) is a type of sleep study that records multimodal physiological signals and is widely used for purposes such as sleep staging and respiratory event detection. Conventional machine learning methods as…
Event DetectionSleep apnea detectionSleep StagingECG-SL: Electrocardiogram(ECG) Segment Learning, a deep learning method for ECG signal
Electrocardiogram (ECG) is an essential signal in monitoring human heart activities. Researchers have achieved promising results in leveraging ECGs in clinical applications with deep learning models. However, the mainstr…
AttributeSelf-Supervised LearningSleep apnea detectionSlAction: Non-intrusive, Lightweight Obstructive Sleep Apnea Detection using Infrared Video
Obstructive sleep apnea (OSA) is a prevalent sleep disorder affecting approximately one billion people world-wide. The current gold standard for diagnosing OSA, Polysomnography (PSG), involves an overnight hospital stay …
Sleep apnea detectionECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning
In the medical field, current ECG signal analysis approaches rely on supervised deep neural networks trained for specific tasks that require substantial amounts of labeled data. However, our paper introduces ECGBERT, a s…
Arrhythmia DetectionHeartbeat ClassificationRepresentation LearningSleep apnea detection+1A novel deep learning-based approach for sleep apnea detection using single-lead ECG signals
Sleep apnea (SA) is a type of sleep disorder characterized by snoring and chronic sleeplessness, which can lead to serious conditions such as high blood pressure, heart failure, and cardiomyopathy (enlargement of the mus…
Feature EngineeringSleep apnea detectionSpecificitySleep Apnea Detection From Single-Lead ECG: A Comprehensive Analysis of Machine Learning and Deep Learning Algorithms
https://ieeexplore.ieee.org/abstract/document/9714370
Sleep apnea detectionAutomatic Home-based Screening of Obstructive Sleep Apnea Using Single Channel Electrocardiogram and SPO2 Signals
Obstructive sleep apnea (OSA) is one of the most widespread respiratory diseases today. Complete or relative breathing cessations due to upper airway subsidence during sleep is OSA. It has confirmed potential influence o…
Sleep apnea detectionSomnNET: An SpO2 Based Deep Learning Network for Sleep Apnea Detection in Smartwatches
The abnormal pause or rate reduction in breathing is known as the sleep-apnea hypopnea syndrome and affects the quality of sleep of an individual. A novel method for the detection of sleep apnea events (pause in breathin…
BinarizationSleep apnea detectionConCAD: Contrastive Learning-based Cross Attention for Sleep Apnea Detection
With recent advancements in deep learning methods, automatically learning deep features from the original data is becoming an effective and widespread approach. However, the hand-crafted expert knowledge-based features a…
Contrastive LearningSleep apnea detectionA 1D-CNN Based Deep Learning Technique for Sleep Apnea Detection in IoT Sensors
Internet of Things (IoT) enabled wearable sensors for health monitoring are widely used to reduce the cost of personal healthcare and improve quality of life. The sleep apnea-hypopnea syndrome, characterized by the abnor…
BinarizationSensitivitySleep apnea detectionFENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection
Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings. Polysomnography (PSG), the gold standard of detecting OSA, requires multiple specialize…
Sleep apnea detectionUsing Under-trained Deep Ensembles to Learn Under Extreme Label Noise
Improper or erroneous labelling can pose a hindrance to reliable generalization for supervised learning. This can have negative consequences, especially for critical fields such as healthcare. We propose an effective new…
Sleep apnea detectionMy Health Sensor, my Classifier: Adapting a Trained Classifier to Unlabeled End-User Data
In this work, we present an approach for unsupervised domain adaptation (DA) with the constraint, that the labeled source data are not directly available, and instead only access to a classifier trained on the source dat…
Domain AdaptationSleep apnea detectionUnsupervised Domain AdaptationLearning Realistic Patterns from Unrealistic Stimuli: Generalization and Data Anonymization
Good training data is a prerequisite to develop useful ML applications. However, in many domains existing data sets cannot be shared due to privacy regulations (e.g., from medical studies). This work investigates a simpl…
Sleep apnea detectionSensor Fusion using Backward Shortcut Connections for Sleep Apnea Detection in Multi-Modal Data
Sleep apnea is a common respiratory disorder characterized by breathing pauses during the night. Consequences of untreated sleep apnea can be severe. Still, many people remain undiagnosed due to shortages of hospital bed…
Sensor FusionSleep apnea detection