Sleep apnea detection
6 papers with code • 2 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Sleep apnea detection
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Apnea-ECG
Latest papers with no code
Multimodal Sleep Apnea Detection with Missing or Noisy Modalities
Our experiments show that the proposed model outperforms other state-of-the-art approaches in sleep apnea detection using various subsets of available data and different levels of noise, and maintains its high performance (AUROC>0. 9) even in the presence of high levels of noise or missingness.
ECG-SL: Electrocardiogram(ECG) Segment Learning, a deep learning method for ECG signal
In this work, we propose a novel ECG-Segment based Learning (ECG-SL) framework to explicitly model the periodic nature of ECG signals.
SlAction: Non-intrusive, Lightweight Obstructive Sleep Apnea Detection using Infrared Video
Recognizing that sleep videos exhibit minimal motion, this work investigates the fundamental question: "Are respiratory events adequately reflected in human motions during sleep?"
ECGBERT: 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.
A novel deep learning-based approach for sleep apnea detection using single-lead ECG signals
In this study, a novel method of feature extraction based on the detection of S peaks is proposed to enhance the detection of adjacent SA segments using a single-lead ECG.
Automatic Home-based Screening of Obstructive Sleep Apnea Using Single Channel Electrocardiogram and SPO2 Signals
In this paper, several solutions for online OSA detection are introduced and tested on 155 subjects of three different databases.
ConCAD: 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.
FENet: 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.
Using Under-trained Deep Ensembles to Learn Under Extreme Label Noise
A new model is trained with these labels to generalize reliably despite the label noise.
My 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 data is provided.
