DWPT based sleep apnea detection

2011 ◽  
Author(s):  
Syeda Qurat-ul-Ain Ali ◽  
Varun Jeoti
Keyword(s):  
Sleep Apnea ◽  
Apnea Detection

scholarly journals Using Under-trained Deep Ensembles to Learn Under Extreme Label Noise: A Case Study for Sleep Apnea Detection

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Konstantinos Nikolaidis ◽  
Thomas Plagemann ◽  
Stein Kristiansen ◽  
Vera Goebel ◽  
Mohan Kankanhalli
Keyword(s):  
Sleep Apnea ◽  
Label Noise ◽  
Apnea Detection

ApneaDetector

2021 ◽  
Vol 5 (2) ◽  
pp. 1-22
Author(s):  
Xianda Chen ◽  
Yifei Xiao ◽  
Yeming Tang ◽  
Julio Fernandez-Mendoza ◽  
Guohong Cao
Keyword(s):  
Machine Learning ◽  
Sleep Apnea ◽  
Wearable Sensors ◽  
Machine Learning Techniques ◽  
Clinical Test ◽  
Learning Techniques ◽  
Apnea Detection ◽  
Hospital Visits ◽  
Calibration Techniques ◽  
Data Calibration

Sleep apnea is a sleep disorder in which breathing is briefly and repeatedly interrupted. Polysomnography (PSG) is the standard clinical test for diagnosing sleep apnea. However, it is expensive and time-consuming which requires hospital visits, specialized wearable sensors, professional installations, and long waiting lists. To address this problem, we design a smartwatch-based system called ApneaDetector, which exploits the built-in sensors in smartwatches to detect sleep apnea. Through a clinical study, we identify features of sleep apnea captured by smartwatch, which can be leveraged by machine learning techniques for sleep apnea detection. However, there are many technical challenges such as how to extract various special patterns from the noisy and multi-axis sensing data. To address these challenges, we propose signal denoising and data calibration techniques to process the noisy data while preserving the peaks and troughs which reflect the possible apnea events. We identify the characteristics of sleep apnea such as signal spikes which can be captured by smartwatch, and propose methods to extract proper features to train machine learning models for apnea detection. Through extensive experimental evaluations, we demonstrate that our system can detect apnea events with high precision (0.9674), recall (0.9625), and F1-score (0.9649).

Acoustic obstructive sleep apnea detection

2009 ◽  
Author(s):  
A. Yadollahi ◽  
Z. Moussavi
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Obstructive Sleep ◽  
Apnea Detection

scholarly journals Automated Sleep Apnea Detection in Raw Respiratory Signals Using Long Short-Term Memory Neural Networks

2019 ◽  
Vol 23 (6) ◽  
pp. 2354-2364 ◽  
Author(s):  
Tom Van Steenkiste ◽  
Willemijn Groenendaal ◽  
Dirk Deschrijver ◽  
Tom Dhaene
Keyword(s):  
Neural Networks ◽  
Sleep Apnea ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Apnea Detection ◽  
Long Short Term Memory

scholarly journals Repeatability of Sleep Apnea Detection in 48-Hour Holter ECG Monitoring

2010 ◽  
Vol 15 (3) ◽  
pp. 218-222 ◽  
Author(s):  
Barbara Uznańska ◽  
Ewa Trzos ◽  
Tomasz Rechciński ◽  
Jarosław D. Kasprzak ◽  
Małgorzata Kurpesa
Keyword(s):  
Sleep Apnea ◽  
Ecg Monitoring ◽  
Holter Ecg ◽  
Apnea Detection

Portable obstructive sleep apnea detection and mobile monitoring

2017 ◽  
Author(s):  
Duygu Demirkol Çakmak ◽  
B. Murat Eyüboğlu
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Mobile Monitoring ◽  
Obstructive Sleep ◽  
Apnea Detection

A portable wireless device based on oximetry for sleep apnea detection

Computing ◽  
2018 ◽  
Vol 100 (11) ◽  
pp. 1203-1219 ◽  
Author(s):  
Fábio Mendonça ◽  
Sheikh Shanawaz Mostafa ◽  
Fernando Morgado-Dias ◽  
Juan L. Navarro-Mesa ◽  
Gabriel Juliá-Serdá ◽  
...  
Keyword(s):  
Sleep Apnea ◽  
Wireless Device ◽  
Apnea Detection

SE-MSCNN: A Lightweight Multi-scaled Fusion Network for Sleep Apnea Detection Using Single-Lead ECG Signals

2021 ◽  
Author(s):  
Xianhui Chen ◽  
Ying Chen ◽  
Wenjun Ma ◽  
Xiaomao Fan ◽  
Ye Li
Keyword(s):  
Sleep Apnea ◽  
Ecg Signals ◽  
Apnea Detection
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