Deep Recurrent Neural Networks for Automatic Detection of Sleep Apnea from Single Channel Respiration Signals
Sleep apnea is a common sleep disorder that causes repeated breathing interruption during sleep. The performance of automated apnea detection methods based on respiratory signals depend on the signals considered and feature extraction methods. Moreover, feature engineering techniques are highly depe...
Main Authors: | Hisham ElMoaqet, Mohammad Eid, Martin Glos, Mutaz Ryalat, Thomas Penzel |
---|---|
Format: | Article |
Jezik: | English |
Izdano: |
MDPI AG
2020-09-01
|
Serija: | Sensors |
Teme: | |
Online dostop: | https://www.mdpi.com/1424-8220/20/18/5037 |
Podobne knjige/članki
-
A Deep Transfer Learning Framework for Sleep Stage Classification with Single-Channel EEG Signals
od: Hisham ElMoaqet, et al.
Izdano: (2022-11-01) -
Gaussian Mixture Models for Detecting Sleep Apnea Events Using Single Oronasal Airflow Record
od: Hisham ElMoaqet, et al.
Izdano: (2020-11-01) -
Obstructive sleep apnea-hypopnea syndrome: Etiology and diagnosis
od: Abdul Ghani Sankri-Tarbichi
Izdano: (2012-01-01) -
Introduction to Obstructive Sleep Apnea for the Internist
od: Rosemary Adamson, et al.
Izdano: (2018-10-01) -
Alternatives to Polysomnography for the Diagnosis of Pediatric Obstructive Sleep Apnea
od: Taylor B. Teplitzky, et al.
Izdano: (2023-06-01)