ZleepNet: A Deep Convolutional Neural Network Model for Predicting Sleep Apnea Using SpO2 Signal
Sleep apnea is one of the most common sleep disorders in the world. It is a common problem for patients to suffer from sleep disturbances. In this paper, we propose a deep convolutional neural network (CNN) model based on the oxygen saturation (SpO2) signal from a smart sensor. This is the reason wh...
Main Authors: | Hnin Thiri Chaw, Thossaporn Kamolphiwong, Sinchai Kamolphiwong, Krongthong Tawaranurak, Rattachai Wongtanawijit |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2023-01-01
|
Series: | Applied Computational Intelligence and Soft Computing |
Online Access: | http://dx.doi.org/10.1155/2023/8888004 |
Similar Items
-
Sleep apnea detection using deep learning
by: Hnin Thiri Chaw, et al.
Published: (2019-01-01) -
Analysis of Features Dataset for DDoS Detection by using ASVM Method on Software Defined Networking
by: Myo Myint Oo, et al.
Published: (2020-04-01) -
Adaptive quality control for multimedia communications
by: Santichai Chuaywong, et al.
Published: (2008-01-01) -
Effect of positive airway pressure compliance on laryngopharyngeal reflux in obstructive sleep apnea patients
by: Krongthong Tawaranurak, et al.
Published: (2023-06-01) -
Prevalence, Risk Factors and Clinical Manifestation of Patients Suspected as having Obstructive Sleep Apnea in Songklanagarind Hospital Sleep Center
by: Krongthong Tawaranurak, et al.
Published: (2019-10-01)