scholarly journals Tracheal Sound Analysis Using a Deep Neural Network to Detect Sleep Apnea

2019 ◽  
Vol 15 (08) ◽  
pp. 1125-1133 ◽  
Author(s):  
Hiroshi Nakano ◽  
Tomokazu Furukawa ◽  
Takeshi Tanigawa
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Deep Neural Network ◽  
Sound Analysis ◽  
Tracheal Sound

Deep Learning for Detecting Sleep Apnea from ECG Signals

2020 ◽  
Vol 10 (6) ◽  
pp. 1265-1273
Author(s):  
Lili Chen ◽  
Huoyao Xu
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Deep Neural Network ◽  
Experimental Results ◽  
Support Vector ◽  
Ecg Signals ◽  
Novel Approach ◽  
Learning Machine ◽  
High Level ◽  
Electrocardiogram Ecg

Sleep apnea (SA) is a common sleep disorders affecting the sleep quality. Therefore the automatic SA detection has far-reaching implications for patients and physicians. In this paper, a novel approach is developed based on deep neural network (DNN) for automatic diagnosis SA. To this end, five features are extracted from electrocardiogram (ECG) signals through wavelet decomposition and sample entropy. The deep neural network is constructed by two-layer stacked sparse autoencoder (SSAE) network and one softmax layer. The softmax layer is added at the top of the SSAE network for diagnosing SA. Afterwards, the SSAE network can get more effective high-level features from raw features. The experimental results reveal that the performance of deep neural network can accomplish an accuracy of 96.66%, a sensitivity of 96.25%, and a specificity of 97%. In addition, the performance of deep neural network outperforms the comparison models including support vector machine (SVM), random forest (RF), and extreme learning machine (ELM). Finally, the experimental results reveal that the proposed method can be valid applied to automatic SA event detection.

Modeling Obstructive Sleep Apnea Voices Using Deep Neural Network Embeddings and Domain-Adversarial Training

2020 ◽  
Vol 14 (2) ◽  
pp. 240-250
Author(s):  
Juan M. Perero-Codosero ◽  
Fernando Espinoza-Cuadros ◽  
Javier Anton-Martin ◽  
Miguel A. Barbero-Alvarez ◽  
Luis A. Hernandez-Gomez
Keyword(s):  
Neural Network ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Deep Neural Network ◽  
Obstructive Sleep ◽  
Adversarial Training

Evolution-based configuration optimization of a Deep Neural Network for the classification of Obstructive Sleep Apnea episodes

2019 ◽  
Vol 98 ◽  
pp. 377-391 ◽  
Author(s):  
Ivanoe De Falco ◽  
Giuseppe De Pietro ◽  
Antonio Della Cioppa ◽  
Giovanna Sannino ◽  
Umberto Scafuri ◽  
...  
Keyword(s):  
Neural Network ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Deep Neural Network ◽  
Configuration Optimization ◽  
Obstructive Sleep

A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal

2018 ◽  
Vol 294 ◽  
pp. 94-101 ◽  
Author(s):  
Kunyang Li ◽  
Weifeng Pan ◽  
Yifan Li ◽  
Qing Jiang ◽  
Guanzheng Liu
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Deep Neural Network ◽  
Hidden Markov ◽  
Ecg Signal

scholarly journals Validation of a New System of Tracheal Sound Analysis for the Diagnosis of Sleep Apnea-Hypopnea Syndrome

SLEEP ◽  
2004 ◽  
Vol 27 (5) ◽  
pp. 951-957 ◽  
Author(s):  
Hiroshi Nakano ◽  
Makito Hayashi ◽  
Etsuko Ohshima ◽  
Nahoko Nishikata ◽  
Toshimitsu Shinohara
Keyword(s):  
Sleep Apnea ◽  
Sound Analysis ◽  
Tracheal Sound ◽  
Hypopnea Syndrome ◽  
New System
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