Recurrent Neural Network Based Classification of ECG Signal Features for Obstruction of Sleep Apnea Detection

2017 ◽  
Author(s):  
Maowei Cheng ◽  
Worku J. Sori ◽  
Feng Jiang ◽  
Adil Khan ◽  
Shaohui Liu
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Recurrent Neural Network ◽  
Ecg Signal ◽  
Signal Features ◽  
Apnea Detection

scholarly journals Performance Comparison Of Ann Classifiers For Sleep Apnea Detection Based On Ecg Signal Analysis Using Hilbert Transform

2018 ◽  
Vol 17 (2) ◽  
pp. 7312-7325 ◽  
Author(s):  
Jyoti S Bali ◽  
Anilkumar V Nandi ◽  
P S Hiremath
Keyword(s):  
Sleep Apnea ◽  
Hilbert Transform ◽  
Signal Analysis ◽  
Principal Component ◽  
Feature Reduction ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Apnea Detection ◽  
Lm Algorithm

In this paper, a methodology for sleep apnea detection based on ECG signal analysis using Hilbert transform is proposed. The proposed work comprises a sequential procedure of preprocessing, QRS complex detection using Hilbert Transform, feature extraction from the detected QRS complex and the feature reduction using principal component analysis (PCA). Finally, the classification of the ECG signal recordings has been done using two different artificial neural networks (ANN), one trained with Levenberg-Marquardt (LM) algorithm and the other trained with Scaled Conjugate Gradient (SCG) method guided by K means clustering. The result of classification of the input ECG record is as either belonging to Apnea or Normal category. The performance measures of classification using the two classification algorithms are compared. The experimental results indicate that the SCG algorithm guided by K means clustering (ANN-SCG) has outperformed the LM algorithm (ANN-LM) by attaining accuracy, sensitivity and specificity values as 99.2%, 96% and 97% respectively, besides the saving achieved in terms of reduced number of principal components. Profiling time and mean square error of the ANN classifier trained with SCG algorithm is significantly reduced by 58% and 83%, respectively, as compared to LM algorithm.

Classification of snoring sound based on a recurrent neural network

2019 ◽  
Vol 123 ◽  
pp. 237-245 ◽  
Author(s):  
Seung Ju Lim ◽  
Seong Jin Jang ◽  
Jee Young Lim ◽  
Jae Hoon Ko
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network
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