Performance evaluation of the spectral autocorrelation function and autoregressive models for automated sleep apnea detection using single-lead ECG signal

2020 ◽  
Vol 195 ◽  
pp. 105626 ◽  
Author(s):  
Asghar Zarei ◽  
Babak Mohammadzadeh Asl
Keyword(s):  
Performance Evaluation ◽  
Sleep Apnea ◽  
Autocorrelation Function ◽  
Autoregressive Models ◽  
Ecg Signal ◽  
Apnea Detection

scholarly journals ECG Signal Analysis on an Embedded Device for Sleep Apnea Detection

2020 ◽  
pp. 377-384
Author(s):  
Rishab Khincha ◽  
Soundarya Krishnan ◽  
Rizwan Parveen ◽  
Neena Goveas
Keyword(s):  
Sleep Apnea ◽  
Signal Analysis ◽  
Ecg Signal ◽  
Embedded Device ◽  
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.

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