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

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY

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Field Value
 
Title Performance Comparison Of Ann Classifiers For Sleep Apnea Detection Based On Ecg Signal Analysis Using Hilbert Transform
 
Creator Bali, Jyoti S
Nandi, Anilkumar V
Hiremath, P S
 
Subject QRS complex
Hilbert transform
Principal Component Analysis
ECG analysis
Sleep Apnea
Artificial Neural Networks
 
Description 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.
 
Publisher KHALSA PUBLICATIONS
 
Date 2018-09-27
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://cirworld.com/index.php/ijct/article/view/7616
10.24297/ijct.v17i2.7616
 
Source INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY; Vol 17 No 2 (2018): Advances in Computing; 7312-7325
2277-3061
 
Language eng
 
Relation http://cirworld.com/index.php/ijct/article/view/7616/7451
 
Rights Copyright (c) 2018 INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY
https://creativecommons.org/licenses/by/4.0
 

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