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

  • Jyoti S Bali KLE Technological University
  • Anilkumar V Nandi
  • P S Hiremath
Keywords: QRS complex, Hilbert transform, Principal Component Analysis, ECG analysis, Sleep Apnea, Artificial Neural Networks


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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L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdor, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, “ PhysioBank, PhysioToolkit, and PhysioNet : Components of a new research resource for complex physiologic signals”, Circulation, 2000.

Robert Joseph Thomas, Chol Shin, Matt Travis Bianchi, Clete Kushida and Chang- Ho Yun, “Distinct polysomnographic and ECG spectograpic phenotypes embedded within Obstructive Sleep Apnea, Sleep Science and practice” , 1:11 , 2017, DOI 10.1186/s41606-017-0012-9.

American Academy of Sleep Medicine,”Sleep Related Breathing Disorders in Adults: Recommendations for Syndrome Definition and Measurement Techniques in Clinical Research”, Sleep, Vol. 22, 1999, pp. 667-689.

T. Young, P. E. Peppard and D. G. Gottlier, “Epidemiology of Obstructive Sleep Apnea, a Population Health Perspective”, American Journal of Respiratory and Critical Care Medicine, Vol. 165, pp. 1217-1239, 2012. ,2002

Rangayyan, Rangaraj M. “Biomedical signal analysis : a case-study approach” . c2002,IEEE Press ,New York, N.Y. : Wiley-Interscience.

James D. Broesch, “Digital Signal Processing Demystified”, Newnes, 1997 – Technology & Engineering - 203 pages

Valtino Afonso, “Biomedical Digital Signal Processing” 1993, Pages 236-264

Prentice-Hall, Inc. , ISBN:0-13-067216-5

Piotr Figo?, Pawe? Irzma?ski, Adam Jó?ko,”ECG Signal Quality Improvement Techniques, PRZEGL?D ELEKTROTECHNICZNY”, 2013, ISSN 0033-2097, R. 89 NR 4/2013

King, F.W., “Hilbert Transforms” Volume 1, Cambridge University Press,Cambidge, 2009

Amin Farahabadi, Eiman Farahabadi, Hossein Rabbani, Mohammad Parsa Mahjoub , “Detection of QRS Complex in Electrocardiogram Signal Based on a Combination of Hilbert Transform, Wavelet Transform and Adaptive Thresholding” , DOI: 10.1109/BHI.2012.6211537, 2012.

Simranjit Singh Kohli, Nikunj Makwana, Nishant Mishra, Balwalli Sagar, “ Hilbert Transform Based Adaptive ECG R-Peak Detection Technique”, International Journal of Electrical and Computer Engineering (IJECE),Vol. 2, No. 5, pp. 639~643, ISSN: 2088-8708 , 2012.

Elgandi M, Eskofier B,Doko S,Abbott D, “Revisiting QRS Detection Methodologies for Portable, wearable Battery operated and Wireless ECG Systems”, PLoS ONE 9(1): Volume 9, Issue: 1, e84018 , 2014.

R. Rodrígueza, A. Mexicanob, J. Bilac, S. Cervantesd, R. Ponceb. “Feature extraction of Electrocardiogram signals by applying adaptive threshold and Principal component analysis”, Journal of Applied Research and Technology 13 , 261-269 , 2015.

Yeldos A. Altay, Artem S. Kremlev. 2018, “Comparative Analysis of ECG Signal Processing Methods in the Time-Frequency Domain”, 978-1-5386-4340-2/18/$31.00 ©2018 IEEE

Sridhar Krishnan, Yashodhan Athavale, “Trends in Biomedical Signal Feature Extraction”, Biomedical Signal Processing and Control, 2018. 1746-8094/© 2018 Elsevier Ltd.

Francisco-Manuel Melgarejo-Meseguer , Estrella Everss-Villalba , Francisco-Javier Gimeno- lanes, Manuel Blanco-Velasco , Zaida Molins-Bordallo, José-Antonio Flores-Yepes , José-Luis Rojo Álvarez and Arcadi García-Alberola, “On the Beat Detection Performance in Long-Term ECG Monitoring Scenarios ” , Sensors, 18, 1387,

DOI: 10.3390/s18051387,, 2018.

Chia-Ping Shen , Wen-Chung Kao, Yueh-Yiing Yang , Ming-Chai Hsu , Yuan-Ting Wu, Feipei Lai, “Detection of cardiac arrhythmia in electrocardiograms using Adaptive feature extraction and modified support vector machines”, Expert Systems with Applications , 39 . 7845–7852, 2012.

Laiali Almazaydeh, Khaled Elleithy, Miad Faezipour, “Detection of Obstructive Sleep Apnea Through ECG Signal Features”, 2012, 978-1-4673-0818-2/12/$31.00 ©2012 Crown

U.Kohler , C. Hennig and R. Orglmeister. “The principles of software QRS detection”, in IEEE Engineering in Medicine and Biology Magazi,ne, vol. 21, no. 1, pp. 42-57, 2012. doi: 10.1109/51.993193,

Santanu Sahoo, Prativa Biswal, Tejaswini Das, Sukanta Sabut. “De-noising of ECG Signal and QRS Detection Using Hilbert Transform and Adaptive Thresholding” , Procedia Technology, Volume 25, Pages 68-75, 2016.

FAUST, Oliver, ACHARYA, U. Rajendra, NG, E. Y. K. and FUJITA, Hamido , “A review of ECG-based diagnosis support systems for obstructive sleep apnea”. Journal of Mechanics in Medicine and Biology, 16 (01), p. 1640004, 2016.

Penzel and A. Bianchi, “Detection of Sleep Apnea from Surface ECG Based on Features Extracted by an Autoregressive Model”, Proceedings of the IEEE International Conference on Engineering in Medicine and Biology Society (EMBS 2007), pp. 6105-6108, 2007.

A.F. Quiceno-Manrique, J.B. Alonso-Hernandez, C.M. Travieso-Gonzalez, M.A. Ferrer-Ballester, G. Castellanos-Dom´?nguez, “Detection of obstructive sleep apnea in ECG recordings using time-frequency distributions and dynamic features”, 31st Annual International Conference of the IEEE EMBS Minneapolis, Minnesota, USA, September 2-6, 978-1-4244-3296-7/09/$25.00 ©2009 IEEE, 2009.

L. Fu., “Neural Networks in Computer Intelligence” (Tata McGraw-Hill, 2003).

S. Haykin, “Neural Networks, A Comprehensive Foundation”,(2nd ed., Pearson Prentice Hall, 2005.

R. Roja,”The Back propagation Algorithm”, Chapter 7: Neural Networks (Springer-Verlag, Berlin, 1996) pp. 151-184.

M. K. S. Alsmadi, K. B. Omar, S. A. Noah, “Back propagation algorithm: The best algorithm among the multi-layer Perceptron Algorithm”, International Journal of Computer Science and Network Security, vol., 9(4), , pp. 378 – 383, 2009.

S. Ali and K. A. Smith, “On learning algorithm selection for classification”, Applied Soft Computing, (6), pp.119–138, 2006.

M. F. Moller, “A scaled conjugate gradient algorithm for fast supervised learning”, Neural Networks, 6, pp. 525–533, 1993.

T. Penzel, G. B. Moody, R. G. Mark, A. L. Goldberger, and J. H. Peter, 2000, “The apnea-ECG database”. Computers in Cardiology, pp. 255-258, 2000.

A. H. Khandoker, M. Palaniswami, C. K. Karmakar, “Support vector machines for automated recognition of obstructive sleep apnea syndrome from ECG recordings”, IEEE Transactions on Information Technology in Biomedicine 13 (1), 37–48, 2009.

D. Liu, X. Yang, G. Wang, J. Ma, Y. Liu, C. K. Peng, J. Zhang, J. Fang, “HHT based cardiopulmonary coupling analysis for sleep apnea detection”, Sleep Medicine 13 (5), 503–509, 2012.

C. Varon, D. Testelmans, B. Buyse, J. A. K. Suykens and S. Van Huffel, “Sleep apnea classification using least-squares support vector machines on single lead ECG," 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, pp. 5029-5032. doi: 10.1109/EMBC.2013.661067 , 2013.

Nadi Sadr, Philip de Chazal, “Automated Detection of Obstructive Sleep Apnoea by Single-lead ECG through ELM Classification”, Computing in Cardiology 2014, DOI: 10.13140/2.1.3881.3446 , 2014.

How to Cite
Bali, J. S., Nandi, A. V., & Hiremath, P. S. (2018). Performance Comparison Of Ann Classifiers For Sleep Apnea Detection Based On Ecg Signal Analysis Using Hilbert Transform. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 17(2), 7312-7325.