An Empirical Mode Decomposition-Based Method for Feature Extraction and Classification of Sleep Apnea

  • A. Smruthy
  • M. Suchetha
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 490)


Background: Sleep apnea is a breathing disorder found among thirty percentage of the total population. Polysomnography (PSG) analysis is the standard method used for the identification of sleep apnea. Sleep laboratories are conducting this sleep test. Unavailability of sleep laboratories in rural areas makes the detection difficult for ordinary people. There are different methods for detecting sleep apnea. Past researches show that electrocardiogram-based detection is more accurate among other signals. This paper investigates the idea of electrocardiogram (ECG) signals for the recognition of sleep apnea. Methods: In this paper, the classification of healthy and apnea subjects is performed using electrocardiogram signals. The proper feature extraction from these signal segments is executed with the help of empirical mode decomposition (EMD). EMD algorithm decomposes the incoming signals into different intrinsic mode functions (IMFs). Four morphological features are extracted from these IMF levels. These features include the morphological characteristics of QRS complex, T and P waves. The classification of healthy and apnea subjects is done using the machine learning technique called support vector machine. Result: All the experiments are carried out by using St. Vincents University Hospital/University College Dublin Sleep Apnea Database (UCD database). This database is available online in physionet. It is observed from the results that by using empirical mode decomposition; it could be possible to extract the proper morphological features from this ECG segments. This technique also enhances the accuracy of the classifier. The overall sensitivity, specificity, and accuracy achieved for this proposed work are 90, 85, and 93.33%, respectively.


Sleep apnea Polysomnography Electrocardiogram Empirical mode decomposition Empirical mode Functions 


  1. 1.
    Koley BL, Dey D (2013) Real-time adaptive apnea and hypopnea event detection methodology for portable sleep apnea monitoring devices. IEEE Trans Biomed Eng 60(12):3354–3363CrossRefGoogle Scholar
  2. 2.
    Ciołek M et al (2015) Automated detection of sleep apnea and hypopnea events based on robust airflow envelope tracking in the presence of breathing artifacts. IEEE J Biomed Health Inform 19(2):418–429CrossRefGoogle Scholar
  3. 3.
    Jin J, Sánchez-Sinencio E (2015) A home sleep apnea screening device with time-domain signal processing and autonomous scoring capability. IEEE Trans Biomed Circ Syst 9(1):96–104CrossRefGoogle Scholar
  4. 4.
    Gutierrez´-Tobal GC, Alvarez D, del Campo F, Hornero R (2016) Utility of adaboost to detect sleep apnea-hypopnea syndrome from single-channel airflow. IEEE Trans Biomed Eng 63(3):636–646CrossRefGoogle Scholar
  5. 5.
    Karmakar C, Khandoker A, Penzel T, Schobel C, Palaniswami M (2014) Detection of respiratory arousals using photoplethysmography (ppg) signal in sleep apnea patients. IEEE J Biomed Health Inform 18(3):1065–1073CrossRefGoogle Scholar
  6. 6.
    Domingues A, Paiva T, Sanches JM (2014) Sleep and wakefulness state detection in nocturnal actigraphy based on movement information. IEEE Trans Biomed Eng 61(2):426–434CrossRefGoogle Scholar
  7. 7.
    Mora GG, Kortelainen JM, Hernandez EPR, Tenhunen M, Bianchi AM, Méndez MO, Guillermina Guerrero (2015) Evaluation of pressure bed sensor for automatic SAHS screening. IEEE Trans Instrum Meas 64(7):1935–1943CrossRefGoogle Scholar
  8. 8.
    Ahmad S, Batkin I, Kelly O, Dajani HR, Bolic M, Groza V (2013) Multiparameter physiological analysis in obstructive sleep apnea simulated with Mueller maneuver. IEEE Trans Instrum Meas 62(10):2751–2762CrossRefGoogle Scholar
  9. 9.
    Hwang SH, Lee HJ, Yoon HN, Lee YJ, Lee YJ, Jeong DU, Park KS et al (2014) Unconstrained sleep apnea monitoring using polyvinylidene fluoride film-based sensor. IEEE Trans Biomed Eng 61(7):2125–2134CrossRefGoogle Scholar
  10. 10.
    Bsoul M, Minn H, Tamil L (2011) Apnea medassist: real-time sleep apnea monitor using single-lead ecg. IEEE Trans Inf Technol Biomed 15(3):416–427CrossRefGoogle Scholar
  11. 11.
    Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the royal society of London A: mathematical, physical and engineering sciences, vol 454. The Royal Society, pp 903–995Google Scholar
  12. 12.
    Xie G, Zhang B, Wang J, Mao C, Zheng X, Cui Y, Gao Y (2010) Research on the propagation characteristic of non-stationary wind power in microgrid network based on empirical mode decomposition. In: 2010 international conference on power system technology (POW-ERCON), pp 1–6, IEEEGoogle Scholar
  13. 13.
    Li H, Feng X, Cao L, Liang H, Chen X (2016) A new ecg signal classification based on WPD and ApEn feature extraction. Circ Syst Sign Process 35(1):339–352MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Sivaranjni V, Rammohan T (2016) Detection of sleep apnea through ecg signal features. In: 2016 2nd international conference on advances in electrical, electronics, information, communication and bio-informatics (AEEICB), pp 322–326, IEEEGoogle Scholar
  15. 15.
    Khandoker AH, Palaniswami M, Karmakar CK (2009) Support vector machines for automated recognition of obstructive sleep apnea syndrome from ecg recordings. IEEE Trans Inf Technol Biomed 13(1):37–48CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Electronics EngineeringVIT UniversityChennaiIndia

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