Detection of Sleep Apnea Based on HRV Analysis of ECG Signal

  • A. J. Heima
  • S. Arun KarthickEmail author
  • L. Suganthi
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


Sleep apnea is a breathing disorder which occurs during sleep. Sleep apnea causes more health-threatening problems such as daytime sleepiness, fatigue and cognitive problems, coronary arterial disease, arrhythmias, and stroke. However, there is an extremely low public consciousness about this disease. The most common type of sleep apnea is obstructive sleep apnea (OSA). Polysomnography (PSG) is the widely used technique to detect OSA. Obstructive sleep apnea is extremely undiagnosed due to the inconvenient and costly polysomnography (PSG) testing procedure at hospitals. Moreover, a human expert has to monitor the patient overnight. Hence, there is a requirement of new method to diagnose sleep apnea with efficient algorithms using noninvasive peripheral signal. This work is basically aimed at detection of sleep apnea using a physiological signal electrocardiogram (ECG) alone which is taken from free online apnea ECG database provided by PhysioNet/PhysioBank. This database consists of 70 ECG recordings. A detailed time- and frequency-domain features and nonlinear features extracted from the RR interval of the ECG signals for observing minutes of sleep apnea are occurred in this work. Time-domain features mean HR (P = 0.0093, r = 0.3593) and RR interval mean (ms) (p = 0.0003, r = 0.376), frequency-domain features VLF power (%) (P = 0.00659, r = 0.1081) and HF power (%) (P = 0.00135, r = 0.41138), and nonlinear analysis feature SD1 (P = 0.00039, r = 0.18998), significantly different for normal and apnea ECG. Further supervised learning algorithms have been used to classify ECG signal to differentiate normal and apnea data. The overall efficiency is 90.5%. Algorithms which deal ECG signal along with respiratory signal will give more incite about apnea disease and also the classification accuracy may be improved.


Effective features Electrocardiogram Neural network Sleep disorder 


  1. 1.
    Rotariu C, Cristea C, Arotaritei D, Bozomitu RG, Pasarica A (2016) Continuous respiratory monitoring device for detection of sleep apnea episodes. In: 2016 IEEE 22nd international symposium for design and technology in electronic packaging (SIITME), pp 106–109Google Scholar
  2. 2.
    Martín-González S, Navarro-Mesa JL, Juliá-Serdá G, Kraemer JF, Wessel N, Ravelo-García AG (2017) Heart rate variability feature selection in the presence of sleep apnea: an expert system for the characterization and detection of the disorder. Comput Biol Med 91:47–58CrossRefGoogle Scholar
  3. 3.
    Fan SH, Chou CC, Chen WC, Fang WC (2015) Real-time obstructive sleep apnea detection from frequency analysis of EDR and HRV using Lomb Periodogram. In: 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 5989–5992Google Scholar
  4. 4.
    Hassan AR, Haque MA (2017) An expert system for automated identification of obstructive sleep apnea from single-lead ECG using random under sampling boosting. Neurocomputing 235:122–130CrossRefGoogle Scholar
  5. 5.
    Saxena N, Shinghal K (2015) Extraction of various features of ECG signal. Int J Eng Sci Emerg Technol 7(4):707–714Google Scholar
  6. 6.
    Rodríguez R, Mexicano A, Bila J, Cervantes S, Ponce R (2015) Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis. J Appl Res Technol 13(2):261–269CrossRefGoogle Scholar
  7. 7.
    Mannurmath JC, Raveendra M (2014) MATLAB based ECG signal classification. Int J Sci Eng Technol Res (IJSETR) 3(7):1946–1950Google Scholar
  8. 8.
    Corrales MM, de la Cruz Torres B, Esquivel AG, Salazar MAG, Orellana JN (2012) Normal values of heart rate variability at rest in a young, healthy and active Mexican population. Health 4(07):377CrossRefGoogle Scholar
  9. 9.
    Ma HT, Liu J, Zhang P, Zhang X, Yang M (2015) Real-time automatic monitoring system for sleep apnea using single-lead electrocardiogram. In: TENCON 2015-2015 IEEE region 10 conference, pp 1–4Google Scholar
  10. 10.
  11. 11.
    Khalil MM, Rifaie OA (1998) Electrocardiographic changes in obstructive sleep apnoea syndrome. Respir Med 92(1):25–27CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Quiceno-Manrique AF, Alonso-Hernandez JB, Travieso-Gonzalez CM, Ferrer-Ballester MA, Castellanos-Dominguez G (2009) Detection of obstructive sleep apnea in ECG recordings using time-frequency distributions and dynamic features. In: Annual international conference of the IEEE engineering in medicine and biology society, 2009, EMBC 2009, pp 5559–5562Google Scholar
  14. 14.
    de Chazal P, Penzel T, Heneghan C (2004) Automated detection of obstructive sleep apnoea at different time scales using the electrocardiogram. Physiol Meas 25(4):967CrossRefGoogle Scholar
  15. 15.
    Kocak O, Bayrak T, Erdamar A, Ozparlak L, Telatar Z, Erogul O (2012) Automated detection and classification of sleep apnea types using electrocardiogram (ECG) and electroencephalogram (EEG) features. In: Advances in electrocardiograms-clinical applications. TurkeyGoogle Scholar
  16. 16.
    Behbahani S, Moridani MK (2015) Non-linear Poincaré analysis of respiratory efforts in sleep apnea. Bratisl Lek Listy 116(7):426–432Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringSSN College of EngineeringChennaiIndia

Personalised recommendations