Medical and Biological Engineering and Computing

, Volume 40, Issue 4, pp 402–407

Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings

Authors

    • Department of Respiratory Critical Care MedicineHospital of Philipps University
  • J. McNames
    • Electrical & Computer EngineeringPortland State University
  • P. de Chazal
    • Department of Electronic & Electrical EngineeringUniversity College
  • B. Raymond
    • Department of Respiratory Physiology
  • A. Murray
    • Regional Medical Physics DepartmentFreeman Hospital
  • G. Moody
    • Harvard-MIT Division of Health Sciences & Technology
Article

DOI: 10.1007/BF02345072

Cite this article as:
Penzel, T., McNames, J., de Chazal, P. et al. Med. Biol. Eng. Comput. (2002) 40: 402. doi:10.1007/BF02345072

Abstract

Sleep apnoea is a common disorder that is usually diagnosed through expensive studies conducted in sleep laboratories. Sleep apnoea is accompanied by a characteristic cyclic variation in heart rate or other changes in the waveform of the electrocardiogram (ECG). If sleep apnoea could be diagnosed using only the ECG, it could be possible to diagnose sleep apnoea automatically and inexpensively from ECG recordings acquired in the patient's home. This study had two parts. The first was to assess the ability of an overnight ECG recording to distinguish between patients with and without apnoea. The second was to assess whether the ECG could detect apnoea during each minute of the recording. An expert, who used additional physiological signals, assessed each of the recordings for apnoea. Research groups were invited to access data via the world-wide web and submit algorithm results to an international challenge linked to a conference. A training set of 35 recordings was made available for algorithm development, and results from a test set of 35 different recordings were made available for independent scoring. Thirteen algorithms were compared. The best algorithms made use of frequency-domain features to estimate changes in heart rate and the effect of respiration on the ECG waveform. Four of these algorithms achieved perfect scores of 100% in the first part of the study, and two achieved an accuracy of over 90% in the second part of the study.

Keywords

Heart rate variabilitySleep apnoeaPhysiologic signal databasePhysioNetECGEstimated respiration
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© IFMBE 2002