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

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.

Assessment of Time and Frequency Domain Entropies to Detect Sleep Apnoea in Heart Rate Variability Recordings from Men and Women

“…A recent study reported 86.6% Acc and 0.941 AROC for a LR classifier modelling with four clinical variables and one symbolic dynamic feature extracted from HRV [57]. Finally, several works have reported 100% Acc when classifying 30 subjects from the PhysioNet Apnea-ECG database, which was used in the Computers in Cardiology Challenge 2000 [58]. However, comparison with studies using this database is difficult since borderline subjects were deliberately removed from the competition.…”

PhysioNet: an NIH research resource for complex signals

“…Diagnosis typically requires a polysomnogram, an expensive and often uncomfortable in-clinic test. The results of the challenge demonstrate that a variety of methods for analyzing an ambulatory ECG recording, which can be obtained for a tenth of the cost at home with little or no discomfort, are just as reliable for diagnosis of sleep apnea (27,28).…”

Non-linear Poincaré analysis of respiratory efforts in sleep apnea

“…One objective of this challenge was to quantify the apnea stress by the number of minutes spent with disordered breathing. The surprisingly good results and methods are summarized in (11).…”