Stimulating rapid research advances via focused competition: the Computers in Cardiology Challenge 2000

Obstructive sleep apnea is associated with a variety of serious health risks. Standard methods for detecting and quantifying sleep apnea are based on respiration monitoring, but previous studies have suggested that apnea detection based on the ECG alone might be possible. The authors therefore offered a challenge to the research community, to demonstrate the efficacy of ECG-based methods for apnea detection using a large, well-characterized, and representative set of data made freely available via the Internet. The goal of the contest was to stimulate effort and advance the state of the art in this clinically significant problem, and to foster both friendly competition and wide-ranging collaborations, The event was an outstanding success, with most entrants achieving 90% to 100% accuracy in identifying subjects with significant apnea, and minute-by-minute apnea detection accuracy between 85% and 93%, comparable to the concurrence of human experts scoring full polysomnograms.

[1]  C. Zywietz,et al.  Detection of sleep apnea in single channel ECGs from the PhysioNet data base , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[2]  Phyllis K. Stein,et al.  Detecting OSAHS from patterns seen on heart-rate tachograms , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[3]  Philip Langley,et al.  Detection of sleep apnoea from frequency analysis of heart rate variability , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[4]  Michael J. Chappell,et al.  Screening for obstructive sleep apnoea based on the electrocardiogram-the computers in cardiology challenge , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[5]  G. Passariello,et al.  Bayesian hierarchical model with wavelet transform coefficients of the ECG in obstructive sleep apnea screening , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[6]  Hartmut Dickhaus,et al.  Recognition and quantification of sleep apnea by analysis of heart rate variability parameters , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[7]  C. Peng,et al.  Detection of obstructive sleep apnea from cardiac interbeat interval time series , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[8]  Bruce W. Pennycook,et al.  Detection of obstructive sleep apnea through auditory display of heart rate variability , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[9]  G. Moody,et al.  The apnea-ECG database , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[10]  A. Rechtsteiner,et al.  Sleep Apnea Classi£cation Based on Frequency of Heart Rate Variability , 2000 .

[11]  R G Mark,et al.  PhysioNet: a research resource for studies of complex physiologic and biomedical signals , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[12]  M. Kryger,et al.  Mortality and apnea index in obstructive sleep apnea. Experience in 385 male patients. , 1988, Chest.

[13]  P. Lavie,et al.  Across-night lengthening of sleep apneic episodes. , 1981, Sleep.

[14]  Christian Guilleminault,et al.  Clinical overview of the sleep apnea syndromes , 1978 .

[15]  W C Dement,et al.  The sleep apnea syndromes. , 1976, Annual review of medicine.