The utility of neural network in the diagnosis of Cheyne-Stokes respiration.
The aim of this study was to design a diagnostic model to identify patients with Cheyne-Stokes respiration (CSR-CSA) based on indices of oximetric spectral analysis. A retrospective analysis of oximetric recordings of 213 sleep studies conducted over a one-year period at a Veterans Affairs medical facility was performed. A probabilistic neural network (PNN) was developed from salient features of the oximetric spectral analysis, desaturation events and the delta index. A fivefold cross-validation was used to assess the accuracy of the neural network in identifying CSR-CSA. When compared to overnight polysomnography, the PNN achieved a sensitivity of 100% (95% confidence interval [CI] 85%-100%) and a specificity of 99% (95% 97%-100%) with a corresponding area under the curve of 99% (95% CI 99%-100%). When combined with overnight pulse oximetry, PNN offers an accurate and easily applicable tool to detect CSR-CSA.
Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, Veteran Affairs Medical Center and Erie County Medical Center, Buffalo, NY, USA.
A A
AA
El-Solh
U J
UJ
Magalang
M J
MJ
Mador
J
J
Dmochowski
S
S
Veeramachaneni
A
A
Saberi
A M
AM
Draw
B B
BB
Lieber
B J B
BJ
Grant
D002639:Cheyne-Stokes Respiration / Q000175:diagnosis*; D016001:Confidence Intervals; D006801:Humans; D016571:Neural Networks, Computer; D010092:Oximetry; D017286:Polysomnography; D012372:ROC Curve; D012680:Sensitivity and Specificity; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis