CPAP Titration Using an Artificial Neural Network: A Randomized Controlled Study

In order to derive the most effective pressure, CPAP titration is performed in the sleep
laboratory during which the pressure is gradually increased until apneas and hypopneas are
abolished in all sleep stages and in all body positions. The technique is however time
consuming and labor intensive. Furthermore, the duration of the study may not be sufficient
to attain this goal because of patient's poor ability to sleep in this environment or due to
difficulty in attaining an appropriate pressure. A predictive algorithm based on
demographic, anthropometric, and polysomnographic data was developed to facilitate the
selection of a starting pressure during the overnight titration study. Yet, the performance
of this model was inconsistent when validated by other centers. One of the potential reasons
for the lack of reproducibility is the complex relation of behavioral processes with
nonlinear attributes. In areas of complex interactions, the artificial neural network (ANN)
has been found to be a more appropriate alternative to linear, parametric statistical tools
due to its inherent property of seeking information embedded in relations among variables
thought to be independent.

Comparison: time to achieve optimal pressure in the conventional technique versus the
intervention model

Inclusion Criteria:

1. patients 18 years of age and older,

2. documented OSA by sleep study defined as AHI > 5/hr

Exclusion Criteria:

1. previously treated OSA,

2. unwilling to undergo a titration study,

3. unable or unwilling to sign an informed consent.