Real-Time Identification of Upper Airway Occlusion Using Electrical Impedance Tomography.

STUDY

objectivesReal-time monitoring of upper airway collapse during sleep could be instrumental for studies in biomechanics of obstructive sleep apnea (OSA) and selecting individualized treatment modalities. Although some imaging techniques are used under sedated sleep, none are available during the entire natural sleep process. We hypothesized that electrical impedance tomography (EIT) can be used for noninvasive continuous imaging of the upper airway during natural sleep and quantifying upper airway collapse in terms of its size.

methodsAfter determining surface landmarks to attach the electrodes for monitoring the retroglossal airway, EIT was conducted in 10 healthy participants. As a feasibility test of EIT in detecting upper airway collapse, transient airway closure was induced by the swallowing maneuver. These EIT images were confirmed by simultaneous magnetic resonance imaging (MRI) scans. Subsequently, EIT scans were conducted in 7 healthy participants and 10 patients with OSA under nonsedated sleep to determine whether it could identify upper airway narrowing or collapse. Respiratory events were identified by concurrent polysomnography (PSG).

resultsSwallowing-induced airway closure was identified successfully in all 10 participants on simultaneous EIT and MRI scans. Sizes and positions of the upper airway closures in reconstructed EIT images were well correlated with those in magnetic resonance images. Obstructive hypopnea and apnea were detected successfully by EIT in 10 patients with OSA, and no significant changes in EIT data were observed in 7 healthy participants during concurrent EIT and PSG tests. Additionally, conductivity changes in the airway were greater during obstructive apnea than during hypopnea (64.3% versus 26.3%, respectively;

conclusionsEIT could be a useful real-time monitoring device for detecting upper airway narrowing or collapse during natural sleep in patients with OSA. Currently, changes in the upper airway size can be estimated with good accuracy, but shape estimation needs future improvements in the EIT image quality.