Detection of movement artifacts in respiratory inductance plethysmography: performance analysis of a Neyman-Pearson energy-based detector

In a previous paper, we developed a method for the automated estimation of the phase relation between thoracic and abdominal signals measured by noninvasive respiratory inductance plethysmography (RIP). In the present paper, we improve on the phase estimator by including an automated procedure for the detection of periods of gross body movements. We assume that the number of sleep obstructive events during periods of gross body movements is zero in probability. We hope that combining the phase estimator with the gross body movement detector should yield improved diagnostic tools for the automated classification of obstructive hypopnea events.