Power-based segmentation of respiratory signals using forward-backward bank filtering.

We present an automated method for the segmentation of ribcage and abdominal signals measured by noninvasive respiratory inductance plethysmography (RIP) into quiet breathing and artifact-corrupted segments. This procedure, which involves forward-backward filtering, is applicable to the automated off-line analysis of long records of respiratory signals. Examples of applications include home and sleep laboratory studies of cardiorespiratory data. The new procedure was successfully applied to the segmentation of cardiorespiratory signals acquired post-operatively from infants in the recovery room of the Montreal Children's Hospital (MCH).

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

In [3] 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.