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)