Irregular Breathing Classification From Multiple Patient Datasets Using Neural Networks

Complicated breathing behaviors including uncertain and irregular patterns can affect the accuracy of predicting respiratory motion for precise radiation dose delivery. So far investigations on irregular breathing patterns have been limited to respiratory monitoring of only extreme inspiration and expiration. Using breathing traces acquired on a Cyberknife treatment facility, we retrospectively categorized breathing data into several classes based on the extracted feature metrics derived from breathing data of multiple patients. The novelty of this paper is that the classifier using neural networks can provide clinical merit for the statistical quantitative modeling of irregular breathing motion based on a regular ratio representing how many regular/irregular patterns exist within an observation period. We propose a new approach to detect irregular breathing patterns using neural networks, where the reconstruction error can be used to build the distribution model for each breathing class. The proposed irregular breathing classification used a regular ratio to decide whether or not the current breathing patterns were regular. The sensitivity, specificity, and receiver operating characteristiccurve of the proposed irregular breathing pattern detector was analyzed. The experimental results of 448 patients' breathing patterns validated the proposed irregular breathing classifier.