document: Automated breath detection on long-duration signals using feedforward backpropagation artificial neural networks A new breath-detection algorithm is presented , intended to automate the analysis of respiratory data acquired during sleep . The algorithm is based on two independent artificial neural networks -LRB- ANN/sub insp / and ANN/sub expi / -RRB- that recognize , in the original signal , windows of interest where the onset of inspiration and expiration occurs . Postprocessing consists in finding inside each of these windows of interest minimum and maximum corresponding to each inspiration and expiration . The ANN/sub insp / and ANN/sub expi / correctly determine respectively 98.0 % and 98.7 % of the desired windows , when compared with 29 820 inspirations and 29 819 expirations detected by a human expert , obtained from three entire-night recordings . Postprocessing allowed determination of inspiration and expiration onsets with a mean difference with respect to the same human expert of -LRB- mean + or - SD -RRB- 34 + or - 71 ms for inspiration and 5 + or - 46 ms for expiration . The method proved to be effective in detecting the onset of inspiration and expiration in full night continuous recordings . A comparison of five human experts performing the same classification task yielded that the automated algorithm was undifferentiable from these human experts , failing within the distribution of human expert results . Besides being applicable to adult respiratory volume data , the presented algorithm was also successfully applied to infant sleep data , consisting of uncalibrated rib cage and abdominal movement recordings . A comparison with two previously published algorithms for breath detection in respiratory volume signal shows that the presented algorithm has a higher specificity , while presenting similar or higher positive predictive values
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