title: Automated breath detection on long-duration signals using feedforward backpropagation artificial neural networks
abstract: 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 (ANN/sub insp/ and ANN/sub expi/) 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 (mean +or- SD) 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
keyphrases: automated breath detection long-duration signals feedforward backpropagation artificial neural networks inspiration expiration postprocessing human experts entire-night recordings automated algorithm adult respiratory volume data infant sleep data uncalibrated rib cage abdominal movement recordings respiratory movements 34 ms 5 ms