Automated breath detection on long-duration signals using feedforward backpropagation artificial neural networks
Automated breath detection on long-duration signals using feedforward backpropagation artificial neural networks
复制标题
使用前馈反向传播人工神经网络对长持续时间信号进行自动呼吸检测
DOI：
10.1109/tbme.2002.803514
复制
发表时间：
2002
影响因子：
4.6
通讯作者：
Y. Verbandt
中科院分区：
文献类型：
--
作者：
R. Sá;Y. Verbandt
提出了一种新的呼吸检测算法，旨在自动分析睡眠期间采集的呼吸数据。该算法是基于两个独立的人工神经网络（ANN/sub insp/和ANN/sub expi/），识别，在原始信号中，感兴趣的窗口的吸气和呼气的开始发生。后处理包括在这些感兴趣的窗口中的每一个内找到对应于每次吸气和呼气的最小值和最大值。人工神经网络/子insp/和人工神经网络/子expi/正确地确定分别为98.0%和98.7%的所需的窗口，当与29 820灵感和29 819由人类专家检测到，从三个整晚的录音。后处理允许确定吸气和呼气开始，相对于同一人类专家的平均差异为（平均值/spl plusmn/ SD）吸气34 /spl plusmn/ 71 ms，呼气5 /spl plusmn/ 46 ms。该方法被证明是有效的，在整个夜间连续记录检测吸气和呼气的开始。对执行相同分类任务的五名人类专家进行比较，发现自动算法与这些人类专家无法区分，未能在人类专家结果的分布范围内。除了适用于成人呼吸量数据，所提出的算法也成功地应用于婴儿睡眠数据，包括未校准的胸腔和腹部运动记录。与两种已发表的呼吸量信号呼吸检测算法的比较表明，所提出的算法具有更高的特异性，同时呈现相似或更高的阳性预测值。
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 /spl plusmn/ SD) 34 /spl plusmn/ 71 ms for inspiration and 5 /spl plusmn/ 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.