Classification of asthmatic breath sounds: Preliminary results of the classifying capacity of human examiners versus artificial neural networks
Classification of asthmatic breath sounds: Preliminary results of the classifying capacity of human examiners versus artificial neural networks
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DOI：
10.1006/cbmr.1999.1522
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发表时间：
1999-10-01
期刊：
影响因子：
--
通讯作者：
Dooijes, EH
中科院分区：
文献类型：
--
作者：
Rietveld, S;Oud, M;Dooijes, EH
为了持续监测病人的呼吸状况，例如在重症监护病房，需要电脑辅助。现有的机械装置，如呼气峰值流量计，只能提供偶然的测量。此外，这些方法需要患者的配合，这在ICU通常是不可能的。对哮喘呼吸音等复杂现象的评价可以利用人工神经网络来完成。为了探讨人工神经网络的优点，本研究比较了人工神经网络和人类检验员对呼吸音分类的能力。在体内记录了50名患有哮喘的学龄儿童和10名对照儿童的呼吸音。持续时间为20秒的声音间隔从哮喘发作期、缓解期和对照组中随机取样。将样本数字化并与呼气流量峰值相关。从每个间隔中选择两个完整的呼吸周期。对每个选定的呼吸周期，计算傅里叶功率谱。利用人工神经网络对得到的光谱向量集进行分类。人类评估了光谱的图形显示。人类检查员不能通过检查光谱图来清楚地区分这三组。自分类神经网络的分类确认了至少三个类的存在；但是，对11个阶级的歧视似乎更为适当。有监督网络获得了很好的结果：高达95%的训练向量和43%的测试向量可以被正确分类。这三组病人，如预先区分的，并不对应于三组明显分离的谱图。似乎有三个以上的班级。人类无法承担光谱复杂性，呈现负分类结果。然而，人工神经网络能够处理分类任务并显示出积极的结果，(C) 1991学术出版社。
For continuous monitoring of the respiratory condition of patients, e.g., at the intensive care unit, computer assistance is required. Existing mechanical devices, such as the peak expiratory flow meter, provide only with incidental measurements. Moreover such methods require cooperation of the patient, which at, e.g., the ICU is usually not possible. The evaluation of complicated phenomena such as asthmatic respiratory sounds may be accomplished by use of artificial neural networks. To investigate the merit of artificial neural networks, the capacities of neural networks and human examiners to classify breath sounds were compared in this study. Breath sounds were in vivo recorded from 50 school-age children with asthma and from 10 controls. Sound intervals with a duration of 20 seconds were randomly sampled from,asthmatics during exacerbation, asthmatics in remission, and controls. The samples were digitized and related to peak expiratory flow. From each interval, two full breath cycles were selected. Of each selected breath cycle, a Fourier power spectrum was calculated. The so-obtained set of spectral vectors was classified by means of artificial neural networks. Humans evaluated graphic displays of the spectra. Human examiners could not clearly discriminate between the three groups by inspecting the spectrograms. Classification by self-classifying neural networks confirmed the existence of at least three classes; however, discrimination of 11 classes seemed more appropriate. Good results were obtained with supervised networks: as much as 95% of the training vectors could be classified correctly, and 43% of the test vectors. The three patient groups, as discriminated in advance, do not correspond with three sharply separated sets of spectrograms. More than three classes seem to be present. Humans cannot take up the spectral complexity and showed negative classification results. Artificial neural networks, however, are able to handle classification tasks and show positive results, (C) 1991 Academic Press.