Wheezing recognition algorithm using recordings of respiratory sounds at the mouth in a pediatric population
Wheezing recognition algorithm using recordings of respiratory sounds at the mouth in a pediatric population
复制标题
DOI：
10.1016/j.compbiomed.2016.01.002
复制
发表时间：
2016-03-01
影响因子：
7.7
通讯作者：
Delclaux, Christophe
中科院分区：
文献类型：
--
作者：
Bokov, Plamen;Mahut, Bruno;Delclaux, Christophe
背景：儿童的呼吸系统疾病是医生就诊的常见原因。当父母在就诊期间听到不再存在的喘息声时，诊断就会出现困难。因此，一种门诊客观的喘息识别工具具有临床应用价值。方法：将智能手机放在口腔附近，利用记录到的呼吸音实现喘息识别。在儿科急诊科总共获得了186份录音，其中大部分是幼儿(平均年龄20个月)。在剔除有伪影和单一听诊的录音后，对95个听诊诊断符合的录音(27个有喘息，68个没有)进行两阶段算法(基于机器学习算法的信号分析和模式分类器)进行分类。结果：基于支持向量机的算法具有最好的性能(71.4%的灵敏度和88.9%的特异度)。我们进一步对一组39段只有一个算子的录音进行了测试，发现算法和算子之间有较好的一致性(kappa=0.28，CI95%[0.12，0.45])。结论：这种算法的主要优点是用于非接触式录音，因此在儿科人群中具有很高的应用价值。(C)2016爱思唯尔有限公司。保留所有权利。
Background: Respiratory diseases in children are a common reason for physician visits. A diagnostic difficulty arises when parents hear wheezing that is no longer present during the medical consultation. Thus, an outpatient objective tool for recognition of wheezing is of clinical value.Method: We developed a wheezing recognition algorithm from recorded respiratory sounds with a Smartphone placed near the mouth. A total of 186 recordings were obtained in a pediatric emergency department, mostly in toddlers (mean age 20 months). After exclusion of recordings with artefacts and those with a single clinical operator auscultation, 95 recordings with the agreement of two operators on auscultation diagnosis (27 with wheezing and 68 without) were subjected to a two phase algorithm (signal analysis and pattern classifier using machine learning algorithms) to classify records.Results: The best performance (71.4% sensitivity and 88.9% specificity) was observed with a Support Vector Machine-based algorithm. We further tested the algorithm over a set of 39 recordings having a single operator and found a fair agreement (kappa=0.28, CI95% [0.12, 0.45]) between the algorithm and the operator.Conclusions: The main advantage of such an algorithm is its use in contact-free sound recording, thus valuable in the pediatric population. (C) 2016 Elsevier Ltd. All rights reserved.