Automatic cough segmentation from non-contact sound recordings in pediatric wards
Automatic cough segmentation from non-contact sound recordings in pediatric wards
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DOI：
10.1016/j.bspc.2015.05.001
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发表时间：
2015-08-01
影响因子：
5.1
通讯作者：
Setyati, Amalia
中科院分区：
文献类型：
--
作者：
Amrulloh, Yusuf A.;Abeyratne, Udantha R.;Setyati, Amalia
咳嗽是几乎所有儿童呼吸道疾病的常见症状。在典型的咨询会议中，医生可能会通过聆听自愿咳嗽或采访患者/护理人员来寻求定性信息（例如湿度）和定量信息（例如咳嗽频率）。该信息可用于鉴别诊断和评估疾病的治疗结果。手动咳嗽评估繁琐、主观，不适合长期记录。研究人员试图开发用于咳嗽评估的自动化系统，但现有系统都没有专门针对儿科人群。在本文中，我们解决了这些问题，并开发了一种从儿科录音中自动识别咳嗽片段的方法。我们的方法基于提取数学特征（例如非高斯性、香农熵和倒谱系数）来描述咳嗽特征。然后，这些特征被用来训练人工神经网络来检测录音中的咳嗽片段。通过对 14 名受试者（录音长度 840 分钟）的前瞻性数据集进行研究，所提出的方法的灵敏度、特异性和 Cohen Kappa 值分别为 93%、98% 和 0.65。这些结果表明，所提出的方法有潜力开发为自动儿科咳嗽计数设备以及咳嗽分析系统的前端。 Crown 版权所有 (C) 2015 由 Elsevier Ltd 出版。保留所有权利。
Cough is a common symptom of almost all childhood respiratory diseases. In a typical consultation session, physicians may seek for qualitative information (e.g., wetness) and quantitative information (e.g., cough frequency) either by listening to voluntary coughs or by interviewing the patients/carers. This information is useful in the differential diagnosis and in assessing the treatment outcome of the disease. The manual cough assessment is tedious, subjective, and not suitable for long-term recording. Researchers have attempted to develop automated systems for cough assessment but none of the existing systems have specifically targeted the pediatric population. In this paper we address these issues and develop a method to automatically identify cough segments from the pediatric sound recordings. Our method is based on extracting mathematical features such as non-Gaussianity, Shannon entropy, and cepstral coefficients to describe cough characteristics. These features were then used to train an artificial neural network to detect coughs segment in the sound recordings. Working on a prospective data set of 14 subjects (sound recording length 840 min), proposed method achieved sensitivity, specificity, and Cohen's Kappa of 93%, 98%, and 0.65, respectively. These results indicate that the proposed method has the potential to be developed as an automated pediatric cough counting device as well as the front-end of a cough analysis system. Crown Copyright (C) 2015 Published by Elsevier Ltd. All rights reserved.