Evaluating the Effectiveness of Inhaler Use Among COPD Patients via Recording and Processing Cough and Breath Sounds from Smartphones
Evaluating the Effectiveness of Inhaler Use Among COPD Patients via Recording and Processing Cough and Breath Sounds from Smartphones
复制标题
通过记录和处理智能手机的咳嗽和呼吸音来评估慢性阻塞性肺病患者使用吸入器的有效性
DOI：
10.1007/978-3-030-64214-3_7
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发表时间：
2020
影响因子：
4
通讯作者：
P. Athilingam
中科院分区：
文献类型：
--
作者：
Anthony Windmon;Sriram Chellappan;P. Athilingam
慢性阻塞性肺疾病（COPD）是当今老年人的一个主要健康问题。慢性咳嗽和喘息是肺部粘液积聚的结果，是慢性阻塞性肺病的主要症状。建议慢性阻塞性肺病患者定期通过吸入器给自己服药，吸入器可以将药物输送到肺部，分解粘液，缓解喘息。不幸的是，许多患者没有正确使用吸入器装置，导致COPD症状没有改善，健康状况恶化。在本文中，我们设计了机器学习（支持向量机）算法，该算法运行在患者咳嗽和呼吸声音的mel频率倒谱系数上（通过智能手机在吸入器使用前后记录），以检测吸入器使用的有效性。研究人员对55名临床诊断为慢性阻塞性肺病的男女患者进行了队列研究，从多个指标评估了我们的系统，包括准确率、召回率、灵敏度和特异性。我们的系统在检测吸入器使用有效性方面达到了接近80%的准确性。我们提出的系统可以帮助慢性阻塞性肺病患者改善自我护理程序，并减少因症状加重而再次住院的比率。
Chronic Obstructive Pulmonary Disease (COPD) is a major health concern for elders today. Chronic cough and wheezing, which occur in the lungs as a result of mucus buildup are the main symptoms of COPD. COPD patients are advised to regularly medicate themselves via an inhaler, which delivers medicine to the lungs to break down mucus and relieve wheezing. Unfortunately, many patients do not use their inhaler devices correctly, resulting in no improvement of COPD symptoms, and worsened health. In this paper, we design machine learning (Support Vector Machine) algorithms operating on Mel-frequency Cepstral Coefficients of cough and breath sounds of patients (recorded via smartphones before and after inhaler usage) to detect the effectiveness of inhaler usage. Using a cohort of 55 clinically diagnosed COPD patients, spread across both genders, we evaluate our system from multiple metrics, including Precision, Recall, Sensitivity and Specificity. Our system achieved accuracies close to 80% in detecting effectiveness of inhaler usage. Our proposed system can aid COPD patients in improved selfcare routines, and also reduce the rate of re-hospitalizations caused by exacerbated symptoms.