Research Article
Evaluating the Effectiveness of Inhaler Use Among COPD Patients via Recording and Processing Cough and Breath Sounds from Smartphones
@INPROCEEDINGS{10.1007/978-3-030-64214-3_7, author={Anthony Windmon and Sriram Chellappan and Ponrathi R. Athilingam}, title={Evaluating the Effectiveness of Inhaler Use Among COPD Patients via Recording and Processing Cough and Breath Sounds from Smartphones}, proceedings={Mobile Computing, Applications, and Services. 11th EAI International Conference, MobiCASE 2020, Shanghai, China, September 12, 2020, Proceedings}, proceedings_a={MOBICASE}, year={2020}, month={12}, keywords={COPD Lungs Health Cough Breath Machine learning Smartphones Aging}, doi={10.1007/978-3-030-64214-3_7} }- Anthony Windmon
Sriram Chellappan
Ponrathi R. Athilingam
Year: 2020
Evaluating the Effectiveness of Inhaler Use Among COPD Patients via Recording and Processing Cough and Breath Sounds from Smartphones
MOBICASE
Springer
DOI: 10.1007/978-3-030-64214-3_7
Abstract
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.
