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IEEE J Biomed Health Inform. 2018 Sep 24. doi: 10.1109/JBHI.2018.2872038. [Epub ahead of print]

TussisWatch: A Smartphone System to Identify Cough Episodes as Early Symptoms of Chronic Obstructive Pulmonary Disease and Congestive Heart Failure.

Abstract

Chronic Obstructive Pulmonary Disease (COPD) and Congestive Heart Failure (CHF) are leading chronic health concerns among the aging population today. They are both typically characterized by episodes of cough that share similarities. In this paper, we design TussisWatch, a smart-phone based system to record and process cough episodes for early identification of COPD or CHF. In our technique, for each cough episode, we do the following: (1) filter noise; (2) use domain expertise to partition each cough episode into multiple segments, indicative of disease or otherwise; (3) identify a limited number of audio features for each cough segment; (4) remove inherent biases as a result of sample size differences; and finally, (5) design a two-level classification scheme, based on the idea of Random Forests, to process a recorded cough segment. Our classifier, at the first-level, identifies whether or not a given cough segment indicates a disease. If yes, the second level classifier identifies the cough segment as symptomatic of COPD or CHF. Testing with a cohort of 9 COPD, 9 CHF and 18 CONTROLS subjects spread across both genders, races and ages, our system achieves good performance in terms of Sensitivity, Specificity, Accuracy and Area under ROC curve. The proposed system has the potential to aid early access to healthcare, and may be also used to educate patients on self-care at home.

PMID:
30273159
DOI:
10.1109/JBHI.2018.2872038

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