Validation of an Automated Cough Detection Algorithm for Tracking Recovery of Pulmonary Tuberculosis Patients

<div><h3>Background</h3><p>A laboratory-free test for assessing recovery from pulmonary tuberculosis (TB) would be extremely beneficial in regions of the world where laboratory facilities are lacking. Our hypothesis is that analysis of cough sound recordings may provide such a test. In the current paper, we present validation of a cough analysis tool.</p> <h3>Methodology/Principal Findings</h3><p>Cough data was collected from a cohort of TB patients in Lima, Peru and 25.5 hours of recordings were manually annotated by clinical staff. Analysis software was developed and validated by comparison to manual scoring. Because many patients cough in bursts, coughing was characterized in terms of <em>cough epochs</em>. Our software correctly detects 75.5% of cough episodes with a specificity of 99.6% (comparable to past results using the same definition) and a median false positive rate of 4 false positives/hour, due to the noisy, real-world nature of our dataset. We then manually review detected coughs to eliminate false positives, in effect using the algorithm as a pre-screening tool that reduces reviewing time to roughly 5% of the recording length. This cough analysis approach provides a foundation to support larger-scale studies of coughing rates over time for TB patients undergoing treatment.</p> </div

Socio-economic, acculturation and behavioral characteristics of the study population by sex.

*<p>Chi squared test was used to compare characteristics by sex.</p

Scatterplots showing the number of epochs found under ‘<i>epoch1</i> ’ and ‘<i>epoch2</i>’ definitions, for semi-automated algorithm results.

<p>Note that the ‘<i>epoch2</i>’ definition cannot be applied to nurse assignments in our dataset. The correlation coefficient between the two definitions is 0.97.</p

Relationship of cardiovascular disease risk factors with socioeconomic, acculturation, behavioral variables with male as reference (n=567).

<p>All odds ratios are adjusted for age.</p><p>Bold=p<0.05.</p>†<p>Male sex is the reference.</p

Boxplot comparing semi-automated estimate of cough count at day 0 and day 14.

<p>The plot shows 25^th, 50^th, and 75^th percentiles, with outliers (1.5*IQR) are shown as ‘+’. At Day 14, the box collapses as 25^th, 50^th, and 75^th percentiles are all zero.</p

Example issue with simple energy detector.

<p>The threshold may miss the start of the acoustic event and is frequently crossed during speech events (shown above), increasing the chance of misclassification.</p

High-level flowchart of cough detection algorithm.

<p>High-level flowchart of cough detection algorithm.</p

Bland-Altman plot comparing the number of epochs (definition <i>epoch1</i>) found by the nurses and the reviewed algorithm (i.e. semi-automated approach).

<p>The mean of the two estimates (used in place of a gold standard) is plotted vs. the difference between nurse and semi-automated results. The mean bias and limits of agreement (+/−1.96 σ) are also shown. The plot shows the bias is not statistically significant and there is no evidence of changing agreement as a function of cough epoch count.</p

Clinical characteristics of the study population by sex.

*<p>Chi squared test was used to compare characteristics by sex.</p><p>HDL; high-density lipoprotein.</p

Prevalence of Metabolic Syndrome by different waist circumference cutoffs by sex (n=589).

<p>*Based on IDF WC cut-off criteria [WC ≥90 cm (men) or ≥80 cm (women)]. **Based on LASO WC cut-off criteria [WC ≥97 cm (men) or ≥94 cm (women)].</p