# Speech-Based Support System to Supervise Chronic Obstructive Pulmonary Disease Patient Status
## 3 Results
### Voice Samples and Statistical Analysis  
Table 3 shows the statistically significant changes in each of the speech features due to physical effort in both study (S) and control (C) groups, and Table 4 shows the statistically significant changes in each of the speech features due to medication in the study group. Corresponding FEV\({}_{1}\), mMRC, and CAT values are provided along with the statistical measures. In both tables, the smoking habits information has been included next to the subject ID: smoker (s), non-smoker (ns, never smoked), and ex-smoker (es). Only \(p\) values < 0.05 are shown for simplicity. Positive (+) and negative (-) changes in the mean values between rest and physical effort/medication conditions are shown after the \(p\) values. In the control and study groups, the significant differences after doing physical effort showed absolute \(t\) values ranging from \(t(2)\) = 2.13 to \(t(2)\) = 3.96, and \(t(2)\) = 2.38 to \(t(2)\) = 24.10, respectively. In the study group, the significant differences after taking medication showed \(t\) values ranging from \(t(2)\) = 3.10 to \(t(2)\) = 24.51. The percentage of subjects in which each feature exhibited a statistically significant modification is also shown. Values with strongest statistical significance (\(p\) < 0.010) are marked in bold. To avoid data imbalance in scenario 1, the non-COPD instances were oversampled to equal both groups and use a baseline of 50% in accuracy. A total of 314 instances (157 for each class) were used to classify COPD/non-COPD classes from the set of eleven speech features. Then, an automatic classification using a random forest (RF) with 10-fold cross-validation in rest condition was performed, achieving an accuracy of 72.0% of COPD detection in the dataset and using only those instances recorded in rest conditions, over a baseline of 50%. Sensitivity and specificity are 70.0% and 74.5%, respectively. \begin{table}
\begin{tabular}{c c c c} \hline
**Descriptor** & **Study Group (N\({}_{\text{s}}\) = 35)** & **Control Group (N\({}_{\text{c}}\) = 13)** & \(p\) **Value *** \\ \hline Age in years, average (SD) & 74.6 (7.8) & 67.4 (8.9) & 0.01 \\ Female, \(n\) (\%) & 8 (22.9) & 4.0 (30.8) & 0.2 \\ FEV\({}_{1}\) without BD (\%), average (SD) & 67.6 (20.2) 1  & 90.5 (7.6) & \textless{}0.01 \\ FEV\({}_{1}\) with BD (\%), average (SD) 1  & 71.6 (19.8) & \(n/\)a & \(n/\)a \\ SPO2 (\%), average (SD) & 96.6 (2.0) 1  & 97.8 (1.28) & 0.02 \\ Smoking habits, \(n\) (\%) & & & \\ Smoker & 8 (22.9) & 2 (15.4) & 0.05 \\ Non smoker & 27 (77.1) & 11 (84.6) & \(n/\)a \\ Ex-smoker & 25 (92.6) & 7 (63.6) & 0.3 \\ Packages per year, average (SD) & 71.6 (103.2) 2  & 9.8 (9.7) & 0.01 \\ Years of tobacco use, average (SD) & 37.9 (16.5) & 24.3 (12.6) & 0.09 \\ Years since quitting tobacco, average (SD) 2  & 17.7 (15.3) & 31.9 (9.3) & 0.01 \\ mMRC, average (SD) 3  & 1.1 (0.9) & \(n/\)a & \(n/\)a \\ CAT score, average (SD) 4  & 11.3 (7.5) & \(n/\)a & \(n/\)a \\ Number of exacerbations/year, average (SD) 5  & 1.3 (1.1) & \(n/\)a & \(n/\)a \\ \hline \end{tabular}
\end{table} 
Table 2: Characteristics of the participants. In scenario 2, after excluding the most severe cases, the average values of FEV\({}_{1}\) before the bronchodilator (preFEV\({}_{1}\)) and after the bronchodilator (postFEV\({}_{1}\)) were computed and used to set up a threshold to define the classes in the classification experiments, ensuring the maximum balance of instances per class. In preFEV\({}_{1}\), class1 was assigned to instances with preFEV\({}_{1}\) < 75%, and class2 to instances with preFEV\({}_{1}\)\(\geq\) 75%. Similarly, the threshold for postFEV\({}_{1}\) was fixed to 77%. In total, 222 instances were used in case (a)--rest conditions--, and 288 instances in case (b)--all recording conditions--to classify two classes for preFEV\({}_{1}\) and two classes for postFEV\({}_{1}\) from the set of speech features.