Automatic Cough Classification for Tuberculosis Screening in a Real-World Environment

Abstract

We present first results showing that it is possible to automatically discriminate between the coughing sounds produced by patients with tuberculosis (TB) and those produced by patients with other lung ailments in a real-world noisy environment. Our experiments are based on a dataset of cough recordings obtained in a real-world clinic setting from 16 patients confirmed to be suffering from TB and 33 patients that are suffering from respiratory conditions, confirmed as other than TB. We have trained and evaluated several machine learning classifiers, including logistic regression (LR), support vector machines (SVM), knearest neighbour (KNN), multilayer perceptrons (MLP) and convolutional neural networks (CNN) inside a nested k-fold cross-validation and find that, although classification is possible in all cases, the best performance is achieved using the LR classifier. In combination with feature selection by sequential forward search (SFS), our best LR system achieves an area under the ROC curve (AUC) of 0.94 using 23 features selected from a set of 78 high-resolution mel-frequency cepstral coefficients (MFCCs). This system achieves a sensitivity of 93% at a specificity of 95% and thus exceeds the 90% sensitivity at 70% specificity specification considered by the WHO as minimal requirements for community-based TB triage test. We conclude that automatic classification of cough audio sounds is promising as a viable means of low-cost easily-deployable front-line screening for TB, which will greatly benefit developing countries with a heavy TB burden.

Wake-Cough: cough spotting and cougher identification for personalised long-term cough monitoring

“…No other information of the patients was collected due to ethical constraints. Wallacedene dataset was collected inside an outdoor booth next to a busy primary health clinic in Wallacedene, near Cape Town, South Africa representing a real-world environment where a TB test would likely to be deployed [19] (Figure 1). Patients were asked to count from 1 to 10, then cough, take a few deep breaths, and cough again, thus producing forced coughs.…”

Automatic non-invasive Cough Detection based on Accelerometer and Audio Signals

“…MFCCs are successfully used as features in audio analysis and especially in automatic speech recognition [44,45]. They can differentiate dry coughs from wet coughs [46] and also classify tuberculosis [47] and COVID-19 coughs [9,48]. We have used the traditional MFCC extraction method considering higher resolution MFCCs along with the velocity (firstorder difference, ∆) and acceleration (second-order difference, ∆∆) as adding these has shown classifier improvement in the past [49].…”

“…It has produced promising results in discriminating influenza coughs from other coughs [62] in the past. MLP has also been applied to classify TB coughs [47,59] and detect coughs in general [25,63]. The penalty ratios, along with the number of neurons are used as the hyperparameters, optimised using the leave-one-out cross-validation process (Figure 7 and Section 5).…”