The present and future of cough counting tools

Abstract

The widespread use of cough counting tools has, to date, been limited by a reliance on human input to determine cough frequency. However, over the last two decades advances in digital technology and audio capture have reduced this dependence. As a result, cough frequency is increasingly recognised as a measurable parameter of respiratory disease. Cough frequency is now the gold standard primary endpoint for trials of new treatments for chronic cough, has been investigated as a marker of infectiousness in tuberculosis (TB), and used to demonstrate recovery in exacerbations of chronic obstructive pulmonary disease (COPD). This review discusses the principles of automatic cough detection and summarises key currently and recently used cough counting technology in clinical research. It additionally makes some predictions on future directions in the field based on recent developments. It seems likely that newer approaches to signal processing, the adoption of techniques from automatic speech recognition, and the widespread ownership of mobile devices will help drive forward the development of real-time fully automated ambulatory cough frequency monitoring over the coming years. These changes should allow cough counting systems to transition from their current status as a niche research tool in chronic cough to a much more widely applicable method for assessing, investigating and understanding respiratory disease.

Keywords: Cough; cough frequency; cough monitor.

Figure 1

The component phases of the cough sound: opening of the vocal cords (first phase), air flow through the open larynx (second phase), and re-apposition of the cords (third, voiced, phase—not always present). Shown as changes in sound amplitude (A) and frequency (B).

Figure 2

Scheme of analysis for automatic cough detection. EMG, electromyography; ECG, electrocardiography; MFCC, Mel-frequency cepstral coefficients; STFT, short-time Fourier transform; ANN, artificial neural network; DNN, deep neural network; KNN, K-nearest neighbours; SVM, support vector machines; HMM, hidden Markov models.