Development and technical validation of a smartphone-based pediatric cough detection algorithm

Abstract

Introduction: Coughing is a common symptom in pediatric lung disease and cough frequency has been shown to be correlated to disease activity in several conditions. Automated cough detection could provide a noninvasive digital biomarker for pediatric clinical trials or care. The aim of this study was to develop a smartphone-based algorithm that objectively and automatically counts cough sounds of children.

Methods: The training set was composed of 3228 pediatric cough sounds and 480,780 noncough sounds from various publicly available sources and continuous sound recordings of 7 patients admitted due to respiratory disease. A Gradient Boost Classifier was fitted on the training data, which was subsequently validated on recordings from 14 additional patients aged 0-14 admitted to the pediatric ward due to respiratory disease. The robustness of the algorithm was investigated by repeatedly classifying a recording with the smartphone-based algorithm during various conditions.

Results: The final algorithm obtained an accuracy of 99.7%, sensitivity of 47.6%, specificity of 99.96%, positive predictive value of 82.2% and negative predictive value 99.8% in the validation dataset. The correlation coefficient between manual- and automated cough counts in the validation dataset was 0.97 (p < .001). The intra- and interdevice reliability of the algorithm was adequate, and the algorithm performed best at an unobstructed distance of 0.5-1 m from the audio source.

Conclusion: This novel smartphone-based pediatric cough detection application can be used for longitudinal follow-up in clinical care or as digital endpoint in clinical trials.

Keywords: algorithm; asthma; cough; detection; lung disease; machine-learning; pediatrics.

Figure 1

Correlation manual‐ and automatic cough count in validation dataset. Pearson correlation between manually counted coughs and automatically detected coughs. Each dot represents an individual subject in the validation dataset

Figure 2

Performance of the algorithm under varying circumstances. (A) Intra‐device repeatability. Each individual line represents a different session with the same device. (B) Inter‐device repeatability. Each individual line represents a different session with a different device of the same type. (C) Influence of device distance from the audio source. (D) Influence of physical barrier or ambient background noise. In each of the panels, the light‐blue line is the reference from the audio file [Color figure can be viewed at wileyonlinelibrary.com]