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Development and technical validation of a smartphone-based pediatric cough detection algorithm
Matthijs D Kruizinga1,2,3, Ahnjili Zhuparris1, Eva Dessing1,2
1Centre for Human Drug Research, Leiden, The Netherlands.
Insights
This study developed a smartphone app to automatically count pediatric coughs, offering a noninvasive digital biomarker for children's lung disease monitoring and clinical trials.
Area of Science:
- Biomedical Engineering
- Digital Health
- Pediatric Pulmonology
Background:
- Coughing is a key symptom in pediatric lung diseases, with frequency often indicating disease activity.
- Automated cough detection can serve as a noninvasive digital biomarker for pediatric clinical trials and care.
- Objective and automatic counting of pediatric cough sounds is needed.
Purpose of the Study:
- To develop a smartphone-based algorithm for objective and automatic counting of pediatric cough sounds.
- To create a tool for noninvasive monitoring of pediatric lung disease activity.
- To enable a digital endpoint for pediatric clinical trials.
Main Methods:
- A Gradient Boost Classifier was trained on 3228 pediatric cough sounds and 480,780 non-cough sounds.
- The algorithm was validated on recordings from 14 pediatric patients (aged 0-14) with respiratory disease.
- Algorithm robustness was tested under various conditions, and performance was evaluated at different distances.
Main Results:
- The algorithm achieved 99.7% accuracy, 47.6% sensitivity, and 99.96% specificity.
- A high correlation (0.97) was found between manual and automated cough counts.
- Adequate intra- and inter-device reliability was demonstrated, with optimal performance at 0.5-1m distance.
Conclusions:
- A novel smartphone-based application for pediatric cough detection has been developed.
- This tool can be utilized for longitudinal follow-up in pediatric clinical care.
- The application serves as a potential digital endpoint for clinical trials in pediatric respiratory diseases.
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.
