Computers in biology and medicine · 2026
On-device cough detection and respiratory disease classification enhanced by generative data augmentation.
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Kontogiannis George, Tzamalis Pantelis, Giannikopoulos Anastases, Nikoletseas Sotiris
Аннотация
BACKGROUND: Cough sounds are accessible, non-invasive biomarkers for respiratory disease assessment and can be captured using consumer-grade smartphones. Existing approaches typically focus solely on cough detection or rely on server-based deep learning for disease classification, which limits deployability and raises privacy concerns. Small, imbalanced cough datasets further hinder model generalization.
OBJECTIVE: To develop a multilayer, smartphone-compatible AI framework for automated cough detection and respiratory disease classification, and to propose a pioneering generative augmentation strategy utilizing a suite of five Variational Autoencoder (VAE) variants and a probabilistic cough-level fusion mechanism to improve disease classification under severe data scarcity and the limitations of conventional audio augmentation techniques.
METHODS: The proposed framework consists of three AI modules: (1) A Cough Detection Module (CDM) that performs real-time cough event detection and segmentation from continuous audio using lightweight models optimized for on-device execution. (2) A Disease Analysis Module (DAM) that classifies cough events into asthma, COVID-19, or healthy classes using parallel Support Vector Machine classifiers and a probabilistic cough-level fusion strategy. (3) A Generative Augmentation Module (GAM) employing five distinct VAE architectures. This module uniquely operates across the time-frequency domain for latent feature optimization while reconstructing samples in the time-domain to ensure acoustic verifiability.
RESULTS: The CDM provides reliable segmentation across heterogeneous recording conditions. The DAM achieves strong discriminability between asthma, COVID-19, and healthy coughs using compact cepstral and spectral features. The multi-variant GAM framework demonstrates superior efficacy in alleviating class imbalance specifically, the cross-domain (time-frequency to time-domain) reconstruction allows for the clinical verification of synthetic biomarkers. All system components operate in real time on commodity Android hardware.
CONCLUSION: This integrated framework addresses key limitations in dataset availability, model generalizability, and deployability, introducing an interpretable generative approach that demonstrates the feasibility of smartphone-based acoustic sensing as a scalable and privacy-preserving tool for respiratory health monitoring.
OBJECTIVE: To develop a multilayer, smartphone-compatible AI framework for automated cough detection and respiratory disease classification, and to propose a pioneering generative augmentation strategy utilizing a suite of five Variational Autoencoder (VAE) variants and a probabilistic cough-level fusion mechanism to improve disease classification under severe data scarcity and the limitations of conventional audio augmentation techniques.
METHODS: The proposed framework consists of three AI modules: (1) A Cough Detection Module (CDM) that performs real-time cough event detection and segmentation from continuous audio using lightweight models optimized for on-device execution. (2) A Disease Analysis Module (DAM) that classifies cough events into asthma, COVID-19, or healthy classes using parallel Support Vector Machine classifiers and a probabilistic cough-level fusion strategy. (3) A Generative Augmentation Module (GAM) employing five distinct VAE architectures. This module uniquely operates across the time-frequency domain for latent feature optimization while reconstructing samples in the time-domain to ensure acoustic verifiability.
RESULTS: The CDM provides reliable segmentation across heterogeneous recording conditions. The DAM achieves strong discriminability between asthma, COVID-19, and healthy coughs using compact cepstral and spectral features. The multi-variant GAM framework demonstrates superior efficacy in alleviating class imbalance specifically, the cross-domain (time-frequency to time-domain) reconstruction allows for the clinical verification of synthetic biomarkers. All system components operate in real time on commodity Android hardware.
CONCLUSION: This integrated framework addresses key limitations in dataset availability, model generalizability, and deployability, introducing an interpretable generative approach that demonstrates the feasibility of smartphone-based acoustic sensing as a scalable and privacy-preserving tool for respiratory health monitoring.