# DeepCough: A Deep Convolutional Neural Network in A Wearable Cough Detection System

arXiv: https://arxiv.org/abs/1509.02512 · HTML: https://mlsanity.com/paper/1509.02512/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2015.

## Built on by (most cited first, 8 of 8)

- [Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification](https://mlsanity.com/paper/2105.07566/) (2021)
- [Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey](https://mlsanity.com/paper/2309.14383/) (2023)
- [Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People's Cough Detection Models](https://mlsanity.com/paper/2311.06707/) (2023)
- [Automatic non-invasive Cough Detection based on Accelerometer and Audio Signals](https://mlsanity.com/paper/2109.00103/) (2021)
- [Deep Neural Network Based Respiratory Pathology Classification Using Cough Sounds](https://mlsanity.com/paper/2106.12174/) (2021)
- [Accelerometer-based Bed Occupancy Detection for Automatic, Non-invasive Long-term Cough Monitoring](https://mlsanity.com/paper/2202.03936/) (2022)
- [XAI-Driven Spectral Analysis of Cough Sounds for Respiratory Disease Characterization](https://mlsanity.com/paper/2508.14949/) (2025)
- [A XAI-based Framework for Frequency Subband Characterization of Cough Spectrograms in Chronic Respiratory Disease](https://mlsanity.com/paper/2508.16237/) (2025)

## Abstract (arXiv)

In this paper, we present a system that employs a wearable acoustic sensor and a deep convolutional neural network for detecting coughs. We evaluate the performance of our system on 14 healthy volunteers and compare it to that of other cough detection systems that have been reported in the literature. Experimental results show that our system achieves a classification sensitivity of 95.1% and a specificity of 99.5%.