Hi everyone, I am Xuhai. It's my great pleasure to present our work: Listen2Cough - Leveraging Deep
Learning Cough Detection Model to Enhance Lung Health Assessment Using Passively Sensed Audio
Nowadays, mobile phones and wearable devices are carried by users almost anywhere at any time.
This provides an unprecedented chance for health condition assessment and monitoring.
Among various physical health conditions, lung health is one of the major leading
causes of morbidity and mortality, and cough is one of most common symptoms.
There have been rich studies about audio-based cough detection algorithms.
However, they usually focus on a specific population,
without addressing the generalizability of the cough detection model.
Moreover, detecting cough is the first step towards passive lung health assessment.
The cough sounds contain rich information that reflects the condition of the lung. Recently,
researchers start to investigate how to use cough sounds for various lung health assessment tasks.
However, due to the difficulties of collecting ground truth labels, previous studies usually
had a limited dataset and could only resort to traditional machine learning models.
To mitigate this issue, we observe the richness of the cough data, and propose a new method to
transfer the knowledge obtained from the cough detection task to the lung health assessment
tasks so that existing large-scale cough sound datasets can be leveraged effectively.
We have two focuses in the paper: a generalized cough detection model,
and lung health detection models. We started with cough detection.
We use an end-to-end deep learning model that takes raw audio signals as input.
We combine a few 1D CNN layers and a pre-trained MobileNet to construct the model. Moreover,
we leverage various augmentation techniques to enhance the generalizability of our model.
We take advantage of a number of public datasets for data augmentation and model training.
We then collect our own data in two studies during
patients' regular visits to a local hospital for real-life evaluation.
The first study is a controlled clinical study in a quiet environment. We recruited
participants for a series of tasks such as breathing, reading, and voluntary coughing.
The second study was conducted during uncontrolled clinical visits and contained
a wide range of natural noise. Another group of participants was recruited with similar tasks.
Moreover, in the second study, we also collected participants' lung health condition information.
Compared to the state-of-the-art, our model shows better performance on both datasets,
The advantage of precision is significant,
which indicates that our method is more robust to noises.
Then, we move on to the lung health detection task.
We employ a multi-instance learning framework with the attention mechanism,
and build new model branch from the cough detection task for lung health assessment tasks.
We evaluate our method on the second dataset and compare it against the common feature engineering
method on three classification tasks: healthy \vs unhealthy, obstructive \vs non-obstructive,
and COPD \vs asthma classifications. Our models outperform the baseline on all three tasks.
In addition, the attention weights in the multi-instance learning framework imply the
relative importance of a user's cough sounds for each task. They can provide interpretability and
identify a small set of cough sounds that may help experts for further diagnosis.
As a case study, we randomly picked two subjects that were correctly classified
in the healthy \vs unhealthy task.
Overall, the majority of the weights are close to
zero and only a small number of the cough snippets have weights.
The two spectrograms in the middle show the cough with the highest weights,
and the two on the right show the coughs with the lowest weights.
For the unhealthy subject at the top, the two cough sounds were quite different.
The cough with the highest weight had an obvious short interruption.
In contrast, for the healthy subject, the two coughs are similar.
This indicates that for the subject who had severe lung health issues,
the model might be able to select the coughs that were potentially more related to the symptoms.
We aim to collaborate with medical experts
to explore the potential of using our method to assist diagnosis.
To summarize, in this paper.
We first built an end-to-end cough detection model by leveraging pre-trained MobileNet and
various audio augmentation techniques. Our model outperforms the baseline models significantly.
Then, we propose to transfer the cough detection task to lung health assessment tasks.
Using a multi-instance learning model with the attention mechanism,
our new models outperform baselines on three assessment tasks.
Finally, we further leverage the attention weights to identify
the important cough sounds highly correlated with a lung health condition,
which can potentially provide further interpretability during diagnosis.
With that, I'd like to end my talk. Thanks to my great team for doing the awesome work
together. And thank you all for listening. I am happy to answer any questions.