| Paper ID | BIO-12.1 |
| Paper Title |
DEEP LUNG AUSCULTATION USING ACOUSTIC BIOMARKERS FOR ABNORMAL RESPIRATORY SOUND EVENT DETECTION |
| Authors |
Upasana Tiwari, Swapnil Bhosale, Rupayan Chakraborty, Sunil Kumar Kopparapu, TCS Research and Innovation, India |
| Session | BIO-12: Feature Extraction and Fusion for Biomedical Applications |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation |
Poster
|
| Topic |
Biomedical Imaging and Signal Processing: [BIO] Biomedical signal processing |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Lung Auscultation is a non-invasive process of distinguishing normal respiratory sounds from abnormal ones by analyzing the airflow along the respiratory tract. With the developments in the deep learning techniques and wider access to anonymized medical data, automatic detection of specific sounds such as crackles and wheezes has been gaining popularity. In this paper, we propose to use two sets of diversified acoustic biomarkers extracted using Discrete Wavelet Transform (DWT) and deep encoded features from the intermediate layer of a pre-trained Audio Event Detection (AED) model trained using sounds from daily activities. First set of biomarkers highlight the time frequency localization characteristics obtained from DWT coefficients. However, the second set of deep encoded biomarkers captures a generalized reliable representation, and thus indemnifies the scarcity of training samples and the class imbalance in dataset. The model trained using these features achieves a 15.05% increase in terms of the specificity over the baseline model that uses spectrogram features. Moreover, ensemble of DWT features and deep encoded feature based models show absolute improvements of 8.32%, 6.66% and 7.40% in terms of sensitivity, specificity and ICBHI-score, respectively, and clearly outperforms the state-of-the-art with a significant margin. |
