Ph.D. Research Work Description
Basic research objectives:
Respiratory Sound-based Respiratory Health Analysis:
Adventitious respiratory sound classification
Pulmonary disorder detection using respiratory sounds
Noise-robust algorithm development for true respiratory sound extraction
Respiratory phase extraction using lung sound, other multimodal data
Real-time standalone device development for respiratory health monitoring and screening
Below the graphical abstract provides a basic overview of the research problem
Pulmonary disorder detection using respiratory sounds
A. Roy and U. Satija, "RDLINet: A Novel Lightweight Inception Network for Respiratory Disease Classification Using Lung Sounds," in IEEE Transactions on Instrumentation and Measurement (Impact factor: 5.6, Q1, h5 index: 83), vol. 72, pp. 1-13, 2023, Art no. 4008813, DOI: 10.1109/TIM.2023.3292953.
COPD severity classification using respiratory sounds
A. Roy and U. Satija, "A Novel Melspectrogram Snippet Representation Learning Framework For Severity Detection of Chronic Obstructive Pulmonary Diseases," in IEEE Transactions on Instrumentation and Measurement (Impact factor: 5.6, Q1, h5 index: 83), vol. 72, pp. 1-11, 2023, Art no. 4003311, DOI: 10.1109/TIM.2023.3256468.
GitHub Repository QR
Asthma identification using respiratory sounds using contrastive supervised embedding learning framework: AsthmaSCELNet
A. Roy, U. Satija, "AsthmaSCELNet: A Lightweight Supervised Contrastive Embedding Learning Framework For Asthma Classification Using Lung Sounds", Proc. INTERSPEECH (Core-A*, h5 index: 107), Dublin, Ireland, 2023, 5431-5435, doi: 10.21437/Interspeech.2023-428.
Asthma identification using respiratory sounds using time-frequency self-operational neural network: AsTFSONN:
