Research area
Sleep Apnea & Respiratory AI
Learning from snoring, tracheal audio and physiological signals to detect respiratory events and support sleep-apnea screening.
Signals
SnoringTracheal audioAirflowSpO₂ECGPPGThoraxAbdomen
Models
CNNsTransformersWav2Vec2WhisperWavLM
Related projects
Multimodal Sleep Apnea Screening
A research pipeline that combines respiratory acoustics and physiological dynamics for OSA screening.
Full-night Tracheal Audio Event Modelling
Full-night acoustic representation learning for localising apnea–hypopnea events and estimating burden.
Related publications
Himanshu Sharma, Pradip K. Das
The 19th International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2026) held in Marbella, Spain. [ Conference Rank - B4]2026Published
Himanshu Sharma, Subash Chapa, Pradip K. Das
IEEE HealthCom 2026 - IEEE International Conference on E-health Networking, Application & Services New York, New York, USA [ ICORE : RANK C]2026Accepted
H. Sharma, P. K. Das
Springer Nature Computer Science Journal2026Submitted
Himanshu Sharma, Pradip K. Das
ICSTE 2023 - International Conference on Science, Technology and Engineering2023Published