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
SnoreFlowNet: Snore-Intensity and Airflow Dynamics Along with Cardiovascular Modalities for Deep Learning Based Sleep Apnea Detection
Bibliographic details pending final verification
TrachEventNet: Full-Night Tracheal Audio Modeling with Self-Supervised Representations for Apnea–Hypopnea Event Localization and Event-Burden Estimation
Bibliographic details pending final verification
A Review on Speech as Biomarker for Obstructive Sleep Apnea (OSA)
Bibliographic details pending final verification