Development of physiology-driven machine learning algorithms for assessing sleep apnea with older adult end-users

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Sleep apnea is a critical health concern affecting older adults, linked to cardiovascular diseases and other risks. Dr. Yadollahi will present her team’s work on developing machine learning algorithms and accessible diagnostic technologies to assess sleep apnea based on signals such as chest movement, heart and respiratory sounds, and snoring—recordable even at home.


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  • Shinshu University, Faculty of Engineering
  • W1 Building Room 115
  • Nagano, Nagano
  • Japan 380-8553
  • Building: W1 Building
  • Room Number: Room 115
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  • Co-sponsored by IEEE EMBS Distinguished Lecturers Program

  Speakers

Azadeh

Topic:

Development of physiology-driven machine learning algorithms for assessing sleep apnea with older adult

This talk introduces a series of physiology-driven machine learning algorithms co-developed with older adult end-users to assess sleep apnea. These algorithms use cardio-respiratory sounds, speech, and snoring to estimate airway characteristics and detect sleep apnea, airflow, and cardiac function — all using signals that can be recorded conveniently at home.

The lecture also discusses ultrasound-based methods and speech acoustics for evaluating pharyngeal airway narrowing and tissue characteristics, aiming to develop accessible diagnostic technologies.

Biography:

Dr. Azadeh Yadollahi is a Canada Research Chair (Tier 2) in Cardiorespiratory Engineering, a Senior Scientist at the Toronto Rehabilitation Institute - KITE, University Health Network (UHN), and an Associate Professor at the University of Toronto’s Institute of Biomedical Engineering. She also holds an adjunct faculty appointment at the University of Manitoba.

She leads the FabrIc-Based REsearch (FIBRE) platform, developing textile-based wearable technologies to improve equitable access to healthcare. At UHN-KITE, she directs the SleepdB lab, which utilizes advanced tools and controlled environments to study cardio-respiratory disorders and develop diagnostic technologies.

Dr. Yadollahi has authored more than 80 peer-reviewed publications, presented over 150 scientific talks, filed several patents, and leads national initiatives to train future healthcare technology innovators.