Learning Disentangled Representations

Funding sources: NIH/NHLBI R15HL140500 (PI: Linwei Wang)
Collaborators: Dr. John L Sapp, QEII Health Sciences Centre, Halifax, NS, Canada 

Confounding factors are inherent in most data analyses, especially those of clinical data. For example, the 12-lead ECG data are generated by a large variety of physiological factors: some pertinent to the diagnosis and treatment of arrhythmia such as rhythm types and the location at which the rhythm originates, while others representing inter-subject variations due to thorax anatomy, heart anatomy and structural remodeling, surface lead positioning, signal artifacts, etc.

To properly disentangle these generative factors from ECG data is critical for automating ECG-based clinical tasks.

We are interested in the development of deep representation learning methods that are able to separate these inter-subject variations from clinical data, and are able to transfer variations learnt from one (larger) dataset (such as a simulation dataset), to another (smaller) dataset (such as a clinical dataset). We are also interested in clinical applications -- in an NIH funded project, we work with clinicians to develop a deep-learning based software tool that is able to guide clinicians progressively closer towards the surgical target in real time during the procedure.