An Efficient and Private ECG Classification System Using Split and Semi-Supervised Learning.

Electrocardiography (ECG) is a standard diagnostic tool for evaluating the overall heart's electrical activity and is vital for detecting many cardiovascular diseases. Classifying ECG recordings using deep neural networks has been investigated in literature and has shown very good performance. However, this performance assumes that the training data is centralized, which is often not the case in real-life scenarios, where data resides in multiple places and only a small portion of it is labeled.