RunDAE model: Running denoising autoencoder models for denoising ECG signals.

Background: The denoising autoencoder (DAE) is commonly used to denoise bio-signals such as electrocardiogram (ECG) signals through dimensional reduction. Typically, the DAE model needs to be trained using correlated input segments such as QRS-aligned segments or long ECG segments. However, using long ECG segments as an input can result in a complex deep DAE model that requires many hidden layers to achieve a low-dimensional representation, which is a major drawback.

Multiple ECG segments denoising autoencoder model.

Objectives: Denoising autoencoder (DAE) with a single hidden layer of neurons can recode a signal, i.e., converting the original signal into a noise-reduced signal.