Coupled Analysis Dictionary Learning to inductively learn inversion: Application to real-time reconstruction of Biomedical signals

TL;DR

This paper introduces a novel inductive learning method using Coupled Analysis Dictionary Learning for real-time biomedical signal reconstruction, outperforming traditional compressed sensing and deep autoencoder approaches.

Contribution

It proposes a new inductive learning framework based on Coupled Analysis Dictionary Learning for faster and more accurate biomedical signal inversion.

Findings

Faster reconstruction speed compared to CS and SSDAE.

Significantly improved reconstruction accuracy.

Effective for real-time biomedical applications.

Abstract

This work addresses the problem of reconstructing biomedical signals from their lower dimensional projections. Traditionally Compressed Sensing (CS) based techniques have been employed for this task. These are transductive inversion processes, the problem with these approaches is that the inversion is time-consuming and hence not suitable for real-time applications. With the recent advent of deep learning, Stacked Sparse Denoising Autoencoder (SSDAE) has been used for learning inversion in an inductive setup. The training period for inductive learning is large but is very fast during application -- capable of real-time speed. This work proposes a new approach for inductive learning of the inversion process. It is based on Coupled Analysis Dictionary Learning. Results on Biomedical signal reconstruction show that our proposed approach is very fast and yields result far better than CS and SSDAE.