  In this work we propose an autoencoder based framework for simultaneous
reconstruction and classification of biomedical signals. Previously these two
tasks, reconstruction and classification were treated as separate problems.
This is the first work to propose a combined framework to address the issue in
a holistic fashion. Reconstruction techniques for biomedical signals for
tele-monitoring are largely based on compressed sensing (CS) based method,
these are designed techniques where the reconstruction formulation is based on
some assumption regarding the signal. In this work, we propose a new paradigm
for reconstruction we learn to reconstruct. An autoencoder can be trained for
the same. But since the final goal is to analyze classify the signal we learn a
linear classification map inside the autoencoder. The ensuing optimization
problem is solved using the Split Bregman technique. Experiments have been
carried out on reconstruction and classification of ECG arrhythmia
classification and EEG seizure classification signals. Our proposed tool is
capable of operating in a semi-supervised fashion. We show that our proposed
method is better and more than an order magnitude faster in reconstruction than
CS based methods; it is capable of real-time operation. Our method is also
better than recently proposed classification methods. Significance: This is the
first work offering an alternative to CS based reconstruction. It also shows
that representation learning can yield better results than hand-crafted
features for signal analysis.
