Deep Neural Oracles for Short-window Optimized Compressed Sensing of
Biosignals
- Mauro Mangia ,
- Luciano Prono ,
- Fabio Pareschi ,
- Riccardo Rovatti ,
- Gianluca Setti
Abstract
The recovery of sparse signals given their linear mapping on
lower-dimensional spaces can be partitioned into a support estimation
phase and a coefficient estimation phase. We propose to estimate the
support with an oracle based on a deep neural network trained jointly
with the linear mapping at the encoder. The divination of the oracle is
then used to estimate the coefficients by pseudo-inversion. This
architecture allows the definition of an encoding-decoding scheme with
state-of-the-art recovery capabilities when applied to biological
signals such as ECG and EEG, thus allowing extremely low-complex
encoders.
As an additional feature, oracle-based recovery is able to self-assess,
by indicating with remarkable accuracy chunks of signals that may have
been reconstructed with a non-satisfactory quality. This self-assessment
capability is unique in the CS literature and paves the way for further
improvements depending on the requirements of the specific application.
As an example, our scheme is able to satisfyingly compress by a factor
of 2.67 an ECG or EEG signal with a complexity equivalent to only 24
signed sums per processed sample.