  We propose a novel deep learning based denoising filter selection algorithm
for noisy Electrocardiograph (ECG) signal preprocessing. ECG signals measured
under clinical conditions, such as those acquired using skin contact devices in
hospitals, often contain baseline signal disturbances and unwanted artefacts;
indeed for signals obtained outside of a clinical environment, such as heart
rate signatures recorded using non-contact radar systems, the measurements
contain greater levels of noise than those acquired under clinical conditions.
In this paper, we focus on heart rate signals acquired using non-contact radar
systems for use in assisted living environments. Such signals contain more
noise than those measured under clinical conditions and thus require a novel
signal noise removal method capable of adaptive determining filters. Currently,
the most common method of removing noise from such a waveform is through the
use of filters; the most popular filtering method amongst which is the wavelet
filter. There are, however, circumstances in which using a different filtering
method may result in higher signal-to-noise-ratios (SNR) for a waveform; in
this paper, we investigate the wavelet and elliptical filtering methods for the
task of reducing noise in ECG signals acquired using assistive technologies.
Our proposed convolutional neural network architecture classifies (with 92.8%
accuracy) the optimum filtering method for noisy signal based on its expected
SNR value.
