  Currently, the telehealth monitoring field has gained huge attention due to
its noteworthy use in day-to-day life. This advancement has led to an increase
in the data collection of electrophysiological signals. Due to this
advancement, electrocardiogram (ECG) signal monitoring has become a leading
task in the medical field. ECG plays an important role in the medical field by
analysing cardiac physiology and abnormalities. However, these signals are
affected due to numerous varieties of noises, such as electrode motion,
baseline wander and white noise etc., which affects the diagnosis accuracy.
Therefore, filtering ECG signals became an important task. Currently, deep
learning schemes are widely employed in signal-filtering tasks due to their
efficient architecture of feature learning. This work presents a deep
learning-based scheme for ECG signal filtering, which is based on the deep
autoencoder module. According to this scheme, the data is processed through the
encoder and decoder layer to reconstruct by eliminating noises. The proposed
deep learning architecture uses a modified ReLU function to improve the
learning of attributes because standard ReLU cannot adapt to huge variations.
Further, a skip connection is also incorporated in the proposed architecture,
which retains the key feature of the encoder layer while mapping these features
to the decoder layer. Similarly, an attention model is also included, which
performs channel and spatial attention, which generates the robust map by using
channel and average pooling operations, resulting in improving the learning
performance. The proposed approach is tested on a publicly available MIT-BIH
dataset where different types of noise, such as electrode motion, baseline
water and motion artifacts, are added to the original signal at varied SNR
levels.
