Removal of Motion Artifacts from ECG signals by Combination of Recurrent
Neural Networks and Deep Neural Networks
Electrocardiogram (ECG) is the graphical portrayal of heart usefulness.
The ECG signals holds its significance in the discovery of heart
irregularities. These ECG signals are frequently tainted by antiques
from various sources. It is basic to diminish these curios and improve
the exactness just as dependability to show signs of improvement results
identified with heart usefulness. The most commonly disturbed artifact
in ECG signals is Motion Artifacts (MA). In this paper, we have proposed
a new concept on how machine learning algorithms can be used for
de-noising the ECG signals. Towards the goal, a unique combination of
Recurrent Neural Network (RNN) and Deep Neural Network (DNN) is used to
efficiently remove MA. The proposed algorithm is validated using ECG
records obtained from the MIT-BIH Arrhythmia Database. To eliminate MA
using the proposed method, we have used Adam optimization algorithm to
train and fit the contaminated ECG data in RNN and DNN models.
Performance evaluation results in terms of SNR and RRMSE show that the
proposed algorithm outperforms other existing MA removal methods without
significantly distorting the morphologies of ECG signals.