  Effective and powerful methods for denoising real electrocardiogram (ECG)
signals are important for wearable sensors and devices. Deep Learning (DL)
models have been used extensively in image processing and other domains with
great success but only very recently have been used in processing ECG signals.
This paper presents several DL models namely Convolutional Neural Networks
(CNNs), Long Short-Term Memory (LSTM), Restricted Boltzmann Machine (RBM)
together with the more conventional filtering methods (low pass filtering, high
pass filtering, Notch filtering) and the standard wavelet-based technique for
denoising EEG signals. These methods are trained, tested and evaluated on
different synthetic and real ECG datasets taken from the MIT PhysioNet database
and for different simulation conditions (i.e. various lengths of the ECG
signals, single or multiple records). The results show the CNN model is a
performant model that can be used for off-line denoising ECG applications where
it is satisfactory to train on a clean part of an ECG signal from an ECG
record, and then to test on the same ECG signal, which would have some high
level of noise added to it. However, for real-time applications or near-real
time applications, this task becomes more cumbersome, as the clean part of an
ECG signal is very probable to be very limited in size. Therefore the solution
put forth in this work is to train a CNN model on 1 second ECG noisy artificial
multiple heartbeat data (i.e. ECG at effort), which was generated in a first
instance based on few sequences of real signal heartbeat ECG data (i.e. ECG at
rest). Afterwards it would be possible to use the trained CNN model in real
life situations to denoise the ECG signal.
