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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 resultsshow 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. This corresponds also to reality, where usually the human is put at rest and the ECG is recorded and then the same human is asked to do some physical exercises and the ECG is recorded at effort. The quality of results is assessed visually but also by using the Root Mean Squared (RMS) and the Signal to Noise Ratio (SNR) measures. All CNN models used an NVIDIA TITAN V Graphical Processing Unit (GPU) with 12 GB RAM, which reduces drastically the computational times. Finally, as an element of novelty, the paper presents also a Design of Experiment (DoE) study which intends to determine the optimal structure of a CNN model, which type of study has not been seen in the literature before.

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We present here an unsupervised approach to learning suitable features for a deep learning framework applied to image classification. PCANet was introduced as a simple and efficient baseline for deep learning approaches which used cascaded principle component analysis (PCA) derived filter banks, as well as other simple image processing elements such as binary hashing and blockwise histograms. This was followed by DCTNet which used discrete cosine transform (DCT) filter banks as a learning-free alternative. In this paper we propose SOMNet which uses self-organizing map (SOM) based filters offering a non-orthogonal alternative to PCANet providing comparable performance. It is well established that SOM is a non-linear version of PCA but does not suffer from the same constraints. We also show that through the use of a simple trick in the binarization process results in a dramatic reduction in the dimension of the final feature vector, thus allowing the utilization of more filters which could lead to deeper and more complex structures in further work. We also demonstrate the results of a hybrid methodology that clusters generative Markov random fields (MRF) as filters which provides more diverse features in a data driven approach to deep learning. Index Terms-Self-organizing maps, unsupervised learning, Markov random fields, convolutional neural network, deep learning, handwritten digit recognition.

In this selection from Abuse of Drugs: Information and Resource, a handbook for teachers and other professionals concerned with drug abuse, the authors attempt to create a frame of reference which will lead to a better understanding of some of the rather complex personal and institutional factors that may be related to the decisions of many individuals to become involved with drugs of abuse.