EMD-based data augmentation method applied to handwriting data for the diagnosis of Essential Tremor using LSTM networks.

The increasing capacity of today's technology represents great advances in diagnosing diseases using standard procedures supported by computer science. Deep learning techniques are able to extract the characteristics of temporal signals to study their patterns and diagnose diseases such as essential tremor. However, these techniques require a large amount of data to train the neural network and achieve good results, and the more data the network has, the more accurate the final model implemented.