Biosignals learning and synthesis using deep neural networks

David Belo,João R Vaz,Hugo Gamboa,João Rodrigues,Pedro Pezarat-Correia

doi:10.1186/s12938-017-0405-0

Abstract

BackgroundModeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. This research could lead the creation of novel algorithms for signal reconstruction in heavily noisy data and source detection in biomedical engineering field.MethodThe present work explores the gated recurrent units (GRU) employed in the training of respiration (RESP), electromyograms (EMG) and electrocardiograms (ECG). Each signal is pre-processed, segmented and quantized in a specific number of classes, corresponding to the amplitude of each sample and fed to the model, which is composed by an embedded matrix, three GRU blocks and a softmax function. This network is trained by adjusting its internal parameters, acquiring the representation of the abstract notion of the next value based on the previous ones. The simulated signal was generated by forecasting a random value and re-feeding itself.Results and conclusionsThe resulting generated signals are similar with the morphological expression of the originals. During the learning process, after a set of iterations, the model starts to grasp the basic morphological characteristics of the signal and later their cyclic characteristics. After training, these models’ prediction are closer to the signals that trained them, specially the RESP and ECG. This synthesis mechanism has shown relevant results that inspire the use to characterize signals from other physiological sources.

Highlights

Modeling physiological signals is a complex task both for understand‐ ing and synthesize biomedical signals
This paper proposes the application of a deep neural networks (DNN) to accurately synthesize the morphologies of a biosignal
The DNN architecture is a fundamental key in this study, since it can learn from the morphology itself, not requiring the input of more features nor the compatibility for one specific signal, unlike other methods existent in the bibliography

Summary

Introduction

Modeling physiological signals is a complex task both for understand‐ ing and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. The hypothesis is that if the created models are capable of generating clean signals, apart from replacing the unrecognizable signals due to contamination of noise, but they could evaluate the distinction between types of signals and, if the signal topology permits, its source. This capacity will be able to unlock novel algorithms, for signal denoising and reconstruction, and for. The DNN architecture is a fundamental key in this study, since it can learn from the morphology itself, not requiring the input of more features nor the compatibility for one specific signal, unlike other methods existent in the bibliography

Methods

Results

Discussion

Conclusion