Information Theory in Deep Learning and Signal Processing for Biomedical Signal Analysis

Dear Colleagues,

Biomedical signals are time series collected by means of technologies that can capture the effects of the organism’s functioning. The activity of biological tissuesmay involve electrical, magnetic, electromagnetic, and biochemical phenomena that can be detected through appropriate techniques. 

Electroencephalography (EEG), electrocorticography (ECoG), magnetoencephalography (MEG), and invasive single neuron recording are some examples of the currently available methods that allow investigation of the complex behavior of the brain. The activity of the heart can be investigated through electrocardiography (ECG) and that of the muscles through electromyography (EMG).

The analysis and interpretation of the aforementioned signals must take into accountknowledge about the physiology of the tissues under examination. The method’s design requires the consideration of the specific characteristics of the phenomenon that we aim to analyze and that are relevant to the goal of the study. 

Entropy, and information theory in general, has been applied many times to the analysis of biomedical signals, since randomness and complexity are often crucial characteristics in the functioning of the human body. In this context, the recent developments of machine-learning methods, information theoretical learning, and deep neural networks, in particular, have drawn the attention of researchers in the field biomedical signal processing.

We believe that the combination of information theory and machine learning can make a decisive contribution to biomedical signal analysis at the feature engineering level, in the determination of significant features for classification; at the learning algorithm level, in the definition of information-theoretical-based learning algorithms; and at the postprocessing level, in the interpretation of the physiological phenomena that generated the processed signals.

This Special Issue aims to attract significant contributions in this context, with the aim of highlighting the potential of the combination of information theory and machine learning in the field of biomedical signal analysis.

Dr. Nadia Mammone
Dr. Juan Pablo Amezquita-Sanchez
Dr. Yiwen Wang
Guest Editors

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