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

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A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 37198

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Special Issue Editors

Dr. Nadia Mammone

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DICEAM Department, Mediterranea University of Reggio Calabria, Via Graziella Feo di Vito, 89060 Reggio Calabria, Italy
Interests: biomedical signal processing; neuroimaging; machine/deep learning techniques; brain computer interface

Dr. Juan Pablo Amezquita-Sanchez

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Guest Editor

Faculty of Engineering, Autonomous University of Queretaro, Campus San Juan del Rio, Rio Moctezuma 249, Col. San Cayetano, 76805, San Juan del Rio 76805, Mexico
Interests: machine learning; nonlinear methods; deep learning; electroencephalography; electrocardiography

Dr. Yiwen Wang

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Guest Editor

Hong Kong University of Science and Technology, Kowloon 999077, Hong Kong, China
Interests: brain–machine interfaces; adaptive filtering; information theoretical learning; neuromorphic engineering

Special Issue Information

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

Manuscript Submission Information

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Keywords

multivariate, multiscale entropy analysis
information theoretical learning
complex network analysis
deep learning
brain–computer interface
physiological signal processing (EEG, MEG, ECG, EMG, etc.)
medical image processing (CT, MRI, PET, SPECT, etc.)

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Published Papers (7 papers)

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Research

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12 pages, 3747 KiB

Open AccessArticle

Increased Entropic Brain Dynamics during DeepDream-Induced Altered Perceptual Phenomenology

by Antonino Greco, Giuseppe Gallitto, Marco D’Alessandro and Clara Rastelli

Entropy 2021, 23(7), 839; https://doi.org/10.3390/e23070839 - 30 Jun 2021

Cited by 4 | Viewed by 3884

Abstract

In recent years, the use of psychedelic drugs to study brain dynamics has flourished due to the unique opportunity they offer to investigate the neural mechanisms of conscious perception. Unfortunately, there are many difficulties to conduct experiments on pharmacologically-induced hallucinations, especially regarding ethical and legal issues. In addition, it is difficult to isolate the neural effects of psychedelic states from other physiological effects elicited by the drug ingestion. Here, we used the DeepDream algorithm to create visual stimuli that mimic the perception of hallucinatory states. Participants were first exposed to a regular video, followed by its modified version, while recording electroencephalography (EEG). Results showed that the frontal region’s activity was characterized by a higher entropy and lower complexity during the modified video, with respect to the regular one, at different time scales. Moreover, we found an increased undirected connectivity and a greater level of entropy in functional connectivity networks elicited by the modified video. These findings suggest that DeepDream and psychedelic drugs induced similar altered brain patterns and demonstrate the potential of adopting this method to study altered perceptual phenomenology in neuroimaging research. Full article

(This article belongs to the Special Issue Information Theory in Deep Learning and Signal Processing for Biomedical Signal Analysis)

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22 pages, 541 KiB

Open AccessArticle

A Nonlinear Maximum Correntropy Information Filter for High-Dimensional Neural Decoding

by Xi Liu, Shuhang Chen, Xiang Shen, Xiang Zhang and Yiwen Wang

Entropy 2021, 23(6), 743; https://doi.org/10.3390/e23060743 - 12 Jun 2021

Cited by 6 | Viewed by 3033

Abstract

Neural signal decoding is a critical technology in brain machine interface (BMI) to interpret movement intention from multi-neural activity collected from paralyzed patients. As a commonly-used decoding algorithm, the Kalman filter is often applied to derive the movement states from high-dimensional neural firing observation. However, its performance is limited and less effective for noisy nonlinear neural systems with high-dimensional measurements. In this paper, we propose a nonlinear maximum correntropy information filter, aiming at better state estimation in the filtering process for a noisy high-dimensional measurement system. We reconstruct the measurement model between the high-dimensional measurements and low-dimensional states using the neural network, and derive the state estimation using the correntropy criterion to cope with the non-Gaussian noise and eliminate large initial uncertainty. Moreover, analyses of convergence and robustness are given. The effectiveness of the proposed algorithm is evaluated by applying it on multiple segments of neural spiking data from two rats to interpret the movement states when the subjects perform a two-lever discrimination task. Our results demonstrate better and more robust state estimation performance when compared with other filters. Full article

(This article belongs to the Special Issue Information Theory in Deep Learning and Signal Processing for Biomedical Signal Analysis)

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17 pages, 1385 KiB

Open AccessArticle

Training Data Selection and Optimal Sensor Placement for Deep-Learning-Based Sparse Inertial Sensor Human Posture Reconstruction

by Zhaolong Zheng, Hao Ma, Weichao Yan, Haoyang Liu and Zaiyue Yang

Entropy 2021, 23(5), 588; https://doi.org/10.3390/e23050588 - 10 May 2021

Cited by 10 | Viewed by 3033

Abstract

Although commercial motion-capture systems have been widely used in various applications, the complex setup limits their application scenarios for ordinary consumers. To overcome the drawbacks of wearability, human posture reconstruction based on a few wearable sensors have been actively studied in recent years. In this paper, we propose a deep-learning-based sparse inertial sensor human posture reconstruction method. This method uses bidirectional recurrent neural network (Bi-RNN) to build an a priori model from a large motion dataset to build human motion, thereby the low-dimensional motion measurements are mapped to whole-body posture. To improve the motion reconstruction performance for specific application scenarios, two fundamental problems in the model construction are investigated: training data selection and sparse sensor placement. The problem of deep-learning training data selection is to select independent and identically distributed (IID) data for a certain scenario from the accumulated imbalanced motion dataset with sufficient information. We formulate the data selection into an optimization problem to obtain continuous and IID data segments, which comply with a small reference dataset collected from the target scenario. A two-step heuristic algorithm is proposed to solve the data selection problem. On the other hand, the optimal sensor placement problem is studied to exploit most information from partial observation of human movement. A method for evaluating the motion information amount of any group of wearable inertial sensors based on mutual information is proposed, and a greedy searching method is adopted to obtain the approximate optimal sensor placement of a given sensor number, so that the maximum motion information and minimum redundancy is achieved. Finally, the human posture reconstruction performance is evaluated with different training data and sensor placement selection methods, and experimental results show that the proposed method takes advantages in both posture reconstruction accuracy and model training time. In the 6 sensors configuration, the posture reconstruction errors of our model for walking, running, and playing basketball are 7.25°, 8.84°, and 14.13°, respectively. Full article

(This article belongs to the Special Issue Information Theory in Deep Learning and Signal Processing for Biomedical Signal Analysis)

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17 pages, 6355 KiB

Open AccessArticle

Research on Channel Selection and Multi-Feature Fusion of EEG Signals for Mental Fatigue Detection

by Quan Liu, Yang Liu, Kun Chen, Lei Wang, Zhilei Li, Qingsong Ai and Li Ma

Entropy 2021, 23(4), 457; https://doi.org/10.3390/e23040457 - 13 Apr 2021

Cited by 22 | Viewed by 3288

Abstract

With the rapid development of modern social science and technology, the pace of life is getting faster, and brain fatigue has become a sub-health state that seriously affects the normal life of people. Electroencephalogram (EEG) signals reflect changes in the central nervous system. Using EEG signals to assess mental fatigue is a research hotspot in related fields. Most existing fatigue detection methods are time-consuming or don’t achieve satisfactory results due to insufficient features extracted from EEG signals. In this paper, a 2-back task is designed to induce fatigue. The weight value of each channel under a single feature is calculated by ReliefF algorithm. The classification accuracy of each channel under the corresponding features is analyzed. The classification accuracy of each single channel is combined to perform weighted summation to obtain the weight value of each channel. The first half channels sorted in descending order based on the weight value is chosen as the common channels. Multi-features in frequency and time domains are extracted from the common channel data, and the sparse representation method is used to perform feature fusion to obtain sparse fused features. Finally, the SRDA classifier is used to detect the fatigue state. Experimental results show that the proposed methods in our work effectively reduce the number of channels for computation and also improve the mental fatigue detection accuracy. Full article

(This article belongs to the Special Issue Information Theory in Deep Learning and Signal Processing for Biomedical Signal Analysis)

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10 pages, 391 KiB

Open AccessArticle

Novel Convolutional Neural Network with Variational Information Bottleneck for P300 Detection

by Hongpeng Liao, Jianwu Xu and Zhuliang Yu

Entropy 2021, 23(1), 39; https://doi.org/10.3390/e23010039 - 29 Dec 2020

Cited by 1 | Viewed by 2881

Abstract

In the area of brain-computer interfaces (BCI), the detection of P300 is a very important technique and has a lot of applications. Although this problem has been studied for decades, it is still a tough problem in electroencephalography (EEG) signal processing owing to its high dimension features and low signal-to-noise ratio (SNR). Recently, neural networks, like conventional neural networks (CNN), has shown excellent performance on many applications. However, standard convolutional neural networks suffer from performance degradation on dealing with noisy data or data with too many redundant information. In this paper, we proposed a novel convolutional neural network with variational information bottleneck for P300 detection. Wiht the CNN architecture and information bottleneck, the proposed network termed P300-VIB-Net could remove the redundant information in data effectively. The experimental results on BCI competition data sets show that P300-VIB-Net achieves cutting-edge character recognition performance. Furthermore, the proposed model is capable of restricting the flow of irrelevant information adaptively in the network from perspective of information theory. The experimental results show that P300-VIB-Net is a promising tool for P300 detection. Full article

(This article belongs to the Special Issue Information Theory in Deep Learning and Signal Processing for Biomedical Signal Analysis)

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10 pages, 1054 KiB

Open AccessArticle

Separated Channel Attention Convolutional Neural Network (SC-CNN-Attention) to Identify ADHD in Multi-Site Rs-fMRI Dataset

by Tao Zhang, Cunbo Li, Peiyang Li, Yueheng Peng, Xiaodong Kang, Chenyang Jiang, Fali Li, Xuyang Zhu, Dezhong Yao, Bharat Biswal and Peng Xu

Entropy 2020, 22(8), 893; https://doi.org/10.3390/e22080893 - 14 Aug 2020

Cited by 50 | Viewed by 9893

Abstract

The accurate identification of an attention deficit hyperactivity disorder (ADHD) subject has remained a challenge for both neuroscience research and clinical diagnosis. Unfortunately, the traditional methods concerning the classification model and feature extraction usually depend on the single-channel model and static measurements (i.e., functional connectivity, FC) in the small, homogenous single-site dataset, which is limited and may cause the loss of intrinsic information in functional MRI (fMRI). In this study, we proposed a new two-stage network structure by combing a separated channel convolutional neural network (SC-CNN) with an attention-based network (SC-CNN-attention) to discriminate ADHD and healthy controls on a large-scale multi-site database (5 sites and n = 1019). To utilize both intrinsic temporal feature and the interactions of temporal dependent in whole-brain resting-state fMRI, in the first stage of our proposed network structure, a SC- CNN is used to learn the temporal feature of each brain region, and an attention network in the second stage is adopted to capture temporal dependent features among regions and extract fusion features. Using a “leave-one-site-out” cross-validation framework, our proposed method obtained a mean classification accuracy of 68.6% on five different sites, which is higher than those reported in previous studies. The classification results demonstrate that our proposed network is robust to data variants and is also replicated across sites. The combination of the SC-CNN with the attention network is powerful to capture the intrinsic fMRI information to discriminate ADHD across multi-site resting-state fMRI data. Full article

(This article belongs to the Special Issue Information Theory in Deep Learning and Signal Processing for Biomedical Signal Analysis)

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Review

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18 pages, 2135 KiB

Open AccessReview

Deep Learning Methods for Heart Sounds Classification: A Systematic Review

by Wei Chen, Qiang Sun, Xiaomin Chen, Gangcai Xie, Huiqun Wu and Chen Xu

Entropy 2021, 23(6), 667; https://doi.org/10.3390/e23060667 - 26 May 2021

Cited by 92 | Viewed by 9902

Abstract

The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study. Full article

(This article belongs to the Special Issue Information Theory in Deep Learning and Signal Processing for Biomedical Signal Analysis)

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