Special Issue "Application of Neural Networks in Biosignal Process"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editors

Prof. Dr. Andrzej Czyżewski
Website
Guest Editor
Faculty of Electronics, Telecommunications and Informatics, Multimedia Systems Department, Gdansk University of Technology, 80-233 Gdańsk, Poland
Interests: multimedia systems; sound and vision engineering; signal processing; biomedical engineering; artificial intelligence
Dr. Piotr Szczuko
Website
Guest Editor
Faculty of Electronics, Telecommunications and Informatics, Multimedia Systems Department, Gdansk University of Technology, 80-233 Gdańsk, Poland
Interests: artificial intelligence; smart cities; biometrics; signal processing

Special Issue Information

Dear Colleagues,

Biosignal acquisition and classification is a multistep process which is crucial for better understanding and decision making in human-health-related applications. Researchers deal with multichannel signals such as EEG, 2D signals such as X-Ray images or gaze tracking heat maps and 3D MRI scans. Depending on the nature of the signal, and the applied sensor and its location, such data can be noisy, unstructured, biased or hampered in many ways. Current advancements in neural networks show their great applicability for supervised and unsupervised signal preprocessing and classification. Many phases of the biosignal process can be augmented with the use of ANN, deep learning, and many types of machine-learning-based methods: Signal denoising, unsupervised clustering, dimensionality reduction, latent featurs extraction, classification, and compression are only a few examples of the many possible applications, important for accurate and effective biosignals processing.

This Special Issue focuses on describing use cases of ANN in biosignal analysis, explaining innovative applications and new methods, and showing the benefits of neural networks in key phases of processing of signals or images.

Prof. Dr. Andrzej Czyżewski
Dr. Piotr Szczuko
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Neural networks
  • Deep learning
  • Unsupervised learning
  • Signal preprocessing
  • Feature extraction
  • Signal and image classification

Published Papers (3 papers)

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Research

Open AccessFeature PaperArticle
Temporal Auditory Coding Features for Causal Speech Enhancement
Electronics 2020, 9(10), 1698; https://doi.org/10.3390/electronics9101698 - 16 Oct 2020
Abstract
Perceptually motivated audio signal processing and feature extraction have played a key role in the determination of high-level semantic processes and the development of emerging systems and applications, such as mobile phone telecommunication and hearing aids. In the era of deep learning, speech [...] Read more.
Perceptually motivated audio signal processing and feature extraction have played a key role in the determination of high-level semantic processes and the development of emerging systems and applications, such as mobile phone telecommunication and hearing aids. In the era of deep learning, speech enhancement methods based on neural networks have seen great success, mainly operating on the log-power spectra. Although these approaches surpass the need for exhaustive feature extraction and selection, it is still unclear whether they target the important sound characteristics related to speech perception. In this study, we propose a novel set of auditory-motivated features for single-channel speech enhancement by fusing temporal envelope and temporal fine structure information in the context of vocoder-like processing. A causal gated recurrent unit (GRU) neural network is employed to recover the low-frequency amplitude modulations of speech. Experimental results indicate that the exploited system achieves considerable gains for normal-hearing and hearing-impaired listeners, in terms of objective intelligibility and quality metrics. The proposed auditory-motivated feature set achieved better objective intelligibility results compared to the conventional log-magnitude spectrogram features, while mixed results were observed for simulated listeners with hearing loss. Finally, we demonstrate that the proposed analysis/synthesis framework provides satisfactory reconstruction accuracy of speech signals. Full article
(This article belongs to the Special Issue Application of Neural Networks in Biosignal Process)
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Open AccessEditor’s ChoiceArticle
Automatic ECG Diagnosis Using Convolutional Neural Network
Electronics 2020, 9(6), 951; https://doi.org/10.3390/electronics9060951 - 08 Jun 2020
Abstract
Cardiovascular disease (CVD) is the most common class of chronic and life-threatening diseases and, therefore, considered to be one of the main causes of mortality. The proposed new neural architecture based on the recent popularity of convolutional neural networks (CNN) was a solution [...] Read more.
Cardiovascular disease (CVD) is the most common class of chronic and life-threatening diseases and, therefore, considered to be one of the main causes of mortality. The proposed new neural architecture based on the recent popularity of convolutional neural networks (CNN) was a solution for the development of automatic heart disease diagnosis systems using electrocardiogram (ECG) signals. More specifically, ECG signals were passed directly to a properly trained CNN network. The database consisted of more than 4000 ECG signal instances extracted from outpatient ECG examinations obtained from 47 subjects: 25 males and 22 females. The confusion matrix derived from the testing dataset indicated 99% accuracy for the “normal” class. For the “atrial premature beat” class, ECG segments were correctly classified 100% of the time. Finally, for the “premature ventricular contraction” class, ECG segments were correctly classified 96% of the time. In total, there was an average classification accuracy of 98.33%. The sensitivity (SNS) and the specificity (SPC) were, respectively, 98.33% and 98.35%. The new approach based on deep learning and, in particular, on a CNN network guaranteed excellent performance in automatic recognition and, therefore, prevention of cardiovascular diseases. Full article
(This article belongs to the Special Issue Application of Neural Networks in Biosignal Process)
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Open AccessArticle
OCT Image Restoration Using Non-Local Deep Image Prior
Electronics 2020, 9(5), 784; https://doi.org/10.3390/electronics9050784 - 11 May 2020
Abstract
In recent years, convolutional neural networks (CNN) have been widely used in image denoising for their high performance. One difficulty in applying the CNN to medical image denoising such as speckle reduction in the optical coherence tomography (OCT) image is that a large [...] Read more.
In recent years, convolutional neural networks (CNN) have been widely used in image denoising for their high performance. One difficulty in applying the CNN to medical image denoising such as speckle reduction in the optical coherence tomography (OCT) image is that a large amount of high-quality data is required for training, which is an inherent limitation for OCT despeckling. Recently, deep image prior (DIP) networks have been proposed for image restoration without pre-training since the CNN structures have the intrinsic ability to capture the low-level statistics of a single image. However, the DIP has difficulty finding a good balance between maintaining details and suppressing speckle noise. Inspired by DIP, in this paper, a sorted non-local statics which measures the signal autocorrelation in the differences between the constructed image and the input image is proposed for OCT image restoration. By adding the sorted non-local statics as a regularization loss in the DIP learning, more low-level image statistics are captured by CNN networks in the process of OCT image restoration. The experimental results demonstrate the superior performance of the proposed method over other state-of-the-art despeckling methods, in terms of objective metrics and visual quality. Full article
(This article belongs to the Special Issue Application of Neural Networks in Biosignal Process)
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