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Electronics
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Biomedical Signal Processing
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Biomedical Signal Processing

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 16277

Special Issue Editors

Prof. Dr. Fernando Seoane

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Guest Editor
1. Department for Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
2. Department of Medical Care Technologies, Biomedical Engineering, Karolinska University Hospital, Stockholm, Sweden
3. Swedish School of Textiles, University of Borås, Borås, Sweden
Interests: electrical bioimpedance smart textiles; biomedical engineering; biomedical instrumentation; biomedical signal processing; wearable sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Kunal Mankodiya

E-Mail Website
Guest Editor
Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA
Interests: wearable biosensors; smart textiles; medical internet of things; and neural engineering
Dr. Ivan Pau

E-Mail Website
Guest Editor
Departamento de Ingeniería Telemática y Electrónica (DTE), Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: mobile computing; smart spaces; e-therapies; e-health applications

Special Issue Information

Dear Colleagues,

Advances in electronics in recent years in miniaturization, growth of computer processing power, and the reduction of power consumption, together with new sensing and visualization technologies, have transformed the landscape of potential applications in biomedical engineering where biomedical signal processing is an enabling factor. Such applications range from personalized healthcare through wearable sensing to implantable instrumentation.

System-on-chip solutions and powerful embedded systems allow for implementing biomedical signal processing methods and tools enabling real-time applications that used to be bounded to offline processing or desktop applications.

This Special Issue calls for papers presenting novel works about biomedical signal processing methods and studies fostering applications and solutions in the following fields:

  • Wearable sensing
  • Implantable electronics
  • Pervasive healthcare
  • M-health
  • Bedside monitoring
  • Point-of-care

Prof. Dr. Fernando Seoane
Dr. Kunal Mankodiya
Dr. Ivan Pau
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 submissions that pass pre-check are 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 semimonthly 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 2400 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

  • embedded systems
  • system-on-a-chip
  • mobile computing
  • biomedical signals and systems

Published Papers (5 papers)

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Research

18 pages, 1722 KiB  
Article
Assessment of Dual-Tree Complex Wavelet Transform to Improve SNR in Collaboration with Neuro-Fuzzy System for Heart-Sound Identification
by Bassam Al-Naami, Hossam Fraihat, Jamal Al-Nabulsi, Nasr Y. Gharaibeh, Paolo Visconti and Abdel-Razzak Al-Hinnawi
Electronics 2022, 11(6), 938; https://doi.org/10.3390/electronics11060938 - 17 Mar 2022
Cited by 3 | Viewed by 2372
Abstract
The research paper proposes a novel denoising method to improve the outcome of heart-sound (HS)-based heart-condition identification by applying the dual-tree complex wavelet transform (DTCWT) together with the adaptive neuro-fuzzy inference System (ANFIS) classifier. The method consists of three steps: first, preprocessing to [...] Read more.
The research paper proposes a novel denoising method to improve the outcome of heart-sound (HS)-based heart-condition identification by applying the dual-tree complex wavelet transform (DTCWT) together with the adaptive neuro-fuzzy inference System (ANFIS) classifier. The method consists of three steps: first, preprocessing to eliminate 50 Hz noise; second, applying four successive levels of DTCWT to denoise and reconstruct the time-domain HS signal; third, to evaluate ANFIS on a total of 2735 HS recordings from an international dataset (PhysioNet Challenge 2016). The results show that the signal-to-noise ratio (SNR) with DTCWT was significantly improved (p < 0.001) as compared to original HS recordings. Quantitatively, there was an 11% to many decibel (dB)-fold increase in SNR after DTCWT, representing a significant improvement in denoising HS. In addition, the ANFIS, using six time-domain features, resulted in 55–86% precision, 51–98% recall, 53–86% f-score, and 54–86% MAcc compared to other attempts on the same dataset. Therefore, DTCWT is a successful technique in removing noise from biosignals such as HS recordings. The adaptive property of ANFIS exhibited capability in classifying HS recordings. Full article
(This article belongs to the Special Issue Biomedical Signal Processing)
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29 pages, 2701 KiB  
Article
Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling
by Muhammad Wasimuddin, Khaled Elleithy, Abdelshakour Abuzneid, Miad Faezipour and Omar Abuzaghleh
Electronics 2021, 10(2), 170; https://doi.org/10.3390/electronics10020170 - 14 Jan 2021
Cited by 27 | Viewed by 4808
Abstract
Cardiovascular diseases have been reported to be the leading cause of mortality across the globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, is of main interest among researchers, as its early diagnosis can prevent life threatening cardiac conditions and [...] Read more.
Cardiovascular diseases have been reported to be the leading cause of mortality across the globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, is of main interest among researchers, as its early diagnosis can prevent life threatening cardiac conditions and potentially save human lives. Analyzing the Electrocardiogram (ECG) can provide valuable diagnostic information to detect different types of cardiac arrhythmia. Real-time ECG monitoring systems with advanced machine learning methods provide information about the health status in real-time and have improved user’s experience. However, advanced machine learning methods have put a burden on portable and wearable devices due to their high computing requirements. We present an improved, less complex Convolutional Neural Network (CNN)-based classifier model that identifies multiple arrhythmia types using the two-dimensional image of the ECG wave in real-time. The proposed model is presented as a three-layer ECG signal analysis model that can potentially be adopted in real-time portable and wearable monitoring devices. We have designed, implemented, and simulated the proposed CNN network using Matlab. We also present the hardware implementation of the proposed method to validate its adaptability in real-time wearable systems. The European ST-T database recorded with single lead L3 is used to validate the CNN classifier and achieved an accuracy of 99.23%, outperforming most existing solutions. Full article
(This article belongs to the Special Issue Biomedical Signal Processing)
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13 pages, 855 KiB  
Article
An FPGA-Based Neuron Activity Extraction Unit for a Wireless Neural Interface
by Mehdi Hasan Chowdhury, Sahar Elyahoodayan, Dong Song and Ray C. C. Cheung 
Electronics 2020, 9(11), 1834; https://doi.org/10.3390/electronics9111834 - 3 Nov 2020
Cited by 4 | Viewed by 2108
Abstract
As computational and functional brain model development are solely dependent upon the data acquired from the neural interface, this device plays a vital role in both prosthetic developments and neurological experiments. A wireless neural interface is preferred over a traditional wired one because [...] Read more.
As computational and functional brain model development are solely dependent upon the data acquired from the neural interface, this device plays a vital role in both prosthetic developments and neurological experiments. A wireless neural interface is preferred over a traditional wired one because it can maximize the comfort of the subject and ensure the freedom of movement while implemented. This paper describes the field programmable gate array (FPGA) prototype design of a low-power multichannel neuron activity extraction unit suitable for a wireless neural interface. To achieve the low-power requirement, we proposed a novel neural signal extraction algorithm which can provide an up to 6000X transmission rate reduction considering the input signal. Consequently, this technique offers at least 2X power reduction compared to the state-of-the-art systems. We implemented this scheme in Xilinx Zynq-7000 FPGA, which can be used as an intermediate transition towards the application specific integrated circuit (ASIC) design for on-chip neural signal processing. The proposed FPGA prototype offers reconfigurable computability, which means the model can be modified and verified according to prerequisites before the final ASIC design. This prototype consists of a signal filtering unit and a signal extraction unit which can be used either as stand-alone units or combined as a complete system. Our proposed scheme also provides a provision to work as a single-channel or a scalable multichannel interface based on user’s demands. We collected practical neural signals from rat brains and validated the efficacy of the implemented system using in-silico signal processing. Full article
(This article belongs to the Special Issue Biomedical Signal Processing)
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14 pages, 2086 KiB  
Article
Blind Source Separation for the Aggregation of Machine Learning Algorithms: An Arrhythmia Classification Case
by Krzysztof Gajowniczek, Iga Grzegorczyk, Michał Gostkowski and Tomasz Ząbkowski
Electronics 2020, 9(3), 425; https://doi.org/10.3390/electronics9030425 - 3 Mar 2020
Cited by 1 | Viewed by 2569
Abstract
In this work, we present an application of the blind source separation (BSS) algorithm to reduce false arrhythmia alarms and to improve the classification accuracy of artificial neural networks (ANNs). The research was focused on a new approach for model aggregation to deal [...] Read more.
In this work, we present an application of the blind source separation (BSS) algorithm to reduce false arrhythmia alarms and to improve the classification accuracy of artificial neural networks (ANNs). The research was focused on a new approach for model aggregation to deal with arrhythmia types that are difficult to predict. The data for analysis consisted of five-minute-long physiological signals (ECG, BP, and PLETH) registered for patients with cardiac arrhythmias. For each patient, the arrhythmia alarm occurred at the end of the signal. The data present a classification problem of whether the alarm is a true one—requiring attention or is false—should not have been generated. It was confirmed that BSS ANNs are able to detect four arrhythmias—asystole, ventricular tachycardia, ventricular fibrillation, and tachycardia—with higher classification accuracy than the benchmarking models, including the ANN, random forest, and recursive partitioning and regression trees. The overall challenge scores were between 63.2 and 90.7. Full article
(This article belongs to the Special Issue Biomedical Signal Processing)
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18 pages, 4040 KiB  
Article
EEG Feature Extraction Based on a Bilevel Network: Minimum Spanning Tree and Regional Network
by Zhizeng Luo, Xianju Lu and Xugang Xi
Electronics 2020, 9(2), 203; https://doi.org/10.3390/electronics9020203 - 21 Jan 2020
Cited by 12 | Viewed by 3284
Abstract
Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain–computer interface (BCI). Although the methods of brain network analysis have been widely studied in the BCI field, these methods are limited by differences in network size, density, and standardization. [...] Read more.
Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain–computer interface (BCI). Although the methods of brain network analysis have been widely studied in the BCI field, these methods are limited by differences in network size, density, and standardization. To address this issue and improve classification accuracy, we propose a novel method, in which the hybrid features of the brain function based on the bilevel network are extracted. Minimum spanning tree (MST) based on electroencephalogram (EEG) signal nodes in different MIs is constructed as the first network layer to solve the global network connectivity problem. In addition, the regional network in different movement patterns is constructed as the second network layer to determine the network characteristics, which is consistent with the correspondence between limb movement patterns and cerebral cortex in neurophysiology. We attempt to apply MST to the classification of the MI EEG signals, and the bilevel network has better interpretability. Thereafter, a vector is formed by combining the MST fundamental features with the directional features of the regional network. Our method is validated using the BCI Competition IV Dataset I. Experimental results verify the feasibility of the bilevel network framework. Furthermore, the average classification performance of the proposed method reaches 89.50%, which is higher than that of other competing methods, thereby indicating that the bilevel network is effective for MI classification. Full article
(This article belongs to the Special Issue Biomedical Signal Processing)
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