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Electronics
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FPGA/GPU Acceleration of Biomedical Engineering Applications
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FPGA/GPU Acceleration of Biomedical Engineering Applications

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 16001

Special Issue Editors

Dr. Gabriel Caffarena

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Guest Editor
Department of Information Technologies, University CEU-San Pablo, 28003 Madrid, Spain
Interests: FPGA/GPU algorithm acceleration; video codecs; biosignal digital processing; VLSI system design and design automation
Special Issues, Collections and Topics in MDPI journals
Dr. Madhav P. Desai

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Guest Editor
Department of Electrical Engineering, Indian Institute of Technology, Bombay, India
Interests: FPGA-based algorithm acceleration; circuit and systems; VLSI system design and design automation
Dr. Ruzica Jevtic

E-Mail Website
Guest Editor
Department of Information Technologies, University CEU-San Pablo, Madrid, Spain
Interests: low-power circuit design; power management in ASICs and FPGAs; security in digital circuits
Prof. Dr. Encarnación Castillo

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Guest Editor
Department of Electronics and Computer Technology, University of Granada, 18071 Granada, Spain
Interests: power electronics; energy harvesting; nanostructured materials; wearable sensors and systems; biomedical instruments; smart instrumentation; flexible electronics; reconfigurable technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical Engineering is one of the most active research fields. Traditionally, Bioinformatics applications were clearly in need of high-performance computing systems. However, in recent years, most biomedical applications have become data-hungry due to the thriving machine learning algorithms, as well as the increase in the data acquisition capabilities. For instance, low-bandwidth application, such as ECG analysis, can now benefit from computationally intensive deep learning techniques. As another example, neural interfaces are being redefined since the number of signal acquisition units is increasing exponentially. 

The use of microprocessors or microcontroller units for high-performance data analysis has severe limitations in terms of power consumption and throughput, so technologies oriented toward massive parallelization as graphics processor units (GPU) and field-programmable gate arrays (FPGA) are attractive and effective solutions acting as accelerators of applications. The former provides an extremely high level of parallelism while keeping a friendly software development system; as a drawback, power consumption is equivalent to that of high-end microprocessors. The latter allows for the design of optimal architectures with high parallelism, while the development times are in general notably increased.

In this Special Issue, we propose to investigate GPU and FPGA devices as a means to improve the computing capabilities for both high-performance systems and embedded systems to deal with the challenges of biomedical engineering applications, as well as techniques to keep up with time-to-market constraints. Topics of interest in this Special Issue include but are not limited to the following topics:

  • Bioinformatics applications;
  • E-Health;
  • Automatic diagnosis;
  • Machine learning for biomedical applications;
  • Biomedical embedded systems;
  • FPGA-/GPU-tuned novel biomedical algorithms;
  • Optimized hardware architectures for biomedical applications;
  • Efficient parallel compilers;
  • Efficient hardware design tools.

Dr. Gabriel Caffarena
Dr. Madhav P. Desai
Dr. Ruzica Jevtic
Dr. Encarnación Castillo
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.

Published Papers (5 papers)

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Research

15 pages, 4375 KiB  
Article
FPGA-Based Hardware Accelerator on Portable Equipment for EEG Signal Patterns Recognition
by Yu Xie, Tamás Majoros and Stefan Oniga
Electronics 2022, 11(15), 2410; https://doi.org/10.3390/electronics11152410 - 02 Aug 2022
Cited by 5 | Viewed by 2175
Abstract
Electroencephalogram (EEG) is a recording of comprehensive reflection of physiological brain activities. Because of many reasons, however, including noises of heartbeat artifacts and muscular movements, there are complex challenges for efficient EEG signal classification. The Convolutional Neural Networks (CNN) is considered a promising [...] Read more.
Electroencephalogram (EEG) is a recording of comprehensive reflection of physiological brain activities. Because of many reasons, however, including noises of heartbeat artifacts and muscular movements, there are complex challenges for efficient EEG signal classification. The Convolutional Neural Networks (CNN) is considered a promising tool for extracting data features. A deep neural network can detect the deeper-level features with a multilayer through nonlinear mapping. However, there are few viable deep learning algorithms applied to BCI systems. This study proposes a more effective acquisition and processing HW-SW method for EEG biosignal. First, we use a consumer-grade EEG acquisition device to record EEG signals. Short-time Fourier transform (STFT) and Continuous Wavelet Transform (CWT) methods will be used for data preprocessing. Compared with other algorithms, the CWT-CNN algorithm shows a better classification accuracy. The research result shows that the best classification accuracy of the CWT-CNN algorithm is 91.65%. On the other side, CNN inference requires many convolution operations. We further propose a lightweight CNN inference hardware accelerator framework to speed up inference calculation, and we verify and evaluate its performance. The proposed framework performs network tasks quickly and precisely while using less logical resources on the PYNQ-Z2 FPGA development board. Full article
(This article belongs to the Special Issue FPGA/GPU Acceleration of Biomedical Engineering Applications)
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28 pages, 7040 KiB  
Article
Novel FPGA-Based Visual Stimulation Method for Eye Movement Analysis
by Alejandro Benitez Fernandez, Bárbaro N. Socarrás Hernández, Justo M. Herrera Rodríguez, Bruno da Silva and Carlos R. Vázquez-Seisdedos
Electronics 2022, 11(3), 303; https://doi.org/10.3390/electronics11030303 - 19 Jan 2022
Viewed by 1882
Abstract
Several studies have demonstrated that irregularities in eye movements represent an important indicator to diagnose diseases affecting the central nervous system. In fact, abnormal horizontal and vertical eye movements play an important role in measuring the progress of neurodegenerative diseases. Electro-oculography (EOG) is [...] Read more.
Several studies have demonstrated that irregularities in eye movements represent an important indicator to diagnose diseases affecting the central nervous system. In fact, abnormal horizontal and vertical eye movements play an important role in measuring the progress of neurodegenerative diseases. Electro-oculography (EOG) is a widespread technique that monitors the horizontal and vertical eye movements in response to a visual stimulation pattern. These visual stimuli require stimulus-response synchronization, low latency, and a real-time response. In this work, a novel system based on a Field-Programmable Gate Array (FPGA) is designed to address hundreds of LEDs for the generation of multiple visual stimulus signals and for EOG acquisition. Our evaluation demonstrates that the proposed system enhances the accuracy of the signals generated, showing excellent results in the stimulus-response synchronism and quality of the stimuli waveform. Full article
(This article belongs to the Special Issue FPGA/GPU Acceleration of Biomedical Engineering Applications)
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17 pages, 700 KiB  
Article
A Low-Latency, Low-Power FPGA Implementation of ECG Signal Characterization Using Hermite Polynomials
by Madhav P. Desai, Gabriel Caffarena, Ruzica Jevtic, David G. Márquez and Abraham Otero
Electronics 2021, 10(19), 2324; https://doi.org/10.3390/electronics10192324 - 22 Sep 2021
Cited by 11 | Viewed by 2812
Abstract
Automatic ECG signal characterization is of critical importance in patient monitoring and diagnosis. This process is computationally intensive, and low-power, online (real-time) solutions to this problem are of great interest. In this paper, we present a novel, dedicated hardware implementation of the ECG [...] Read more.
Automatic ECG signal characterization is of critical importance in patient monitoring and diagnosis. This process is computationally intensive, and low-power, online (real-time) solutions to this problem are of great interest. In this paper, we present a novel, dedicated hardware implementation of the ECG signal processing chain based on Hermite functions, aiming for real-time processing. Starting from 12-bit ADC samples of the ECG signal, the hardware implements filtering, peak and QRS detection, and least-squares Hermite polynomial fit on heartbeats. This hardware module can be used to compress ECG data or to perform beat classification. The hardware implementation has been validated on a Field Programmable Gate Array (FPGA). The implementation is generated using an algorithm-to-hardware compiler tool-chain and the resulting hardware is characterized using a low-cost off-the-shelf FPGA card. The single-beat best-fit computation latency when using six Hermite basis polynomials is under 1 s with a throughput of 3 beats/s and with an average power dissipation around 28 mW, demonstrating true real-time applicability. Full article
(This article belongs to the Special Issue FPGA/GPU Acceleration of Biomedical Engineering Applications)
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19 pages, 896 KiB  
Article
SOFIA: Selection of Medical Features by Induced Alterations in Numeric Labels
by Franklin Parrales Bravo, Alberto A. Del Barrio García, Luis M. S. Russo and Jose L. Ayala
Electronics 2020, 9(9), 1492; https://doi.org/10.3390/electronics9091492 - 11 Sep 2020
Cited by 1 | Viewed by 2689
Abstract
This work deals with the improvement of multi-target prediction models through a proposed optimization called Selection Of medical Features by Induced Alterations in numeric labels (SOFIA). This method performs a data transformation when: (1) weighting the features, (2) performing small perturbations on numeric [...] Read more.
This work deals with the improvement of multi-target prediction models through a proposed optimization called Selection Of medical Features by Induced Alterations in numeric labels (SOFIA). This method performs a data transformation when: (1) weighting the features, (2) performing small perturbations on numeric labels and (3) selecting the features that are relevant in the trained multi-target prediction models. With the purpose of decreasing the computational cost in the SOFIA method, we consider those multi-objective optimization metaheuristics that support parallelization. In this sense, we propose an extension of the Natural Optimization (NO) approach for Simulated Annealing to support a multi-objective (MO) optimization. This proposed extension, called MONO, and some multiobjective evolutionary algorithms (MOEAs) are considered when performing the SOFIA method to improve prediction models in a multi-stage migraine treatment. This work also considers the adaptation of these metaheuristics to run on GPUs for accelerating the exploration of a larger space of solutions and improving results at the same time. The obtained results show that accuracies close to 88% are obtained with the MONO metaheuristic when employing eight threads and when running on a GPU. In addition, training times have been decreased from more than 8 h to less than 45 min when running the algorithms on a GPU. Besides, classification models trained with the SOFIA method only require 15 medical features or fewer to predict treatment responses. All in all, the methods proposed in this work remarkably improve the accuracy of multi-target prediction models for the OnabotulinumtoxinA (BoNT-A) treatment, while selecting those relevant features that allow us to know in advance the response to every stage of the treatment. Full article
(This article belongs to the Special Issue FPGA/GPU Acceleration of Biomedical Engineering Applications)
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25 pages, 33441 KiB  
Article
FlexAlign: An Accurate and Fast Algorithm for Movie Alignment in Cryo-Electron Microscopy
by David Střelák, Jiří Filipovič, Amaya Jiménez-Moreno, Jose María Carazo and Carlos Óscar Sánchez Sorzano
Electronics 2020, 9(6), 1040; https://doi.org/10.3390/electronics9061040 - 23 Jun 2020
Cited by 5 | Viewed by 4535
Abstract
Cryogenic Electron Microscopy (Cryo-EM) has been established as one of the key players in Structural Biology. It can reconstruct a 3D model of the sample at the near-atomic resolution, which led to a Method of the year award by Nature, and the Nobel [...] Read more.
Cryogenic Electron Microscopy (Cryo-EM) has been established as one of the key players in Structural Biology. It can reconstruct a 3D model of the sample at the near-atomic resolution, which led to a Method of the year award by Nature, and the Nobel Prize in 2017. With the growing number of facilities, faster microscopes, and new imaging techniques, new algorithms are needed to process the so-called movies data produced by the microscopes in real-time, while preserving a high resolution and maximum of additional information. In this article, we present a new algorithm used for movie alignment, called FlexAlign. FlexAlign is able to correctly compensate for the shift produced during the movie acquisition on-the-fly, using the current generation of hardware. The algorithm performs a global and elastic local registration of the movie frames using Cross-Correlation and B-spline interpolation for high precision. We show that our execution time is compatible with real-time correction and that we preserve the high-resolution information up to high frequency. Full article
(This article belongs to the Special Issue FPGA/GPU Acceleration of Biomedical Engineering Applications)
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