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Applied Sciences
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Recent Advances in Biomedical Image and Signal Processing
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Recent Advances in Biomedical Image and Signal Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (30 July 2023) | Viewed by 8033

Special Issue Editors

Prof. Dr. Sándor Fridli

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Guest Editor
Department of Numerical Analysis, Faculty of Informatics, ELTE Eötvös Loránd University, 1117 Budapest, Hungary
Interests: mathematics; computer science
Dr. Adam Pantanowitz

E-Mail Website
Guest Editor
Angela & David Fine Chair in Innovation, and Director of the Wits Innovation Centre, Biomedical Engineering Research Group, School of Electrical & Information Engineering University of The Witwatersrand, Private Bag 3, Johannesburg 2050, South Africa
Interests: biomedical engineering; machine learning; human computer interaction

Special Issue Information

Dear Colleagues,

Accurate patient monitoring by acquiring various biomedical signals and processing the obtained biomedical images is very useful to support physicians in the early detection, investigation, diagnosis, classification, management and treatment of many pathological conditions and diseases. Biomedical imaging techniques include digital radiography; X-ray computed tomography (CT); nuclear (positron emission tomography—PET); ultrasound; optical and magnetic resonance imaging (MRI); and a variety of new microscopes, including whole-slide imaging (WSI) in digital pathology. New approaches to apply machine learning and image processing/analysis methods to solve learning tasks efficiently and intuitively have been widely used in different aspects of biomedical imaging/signal processing to identify complex patterns and obtain accurate information. We invite submissions of papers exploring cutting-edge research and recent developments in the field of biomedical image processing. Submissions related to recent developments and applied results as well as comprehensive review articles are welcome.

Prof. Dr. Sándor Fridli
Dr. Adam Pantanowitz
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. Applied Sciences 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 (3 papers)

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Research

32 pages, 5164 KiB  
Article
Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals
by Anfal Ahmed Aleidan, Qaisar Abbas, Yassine Daadaa, Imran Qureshi, Ganeshkumar Perumal, Mostafa E. A. Ibrahim and Alaa E. S. Ahmed
Appl. Sci. 2023, 13(16), 9454; https://doi.org/10.3390/app13169454 - 21 Aug 2023
Cited by 2 | Viewed by 1752
Abstract
User authentication has become necessary in different life domains. Traditional authentication methods like personal information numbers (PINs), password ID cards, and tokens are vulnerable to attacks. For secure authentication, methods like biometrics have been developed in the past. Biometric information is hard to [...] Read more.
User authentication has become necessary in different life domains. Traditional authentication methods like personal information numbers (PINs), password ID cards, and tokens are vulnerable to attacks. For secure authentication, methods like biometrics have been developed in the past. Biometric information is hard to lose, forget, duplicate, or share because it is a part of the human body. Many authentication methods focused on electrocardiogram (ECG) signals have achieved great success. In this paper, we have developed cardiac biometrics for human identification using a deep learning (DL) approach. Cardiac biometric systems rely on cardiac signals that are captured using the electrocardiogram (ECG), photoplethysmogram (PPG), and phonocardiogram (PCG). This study utilizes the ECG as a biometric modality because ECG signals are a superior choice for accurate, secure, and reliable biometric-based human identification systems, setting them apart from PPG and PCG approaches. To get better performance in terms of accuracy and computational time, we have developed an ensemble approach based on VGG16 pre-trained transfer learning (TL) and Long Short-Term Memory (LSTM) architectures to optimize features. To develop this authentication system, we have fine-tuned this ensemble network. In the first phase, we preprocessed the ECG biosignal to remove noise. In the second phase, we converted the 1-D ECG signals into a 2-D spectrogram image using a transformation phase. Next, the feature extraction step is performed on spectrogram images using the proposed ensemble DL technique, and finally, those features are identified by the boosting machine learning classifier to recognize humans. Several experiments were performed on the selected dataset, and on average, the proposed system achieved 98.7% accuracy, 98.01% precision, 97.1% recall, and 0.98 AUC. In this paper, we have compared the developed approach with state-of-the-art biometric authentication systems. The experimental results demonstrate that our proposed system outperformed the human recognition competition. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Image and Signal Processing)
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14 pages, 1221 KiB  
Article
A Machine Learning Approach to the Non-Invasive Estimation of Continuous Blood Pressure Using Photoplethysmography
by Basheq Tarifi, Aaron Fainman, Adam Pantanowitz and David M. Rubin
Appl. Sci. 2023, 13(6), 3955; https://doi.org/10.3390/app13063955 - 20 Mar 2023
Cited by 2 | Viewed by 3655
Abstract
Blood pressure is an important vital sign that sometimes requires continuous measurement. The current methods include cuff measurements (manual auscultation and oscillometric techniques) for non-continuous measurement and invasive arterial cannulation for continuous measurement. The use of photoplethysmography as a cuffless, non-invasive, and continuous [...] Read more.
Blood pressure is an important vital sign that sometimes requires continuous measurement. The current methods include cuff measurements (manual auscultation and oscillometric techniques) for non-continuous measurement and invasive arterial cannulation for continuous measurement. The use of photoplethysmography as a cuffless, non-invasive, and continuous blood pressure measurement system is investigated through the use of four neural networks. These predict the systolic blood pressure, diastolic blood pressure, mean arterial blood pressure, and waveform shape. The models are trained on 890 h of data from 1669 patients in the MIMIC-III database. Feature-trained artificial neural networks predict the systolic blood pressure to 5.26 ± 6.53 mmHg (mean error ± standard deviation), the diastolic blood pressure to 2.96 ± 3.31 mmHg, and the mean arterial pressure to 3.27 ± 3.55 mmHg. These are used to shift and scale the predicted waveform, allowing the waveform prediction neural network to optimise for the wave shape rather than the amplitude. The waveform prediction has 86.4% correlation with the actual arterial blood pressure waveform. All results meet international clinical blood pressure measurement standards and could potentially change how blood pressure is measured in both clinical and research settings. However, more data from healthy individuals and analysis of the models’ biases based on clinical features is required. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Image and Signal Processing)
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29 pages, 12633 KiB  
Article
Common-Mode Driven Synchronous Filtering of the Powerline Interference in ECG
by Tatyana Neycheva, Dobromir Dobrev and Vessela Krasteva
Appl. Sci. 2022, 12(22), 11328; https://doi.org/10.3390/app122211328 - 8 Nov 2022
Cited by 2 | Viewed by 2059
Abstract
Powerline interference (PLI) is a major disturbing factor in ground-free biopotential acquisition systems. PLI produces both common-mode and differential input voltages. The first is suppressed by a high common-mode rejection ratio of bioamplifiers. However, the differential PLI component evoked by the imbalance of [...] Read more.
Powerline interference (PLI) is a major disturbing factor in ground-free biopotential acquisition systems. PLI produces both common-mode and differential input voltages. The first is suppressed by a high common-mode rejection ratio of bioamplifiers. However, the differential PLI component evoked by the imbalance of electrode impedances is amplified together with the diagnostic differential biosignal. Therefore, PLI filtering is always demanded and commonly managed by analog or digital band-rejection filters. In electrocardiography (ECG), PLI filters are not ideal, inducing QRS and ST distortions as a transient reaction to steep slopes, or PLI remains when its amplitude varies and PLI frequency deviates from the notch. This study aims to minimize the filter errors in wide deviation ranges of PLI amplitudes and frequencies, introducing a novel biopotential readout circuit with a software PLI demodulator–remodulator concept for synchronous processing of both differential-mode and common-mode signals. A closed-loop digital synchronous filtering (SF) algorithm is designed to subtract a PLI estimation from the differential-mode input in real time. The PLI estimation branch connected to the SF output includes four stages: (i) prefilter and QRS limiter; (ii) quadrature demodulator of the output PLI using a common-mode driven reference; (iii) two servo loops for low-pass filtering and the integration of in-phase and quadrature errors; (iv) quadrature remodulator for synthesis of the estimated PLI using the common-mode signal as a carrier frequency. A simulation study of artificially generated PLI sinusoids with frequency deviations (48–52 Hz, slew rate 0.01–0.1 Hz/s) and amplitude deviations (root mean square (r.m.s.) 50–1000 μV, slew rate 10–200 μV/s) is conducted for the optimization of SF servo loop settings with artificial signals from the CTS-ECG calibration database (10 s, 1 lead) as well as for the SF algorithm test with 40 low-noise recordings from the Physionet PTB Diagnostic ECG database (10 s, 12 leads) and CTS-ECG analytical database (10 s, 8 leads). The statistical study for the PLI frequencies (48–52 Hz, slew rate ≤ 0.1 Hz/s) and amplitudes (≤1000 μV r.m.s., slew rate ≤ 40 μV/s) show that maximal SF errors do not exceed 15 μV for any record and any lead, which satisfies the standard requirements for a peak ringing noise of < 25 μV. The signal-to-noise ratio improvement reaches 57–60 dB. SF is shown to be robust against phase shifts between differential- and common-mode PLI. Although validated for ECG signals, the presented SF algorithm is generalizable to different biopotential acquisition settings via surface electrodes (electroencephalogram, electromyogram, electrooculogram, etc.) and can benefit many diagnostic and therapeutic medical devices. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Image and Signal Processing)
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