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Bioengineering
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Artificial Intelligence and Optimization Methods in Biomedical Engineering
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Artificial Intelligence and Optimization Methods in Biomedical Engineering

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 12478

Special Issue Editors

Dr. Mohammad Shokouhifar

E-Mail Website
Guest Editor
DTU AI and Data Science Hub (DAIDASH), Duy Tan University, Da Nang 550000, Vietnam
Interests: artificial intelligence; optimization algorithms; biomedical engineering; remote healthcare monitoring; prognosis and diagnosis; machine learning; deep learning; swarm and evolutionary algorithms; hyper-heuristic algorithms; biomedical image processing; biomedical signal processing; time-series analysis; internet-of-things; wireless body sensor networks
Special Issues, Collections and Topics in MDPI journals
Dr. Jose Luis Calvo-Rolle

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of A Coruña, 15405 Ferrol, Spain
Interests: knowledge engineering and expert systems for diagnosis and control systems; intelligent systems for modeling; optimization, and control; fault and anomaly detection using traditional and intelligent techniques; new sensors; robust sensors; and virtual sensors
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Frank Werner

E-Mail Website
Guest Editor
Faculty of Mathematics, Otto-von-Guericke-University, P.O. Box 4120, D-39016 Magdeburg, Germany
Interests: scheduling; development of exact and approximate algorithms; stability investigations; discrete optimization; scheduling with interval processing times; complex investigations for scheduling problems; train scheduling; graph theory; logistics; supply chains; packing; simulation; applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) plays an important role in a variety of biomedical engineering applications, range from medical signal/image processing, disease prognosis and diagnosis systems, to personalized medicine and IoT-based remote healthcare monitoring. Traditionally, healthcare monitoring was performed through population health surveys, clinical data, and reports. Over the past years, rapid growth in accessibility of health-related report/signal/image data and advancements in AI and optimization algorithms provide opportunities to improve the healthcare systems by automatically identifying emerging health threats and developing a more detailed understanding of population disease and risk factor distributions. Optimized AI-driven healthcare systems can be helpful in providing up-to-date information as data are collected, processed, and analyzed in a real-time scheme.

This special issue is a venue for communicating recent advances of AI and optimization techniques in healthcare. Authors are invited to submit original research and survey papers focusing on AI-driven approaches for the development, measurement, evaluation, diagnosis, and monitoring solutions related to biomedical engineering.

Topics of interest include but not be limited to:

  • Applications of AI in biomedical engineering
  • Optimized AI-driven smart healthcare monitoring systems
  • AI for measurement, assessment, prognosis and diagnosis systems
  • Machine learning and deep learning techniques in biomedicine
  • AI-driven univariate and multivariate time-series analysis
  • Optimization algorithms in biomedical engineering
  • AI-driven biomedical signal and image processing
  • Security and privacy techniques for personal data preservation
  • Remote healthcare monitoring using wireless body sensor networks
  • AI-driven IoT-based patient data analysis and management

Dr. Mohammad Shokouhifar
Dr. Jose Luis Calvo-Rolle
Prof. Dr. Frank Werner
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. Bioengineering 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 2700 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

  • artificial intelligence
  • optimization
  • bioengineering
  • healthcare monitoring
  • prognosis and diagnosis
  • wearable biosensors
  • machine learning
  • deep learning
  • biomedical image processing
  • time-series analysis
  • internet-of-things (IoT)
  • wireless body sensor networks

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

16 pages, 3910 KiB  
Article
A Deep Learning Method of Human Identification from Radar Signal for Daily Sleep Health Monitoring
by Ken Chen, Yulong Duan, Yi Huang, Wei Hu and Yaoqin Xie
Bioengineering 2024, 11(1), 2; https://doi.org/10.3390/bioengineering11010002 - 20 Dec 2023
Cited by 1 | Viewed by 1580
Abstract
Radar signal has been shown as a promising source for human identification. In daily home sleep-monitoring scenarios, large-scale motion features may not always be practical, and the heart motion or respiration data may not be as ideal as they are in a controlled [...] Read more.
Radar signal has been shown as a promising source for human identification. In daily home sleep-monitoring scenarios, large-scale motion features may not always be practical, and the heart motion or respiration data may not be as ideal as they are in a controlled laboratory setting. Human identification from radar sequences is still a challenging task. Furthermore, there is a need to address the open-set recognition problem for radar sequences, which has not been sufficiently studied. In this paper, we propose a deep learning-based approach for human identification using radar sequences captured during sleep in a daily home-monitoring setup. To enhance robustness, we preprocess the sequences to mitigate environmental interference before employing a deep convolution neural network for human identification. We introduce a Principal Component Space feature representation to detect unknown sequences. Our method is rigorously evaluated using both a public data set and a set of experimentally acquired radar sequences. We report a labeling accuracy of 98.2% and 96.8% on average for the two data sets, respectively, which outperforms the state-of-the-art techniques. Our method excels at accurately distinguishing unknown sequences from labeled ones, with nearly 100% detection of unknown samples and minimal misclassification of labeled samples as unknown. Full article
(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Biomedical Engineering)
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17 pages, 3326 KiB  
Article
An Efficient Binary Sand Cat Swarm Optimization for Feature Selection in High-Dimensional Biomedical Data
by Elnaz Pashaei
Bioengineering 2023, 10(10), 1123; https://doi.org/10.3390/bioengineering10101123 - 25 Sep 2023
Cited by 1 | Viewed by 1505
Abstract
Recent breakthroughs are making a significant contribution to big data in biomedicine which are anticipated to assist in disease diagnosis and patient care management. To obtain relevant information from this data, effective administration and analysis are required. One of the major challenges associated [...] Read more.
Recent breakthroughs are making a significant contribution to big data in biomedicine which are anticipated to assist in disease diagnosis and patient care management. To obtain relevant information from this data, effective administration and analysis are required. One of the major challenges associated with biomedical data analysis is the so-called “curse of dimensionality”. For this issue, a new version of Binary Sand Cat Swarm Optimization (called PILC-BSCSO), incorporating a pinhole-imaging-based learning strategy and crossover operator, is presented for selecting the most informative features. First, the crossover operator is used to strengthen the search capability of BSCSO. Second, the pinhole-imaging learning strategy is utilized to effectively increase exploration capacity while avoiding premature convergence. The Support Vector Machine (SVM) classifier with a linear kernel is used to assess classification accuracy. The experimental results show that the PILC-BSCSO algorithm beats 11 cutting-edge techniques in terms of classification accuracy and the number of selected features using three public medical datasets. Moreover, PILC-BSCSO achieves a classification accuracy of 100% for colon cancer, which is difficult to classify accurately, based on just 10 genes. A real Liver Hepatocellular Carcinoma (TCGA-HCC) data set was also used to further evaluate the effectiveness of the PILC-BSCSO approach. PILC-BSCSO identifies a subset of five marker genes, including prognostic biomarkers HMMR, CHST4, and COL15A1, that have excellent predictive potential for liver cancer using TCGA data. Full article
(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Biomedical Engineering)
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14 pages, 3308 KiB  
Article
Sparse Logistic Regression-Based EEG Channel Optimization Algorithm for Improved Universality across Participants
by Yuxi Shi, Yuanhao Li and Yasuharu Koike
Bioengineering 2023, 10(6), 664; https://doi.org/10.3390/bioengineering10060664 - 31 May 2023
Cited by 3 | Viewed by 1694
Abstract
Electroencephalogram (EEG) channel optimization can reduce redundant information and improve EEG decoding accuracy by selecting the most informative channels. This article aims to investigate the universality regarding EEG channel optimization in terms of how well the selected EEG channels can be generalized to [...] Read more.
Electroencephalogram (EEG) channel optimization can reduce redundant information and improve EEG decoding accuracy by selecting the most informative channels. This article aims to investigate the universality regarding EEG channel optimization in terms of how well the selected EEG channels can be generalized to different participants. In particular, this study proposes a sparse logistic regression (SLR)-based EEG channel optimization algorithm using a non-zero model parameter ranking method. The proposed channel optimization algorithm was evaluated in both individual analysis and group analysis using the raw EEG data, compared with the conventional channel selection method based on the correlation coefficients (CCS). The experimental results demonstrate that the SLR-based EEG channel optimization algorithm not only filters out most redundant channels (filters 75–96.9% of channels) with a 1.65–5.1% increase in decoding accuracy, but it can also achieve a satisfactory level of decoding accuracy in the group analysis by employing only a few (2–15) common EEG electrodes, even for different participants. The proposed channel optimization algorithm can realize better universality for EEG decoding, which can reduce the burden of EEG data acquisition and enhance the real-world application of EEG-based brain–computer interface (BCI). Full article
(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Biomedical Engineering)
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21 pages, 3215 KiB  
Article
Optimized Solutions of Electrocardiogram Lead and Segment Selection for Cardiovascular Disease Diagnostics
by Jiguang Shi, Zhoutong Li, Wenhan Liu, Huaicheng Zhang, Qianxi Guo, Sheng Chang, Hao Wang, Jin He and Qijun Huang
Bioengineering 2023, 10(5), 607; https://doi.org/10.3390/bioengineering10050607 - 18 May 2023
Cited by 3 | Viewed by 1725
Abstract
Most of the existing multi-lead electrocardiogram (ECG) detection methods are based on all 12 leads, which undoubtedly results in a large amount of calculation and is not suitable for the application in portable ECG detection systems. Moreover, the influence of different lead and [...] Read more.
Most of the existing multi-lead electrocardiogram (ECG) detection methods are based on all 12 leads, which undoubtedly results in a large amount of calculation and is not suitable for the application in portable ECG detection systems. Moreover, the influence of different lead and heartbeat segment lengths on the detection is not clear. In this paper, a novel Genetic Algorithm-based ECG Leads and Segment Length Optimization (GA-LSLO) framework is proposed, aiming to automatically select the appropriate leads and input ECG length to achieve optimized cardiovascular disease detection. GA-LSLO extracts the features of each lead under different heartbeat segment lengths through the convolutional neural network and uses the genetic algorithm to automatically select the optimal combination of ECG leads and segment length. In addition, the lead attention module (LAM) is proposed to weight the features of the selected leads, which improves the accuracy of cardiac disease detection. The algorithm is validated on the ECG data from the Huangpu Branch of Shanghai Ninth People’s Hospital (defined as the SH database) and the open-source Physikalisch-Technische Bundesanstalt diagnostic ECG database (PTB database). The accuracy for detection of arrhythmia and myocardial infarction under the inter-patient paradigm is 99.65% (95% confidence interval: 99.20–99.76%) and 97.62% (95% confidence interval: 96.80–98.16%), respectively. In addition, ECG detection devices are designed using Raspberry Pi, which verifies the convenience of hardware implementation of the algorithm. In conclusion, the proposed method achieves good cardiovascular disease detection performance. It selects the ECG leads and heartbeat segment length with the lowest algorithm complexity while ensuring classification accuracy, which is suitable for portable ECG detection devices. Full article
(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Biomedical Engineering)
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Graphical abstract

14 pages, 1546 KiB  
Article
Visual Blood, Visualisation of Blood Gas Analysis in Virtual Reality, Leads to More Correct Diagnoses: A Computer-Based, Multicentre, Simulation Study
by Lisa Bergauer, Samira Akbas, Julia Braun, Michael T. Ganter, Patrick Meybohm, Sebastian Hottenrott, Kai Zacharowski, Florian J. Raimann, Eva Rivas, Manuel López-Baamonde, Donat R. Spahn, Christoph B. Noethiger, David W. Tscholl and Tadzio R. Roche
Bioengineering 2023, 10(3), 340; https://doi.org/10.3390/bioengineering10030340 - 8 Mar 2023
Cited by 3 | Viewed by 2437
Abstract
Interpreting blood gas analysis results can be challenging for the clinician, especially in stressful situations under time pressure. To foster fast and correct interpretation of blood gas results, we developed Visual Blood. This computer-based, multicentre, noninferiority study compared Visual Blood and conventional arterial [...] Read more.
Interpreting blood gas analysis results can be challenging for the clinician, especially in stressful situations under time pressure. To foster fast and correct interpretation of blood gas results, we developed Visual Blood. This computer-based, multicentre, noninferiority study compared Visual Blood and conventional arterial blood gas (ABG) printouts. We presented six scenarios to anaesthesiologists, once with Visual Blood and once with the conventional ABG printout. The primary outcome was ABG parameter perception. The secondary outcomes included correct clinical diagnoses, perceived diagnostic confidence, and perceived workload. To analyse the results, we used mixed models and matched odds ratios. Analysing 300 within-subject cases, we showed noninferiority of Visual Blood compared to ABG printouts concerning the rate of correctly perceived ABG parameters (rate ratio, 0.96; 95% CI, 0.92–1.00; p = 0.06). Additionally, the study revealed two times higher odds of making the correct clinical diagnosis using Visual Blood (OR, 2.16; 95% CI, 1.42–3.29; p < 0.001) than using ABG printouts. There was no or, respectively, weak evidence for a difference in diagnostic confidence (OR, 0.84; 95% CI, 0.58–1.21; p = 0.34) and perceived workload (Coefficient, 2.44; 95% CI, −0.09–4.98; p = 0.06). This study showed that participants did not perceive the ABG parameters better, but using Visual Blood resulted in more correct clinical diagnoses than using conventional ABG printouts. This suggests that Visual Blood allows for a higher level of situation awareness beyond individual parameters’ perception. However, the study also highlighted the limitations of today’s virtual reality headsets and Visual Blood. Full article
(This article belongs to the Special Issue Artificial Intelligence and Optimization Methods in Biomedical Engineering)
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