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Biomimetics
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Advances in Artificial Intelligence for Biomedical Signal and Image Processing:...
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Advances in Artificial Intelligence for Biomedical Signal and Image Processing: From Theory to Practice

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Bioinspired Sensorics, Information Processing and Control".

Deadline for manuscript submissions: closed (25 April 2024) | Viewed by 2605

Special Issue Editor

Dr. Ebrahim Karami

E-Mail Website
Guest Editor
Department of Engineering and Applied Sciences, Memorial University, St. John’s, NL A1B 3X5, Canada
Interests: machine learning; computer vision; biomedical engineering; wireless communications and networks; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the ever-evolving landscape of biomedical engineering, Artificial Intelligence (AI) has emerged as a transformative force, revolutionizing the realms of signal and image processing. This Special Issue seeks to showcase the diverse applications of AI in biomedical engineering, ranging from innovative signal processing techniques to cutting-edge image analysis methodologies.

The scope encompasses a broad spectrum of research areas, including but not limited to medical signal processing, biomedical image analysis, bioinformatics, and computational biology. We invite contributions that delve into the development and application of AI algorithms to address challenges in diagnostics, monitoring, treatment planning, and therapeutic interventions.

Submissions are encouraged to explore, but are not limited to, the following topics:

  • Machine learning applications in medical signal processing;
  • Deep learning techniques for biomedical image analysis;
  • AI-driven diagnostics and disease prediction;
  • Computational modeling for physiological systems;
  • Bioinformatics and genomic data analysis;
  • Wearable devices and AI for personalized healthcare;
  • Human–machine interfaces for medical applications;
  • Ethical considerations in AI-driven biomedical engineering;
  • AI applications in ECG, EEG, and EMG signal processing;
  • Genetics and genomics in the era of AI;
  • Integrative approaches to multi-modal data fusion.

Dr. Ebrahim Karami
Guest Editor

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. Biomimetics 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 2200 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
  • biomedical engineering
  • medical imaging
  • signal processing
  • deep learning
  • human–machine interfaces
  • wearable devices

Published Papers (3 papers)

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Research

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20 pages, 9881 KiB  
Article
Review of Vision-Based Environmental Perception for Lower-Limb Exoskeleton Robots
by Chen Wang, Zhongcai Pei, Yanan Fan, Shuang Qiu and Zhiyong Tang
Biomimetics 2024, 9(4), 254; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics9040254 - 22 Apr 2024
Viewed by 354
Abstract
The exoskeleton robot is a wearable electromechanical device inspired by animal exoskeletons. It combines technologies such as sensing, control, information, and mobile computing, enhancing human physical abilities and assisting in rehabilitation training. In recent years, with the development of visual sensors and deep [...] Read more.
The exoskeleton robot is a wearable electromechanical device inspired by animal exoskeletons. It combines technologies such as sensing, control, information, and mobile computing, enhancing human physical abilities and assisting in rehabilitation training. In recent years, with the development of visual sensors and deep learning, the environmental perception of exoskeletons has drawn widespread attention in the industry. Environmental perception can provide exoskeletons with a certain level of autonomous perception and decision-making ability, enhance their stability and safety in complex environments, and improve the human–machine–environment interaction loop. This paper provides a review of environmental perception and its related technologies of lower-limb exoskeleton robots. First, we briefly introduce the visual sensors and control system. Second, we analyze and summarize the key technologies of environmental perception, including related datasets, detection of critical terrains, and environment-oriented adaptive gait planning. Finally, we analyze the current factors limiting the development of exoskeleton environmental perception and propose future directions. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Biomedical Signal and Image Processing: From Theory to Practice)
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30 pages, 3607 KiB  
Article
ADHD-AID: Aiding Tool for Detecting Children’s Attention Deficit Hyperactivity Disorder via EEG-Based Multi-Resolution Analysis and Feature Selection
by Omneya Attallah
Biomimetics 2024, 9(3), 188; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics9030188 - 20 Mar 2024
Viewed by 1027
Abstract
The severe effects of attention deficit hyperactivity disorder (ADHD) among adolescents can be prevented by timely identification and prompt therapeutic intervention. Traditional diagnostic techniques are complicated and time-consuming because they are subjective-based assessments. Machine learning (ML) techniques can automate this process and prevent [...] Read more.
The severe effects of attention deficit hyperactivity disorder (ADHD) among adolescents can be prevented by timely identification and prompt therapeutic intervention. Traditional diagnostic techniques are complicated and time-consuming because they are subjective-based assessments. Machine learning (ML) techniques can automate this process and prevent the limitations of manual evaluation. However, most of the ML-based models extract few features from a single domain. Furthermore, most ML-based studies have not examined the most effective electrode placement on the skull, which affects the identification process, while others have not employed feature selection approaches to reduce the feature space dimension and consequently the complexity of the training models. This study presents an ML-based tool for automatically identifying ADHD entitled “ADHD-AID”. The present study uses several multi-resolution analysis techniques including variational mode decomposition, discrete wavelet transform, and empirical wavelet decomposition. ADHD-AID extracts thirty features from the time and time–frequency domains to identify ADHD, including nonlinear features, band-power features, entropy-based features, and statistical features. The present study also looks at the best EEG electrode placement for detecting ADHD. Additionally, it looks into the location combinations that have the most significant impact on identification accuracy. Additionally, it uses a variety of feature selection methods to choose those features that have the greatest influence on the diagnosis of ADHD, reducing the classification’s complexity and training time. The results show that ADHD-AID has provided scores for accuracy, sensitivity, specificity, F1-score, and Mathew correlation coefficients of 0.991, 0.989, 0.992, 0.989, and 0.982, respectively, in identifying ADHD with 10-fold cross-validation. Also, the area under the curve has reached 0.9958. ADHD-AID’s results are significantly higher than those of all earlier studies for the detection of ADHD in adolescents. These notable and trustworthy findings support the use of such an automated tool as a means of assistance for doctors in the prompt identification of ADHD in youngsters. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Biomedical Signal and Image Processing: From Theory to Practice)
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Review

Jump to: Research

19 pages, 887 KiB  
Review
Remote Wearable Neuroimaging Devices for Health Monitoring and Neurophenotyping: A Scoping Review
by Mohamed Emish and Sean D. Young
Biomimetics 2024, 9(4), 237; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics9040237 - 16 Apr 2024
Viewed by 710
Abstract
Digital health tracking is a source of valuable insights for public health research and consumer health technology. The brain is the most complex organ, containing information about psychophysical and physiological biomarkers that correlate with health. Specifically, recent developments in electroencephalogram (EEG), functional near-infra-red [...] Read more.
Digital health tracking is a source of valuable insights for public health research and consumer health technology. The brain is the most complex organ, containing information about psychophysical and physiological biomarkers that correlate with health. Specifically, recent developments in electroencephalogram (EEG), functional near-infra-red spectroscopy (fNIRS), and photoplethysmography (PPG) technologies have allowed the development of devices that can remotely monitor changes in brain activity. The inclusion criteria for the papers in this review encompassed studies on self-applied, remote, non-invasive neuroimaging techniques (EEG, fNIRS, or PPG) within healthcare applications. A total of 23 papers were reviewed, comprising 17 on using EEGs for remote monitoring and 6 on neurofeedback interventions, while no papers were found related to fNIRS and PPG. This review reveals that previous studies have leveraged mobile EEG devices for remote monitoring across the mental health, neurological, and sleep domains, as well as for delivering neurofeedback interventions. With headsets and ear-EEG devices being the most common, studies found mobile devices feasible for implementation in study protocols while providing reliable signal quality. Moderate to substantial agreement overall between remote and clinical-grade EEGs was found using statistical tests. The results highlight the promise of portable brain-imaging devices with regard to continuously evaluating patients in natural settings, though further validation and usability enhancements are needed as this technology develops. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Biomedical Signal and Image Processing: From Theory to Practice)
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