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Biomedical Sensing and Bioinformatics Processing
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Biomedical Sensing and Bioinformatics Processing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 4294

Special Issue Editors

Dr. Min Shi

E-Mail Website
Guest Editor
Department of Ophthalmology, Harvard University, Boston, MA 02114, USA
Interests: data mining; AI for vision science; medical image analysis; bioinformatics
Dr. Yan Luo

E-Mail Website
Guest Editor
Department of Ophthalmology, Harvard University, Boston, MA 02114, USA
Interests: AI for vision science; responsible AI; medical imaging
Dr. Yu Tian

E-Mail Website
Guest Editor
Department of Ophthalmology, Harvard University, Boston, MA 02114, USA
Interests: AI for vision science; responsible AI; medical imaging; anomaly detection; AI for healthcare
Dr. Wenfeng Zhao

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Binghamton University State University of New York, New York, NY 13902, USA
Interests: neural engineering; neural signal processing; compressed sensing; in-memory computing; and ultralow power VLSI designs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical sensing and imaging technologies are not only significant for the early detection, rapid diagnosis, and precise treatment of diseases, but they also align with key societal values and offer numerous benefits to societies worldwide. These technologies encompass a range of techniques and devices that enable the non-invasive monitoring, diagnosis, and treatment of various medical conditions. The significance and benefits of these technologies are clearly demonstrated via their capacity to enhance patient outcomes, improve quality of life, and foster equitable access to healthcare.

Specifically, wearable/smart biosensors and devices are critical in healthcare and play a pivotal role in facilitating medical Artificial Intelligence (AI) applications. They provide continuous monitoring, accurate data, patient empowerment, and enable remote healthcare. By facilitating the collection of high-quality data, they serve as a foundation for medical AI applications, leading to improved diagnostics, personalized interventions, and enhanced healthcare outcomes. Biosensors drive the shift towards data-driven, proactive, and patient-centric healthcare.

In this Special Issue, we welcome the submission of papers that explore the various aspects of health monitoring by introducing novel imaging and/or sensing methods with pre-clinical/clinical applications. The aim of this Special Issue is to cover a wide range of topics within this domain, including, but not limited to, the following areas:

  • AI and deep learning in biomedical sensing and imaging
  • Photoacoustic imaging and applications
  • Optical coherence tomography
  • Super-resolution imaging
  • Multi-photon imaging
  • Functional near-infrared spectroscopy
  • Multi-modality imaging
  • Molecular imaging and therapy
  • Phototherapy
  • Novel microscopy
  • Translational biomedical optics
  • Optical biosensors
  • Diagnostic, interventional, and therapeutic ultrasound
  • Contrast-enhanced optical and ultrasound imaging
  • Medical AI: classification, object detection, and segmentation using biomedical imaging data

Dr. Min Shi
Dr. Yan Luo
Dr. Yu Tian
Dr. Wenfeng Zhao
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. Sensors 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 2600 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.

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

Published Papers (3 papers)

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Research

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22 pages, 2309 KiB  
Article
Enhancing EEG-Based MI-BCIs with Class-Specific and Subject-Specific Features Detected by Neural Manifold Analysis
by Mirco Frosolone, Roberto Prevete, Lorenzo Ognibeni, Salvatore Giugliano, Andrea Apicella, Giovanni Pezzulo and Francesco Donnarumma
Sensors 2024, 24(18), 6110; https://doi.org/10.3390/s24186110 - 21 Sep 2024
Viewed by 804
Abstract
This paper presents an innovative approach leveraging Neuronal Manifold Analysis of EEG data to identify specific time intervals for feature extraction, effectively capturing both class-specific and subject-specific characteristics. Different pipelines were constructed and employed to extract distinctive features within these intervals, specifically for [...] Read more.
This paper presents an innovative approach leveraging Neuronal Manifold Analysis of EEG data to identify specific time intervals for feature extraction, effectively capturing both class-specific and subject-specific characteristics. Different pipelines were constructed and employed to extract distinctive features within these intervals, specifically for motor imagery (MI) tasks. The methodology was validated using the Graz Competition IV datasets 2A (four-class) and 2B (two-class) motor imagery classification, demonstrating an improvement in classification accuracy that surpasses state-of-the-art algorithms designed for MI tasks. A multi-dimensional feature space, constructed using NMA, was built to detect intervals that capture these critical characteristics, which led to significantly enhanced classification accuracy, especially for individuals with initially poor classification performance. These findings highlight the robustness of this method and its potential to improve classification performance in EEG-based MI-BCI systems. Full article
(This article belongs to the Special Issue Biomedical Sensing and Bioinformatics Processing)
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17 pages, 5014 KiB  
Article
Electrocardiogram-Based Biometric Identification Using Mixed Feature Extraction and Sparse Representation
by Xu Zhang, Qifeng Liu, Dong He, Hui Suo and Chun Zhao
Sensors 2023, 23(22), 9179; https://doi.org/10.3390/s23229179 - 14 Nov 2023
Cited by 1 | Viewed by 1125
Abstract
(1) Background: The ability to recognize identities is an essential component of security. Electrocardiogram (ECG) signals have gained popularity for identity recognition because of their universal, unique, stable, and measurable characteristics. To ensure accurate identification of ECG signals, this paper proposes an approach [...] Read more.
(1) Background: The ability to recognize identities is an essential component of security. Electrocardiogram (ECG) signals have gained popularity for identity recognition because of their universal, unique, stable, and measurable characteristics. To ensure accurate identification of ECG signals, this paper proposes an approach which involves mixed feature sampling, sparse representation, and recognition. (2) Methods: This paper introduces a new method of identifying individuals through their ECG signals. This technique combines the extraction of fixed ECG features and specific frequency features to improve accuracy in ECG identity recognition. This approach uses the wavelet transform to extract frequency bands which contain personal information features from the ECG signals. These bands are reconstructed, and the single R-peak localization determines the ECG window. The signals are segmented and standardized based on the located windows. A sparse dictionary is created using the standardized ECG signals, and the KSVD (K-Orthogonal Matching Pursuit) algorithm is employed to project ECG target signals into a sparse vector–matrix representation. To extract the final representation of the target signals for identification, the sparse coefficient vectors in the signals are maximally pooled. For recognition, the co-dimensional bundle search method is used in this paper. (3) Results: This paper utilizes the publicly available European ST-T database for our study. Specifically, this paper selects ECG signals from 20, 50 and 70 subjects, each with 30 testing segments. The method proposed in this paper achieved recognition rates of 99.14%, 99.09%, and 99.05%, respectively. (4) Conclusion: The experiments indicate that the method proposed in this paper can accurately capture, represent and identify ECG signals. Full article
(This article belongs to the Special Issue Biomedical Sensing and Bioinformatics Processing)
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Review

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19 pages, 632 KiB  
Review
Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective
by Stephanie Batista Niño, Jorge Bernardino and Inês Domingues
Sensors 2024, 24(6), 1752; https://doi.org/10.3390/s24061752 - 8 Mar 2024
Cited by 1 | Viewed by 1541
Abstract
Oncology has emerged as a crucial field of study in the domain of medicine. Computed tomography has gained widespread adoption as a radiological modality for the identification and characterisation of pathologies, particularly in oncology, enabling precise identification of affected organs and tissues. However, [...] Read more.
Oncology has emerged as a crucial field of study in the domain of medicine. Computed tomography has gained widespread adoption as a radiological modality for the identification and characterisation of pathologies, particularly in oncology, enabling precise identification of affected organs and tissues. However, achieving accurate liver segmentation in computed tomography scans remains a challenge due to the presence of artefacts and the varying densities of soft tissues and adjacent organs. This paper compares artificial intelligence algorithms and traditional medical image processing techniques to assist radiologists in liver segmentation in computed tomography scans and evaluates their accuracy and efficiency. Despite notable progress in the field, the limited availability of public datasets remains a significant barrier to broad participation in research studies and replication of methodologies. Future directions should focus on increasing the accessibility of public datasets, establishing standardised evaluation metrics, and advancing the development of three-dimensional segmentation techniques. In addition, maintaining a collaborative relationship between technological advances and medical expertise is essential to ensure that these innovations not only achieve technical accuracy, but also remain aligned with clinical needs and realities. This synergy ensures their applicability and effectiveness in real-world healthcare environments. Full article
(This article belongs to the Special Issue Biomedical Sensing and Bioinformatics Processing)
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