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Sensors
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Biomedical Signals, Images and Healthcare Data Analysis: 2nd Edition
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Biomedical Signals, Images and Healthcare Data Analysis: 2nd Edition

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

Deadline for manuscript submissions: 20 March 2026 | Viewed by 2226

Special Issue Editors

Dr. Shivaram Poigai Arunachalam

E-Mail Website
Guest Editor
Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, USA
Interests: physiology; clinical medicine; biomedical engineering; clinical imaging; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Kanchan Kulkarni

E-Mail Website
Guest Editor
1. IHU-LIRYC, Heart Rhythm Disease Institute, Fondation Bordeaux Université, 33600 Bordeaux, France
2. INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, University of Bordeaux, 33000 Bordeaux, France
Interests: cardiology; smart health; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Anup Kumar Mishra

E-Mail Website
Guest Editor
GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
Interests: machine learning; artificial intelligence; fall detection; pancreatic cancer; cancer; assistive living
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the digital health era revolutionizing patient care, health systems are focusing on identifying areas of improvement for both clinical practice changes as well as improved patient outcomes using biomedical data. In recent years, there has been an exponential growth in healthcare data, ranging from Electronic Health Records (EHR), medical imaging data, and data from in-home and in-hospital health tracking and diagnostic sensors. Healthcare data analytics have been shown to improve patient outcomes, such as reducing mortality and providing opportunities for personalized and early interventions, and to have operational benefits, such as identifying waste and optimizing healthcare spending. However, there are several unmet clinical needs and the utilization of biomedical data is still sub-optimal. Therefore, we have an opportunity to build efficient biomedical data-driven digital tools to improve patient care. Advanced biomedical data processing and analytics techniques leveraging the potential of artificial intelligence (AI), cloud computing, data mining, and data visualization, in addition to a multidisciplinary collaborative research environment with clinicians, are essential to enhance the ability of healthcare providers to optimize care delivery and improve patient outcomes. In the future, novel data analysis methods will be crucial to help transform healthcare systems from a reactive, treatment-based approach to a more integrated, preventive model. Novel machine learning-based clinical decision tools will become inevitable for an enriched healthcare system to provide much-needed care to patients in a timely fashion.

This Special Issue will provide an opportunity to showcase novel methodological innovation and translational efforts related to the analysis of various healthcare data, including EHR data, biomedical signals, and imaging data, to enhance patient care.

Dr. Shivaram Poigai Arunachalam
Dr. Kanchan Kulkarni
Dr. Anup Kumar Mishra
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.

Keywords

  • biomedical signals
  • imaging data
  • healthcare data analysis

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Related Special Issue

Published Papers (2 papers)

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Research

16 pages, 7956 KB  
Article
Development and Evaluation of a Keypoint-Based Video Stabilization Pipeline for Oral Capillaroscopy
by Vito Gentile, Vincenzo Taormina, Luana Conte, Giorgio De Nunzio, Giuseppe Raso and Donato Cascio
Sensors 2025, 25(18), 5738; https://doi.org/10.3390/s25185738 - 15 Sep 2025
Abstract
Capillaroscopy imaging is a non-invasive technique used to examine the microcirculation of the oral mucosa. However, the acquired video sequences are often affected by motion noise and shaking, which can compromise diagnostic accuracy and hinder the development of automated systems for capillary identification [...] Read more.
Capillaroscopy imaging is a non-invasive technique used to examine the microcirculation of the oral mucosa. However, the acquired video sequences are often affected by motion noise and shaking, which can compromise diagnostic accuracy and hinder the development of automated systems for capillary identification and segmentation. To address these challenges, we implemented a comprehensive video stabilization model, structured as a multi-phase pipeline and visually represented through a flow-chart. The proposed method integrates keypoint extraction, optical flow estimation, and affine transformation-based frame alignment to enhance video stability. Within this framework, we evaluated the performance of three keypoint extraction algorithms—Scale-Invariant Feature Transform (SIFT), Oriented FAST and Rotated BRIEF (ORB) and Good Features to Track (GFTT)—on a curated dataset of oral capillaroscopy videos. To simulate real-world acquisition conditions, synthetic tremors were introduced via Gaussian affine transformations. Experimental results demonstrate that all three algorithms yield comparable stabilization performance, with GFTT offering slightly higher structural fidelity and ORB excelling in computational efficiency. These findings validate the effectiveness of the proposed model and highlight its potential for improving the quality and reliability of oral videocapillaroscopy imaging. Experimental evaluation showed that the proposed pipeline achieved an average SSIM of 0.789 and reduced jitter to 25.8, compared to the perturbed input sequences. In addition, path smoothness and RMS errors (translation and rotation) consistently indicated improved stabilization across all tested feature extractors. Compared to previous stabilization approaches in nailfold capillaroscopy, our method achieved comparable or superior structural fidelity while maintaining computational efficiency. Full article
(This article belongs to the Special Issue Biomedical Signals, Images and Healthcare Data Analysis: 2nd Edition)
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13 pages, 769 KB  
Article
A Novel You Only Listen Once (YOLO) Deep Learning Model for Automatic Prominent Bowel Sounds Detection: Feasibility Study in Healthy Subjects
by Rohan Kalahasty, Gayathri Yerrapragada, Jieun Lee, Keerthy Gopalakrishnan, Avneet Kaur, Pratyusha Muddaloor, Divyanshi Sood, Charmy Parikh, Jay Gohri, Gianeshwaree Alias Rachna Panjwani, Naghmeh Asadimanesh, Rabiah Aslam Ansari, Swetha Rapolu, Poonguzhali Elangovan, Shiva Sankari Karuppiah, Vijaya M. Dasari, Scott A. Helgeson, Venkata S. Akshintala and Shivaram P. Arunachalam
Sensors 2025, 25(15), 4735; https://doi.org/10.3390/s25154735 - 31 Jul 2025
Cited by 1 | Viewed by 1868
Abstract
Accurate diagnosis of gastrointestinal (GI) diseases typically requires invasive procedures or imaging studies that pose the risk of various post-procedural complications or involve radiation exposure. Bowel sounds (BSs), though typically described during a GI-focused physical exam, are highly inaccurate and variable, with low [...] Read more.
Accurate diagnosis of gastrointestinal (GI) diseases typically requires invasive procedures or imaging studies that pose the risk of various post-procedural complications or involve radiation exposure. Bowel sounds (BSs), though typically described during a GI-focused physical exam, are highly inaccurate and variable, with low clinical value in diagnosis. Interpretation of the acoustic characteristics of BSs, i.e., using a phonoenterogram (PEG), may aid in diagnosing various GI conditions non-invasively. Use of artificial intelligence (AI) and improvements in computational analysis can enhance the use of PEGs in different GI diseases and lead to a non-invasive, cost-effective diagnostic modality that has not been explored before. The purpose of this work was to develop an automated AI model, You Only Listen Once (YOLO), to detect prominent bowel sounds that can enable real-time analysis for future GI disease detection and diagnosis. A total of 110 2-minute PEGs sampled at 44.1 kHz were recorded using the Eko DUO® stethoscope from eight healthy volunteers at two locations, namely, left upper quadrant (LUQ) and right lower quadrant (RLQ) after IRB approval. The datasets were annotated by trained physicians, categorizing BSs as prominent or obscure using version 1.7 of Label Studio Software®. Each BS recording was split up into 375 ms segments with 200 ms overlap for real-time BS detection. Each segment was binned based on whether it contained a prominent BS, resulting in a dataset of 36,149 non-prominent segments and 6435 prominent segments. Our dataset was divided into training, validation, and test sets (60/20/20% split). A 1D-CNN augmented transformer was trained to classify these segments via the input of Mel-frequency cepstral coefficients. The developed AI model achieved area under the receiver operating curve (ROC) of 0.92, accuracy of 86.6%, precision of 86.85%, and recall of 86.08%. This shows that the 1D-CNN augmented transformer with Mel-frequency cepstral coefficients achieved creditable performance metrics, signifying the YOLO model’s capability to classify prominent bowel sounds that can be further analyzed for various GI diseases. This proof-of-concept study in healthy volunteers demonstrates that automated BS detection can pave the way for developing more intuitive and efficient AI-PEG devices that can be trained and utilized to diagnose various GI conditions. To ensure the robustness and generalizability of these findings, further investigations encompassing a broader cohort, inclusive of both healthy and disease states are needed. Full article
(This article belongs to the Special Issue Biomedical Signals, Images and Healthcare Data Analysis: 2nd Edition)
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