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Algorithms
Special Issues
Advancements in Signal Processing and Machine Learning for Healthcare
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Advancements in Signal Processing and Machine Learning for Healthcare

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 2565

Special Issue Editors

Dr. Shaoxiong Sun

E-Mail Website
Guest Editor
Department of Computer Science, The University of Sheffield, Sheffield S10 2TN, UK
Interests: health data science; bedside and remote patient monitoring; wearable computing; cardiovascular monitoring
Dr. Nur Ahmadi

E-Mail Website
Guest Editor
School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132, Indonesia
Interests: biomedical signal processing; AI for healthcare; wearable and contactless vital sign monitoring; brain-machine interfaces; biomedical circuits and systems
Dr. Wei Lu

E-Mail Website
Guest Editor
Australian E-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD 4074, Australia
Interests: medical informatics; smart homes; fall detection; sensor technologies for healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research in the development of signal processing and machine learning algorithms to this Special Issue, “Advancements in Signal Processing and Machine Learning for Healthcare”. This Special Issue aims to highlight the latest innovations and breakthroughs at the intersection of signal processing, machine learning, and healthcare, with a particular emphasis on advancing the fields of health monitoring, diagnosis, and personalized care. As the integration of technology into healthcare continues to evolve, signal processing and machine learning techniques play increasingly pivotal roles in extracting meaningful insights from complex healthcare data, ultimately leading to improved patient outcomes and enhanced quality of care. Topics of interest include but are not limited to physiological signal processing, wearable devices, remote monitoring systems, predictive modeling, medical imaging analysis, clinical decision support systems, and personalized medicine. We welcome submissions that address challenges in data acquisition, processing, analysis, interpretation, and decision making, with the overarching goals of advancing state-of-the-art healthcare technology and transforming the delivery of healthcare services.

Dr. Shaoxiong Sun
Dr. Nur Ahmadi
Dr. Wei Lu
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. Algorithms 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 1600 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 signal processing
  • machine learning
  • wearable devices/computing
  • medical imaging
  • contact/contactless monitoring
  • sensor fusion
  • health informatics
  • remote patient monitoring
  • disease modeling
  • health data science

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

Published Papers (3 papers)

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16 pages, 2365 KiB  
Article
Using Coherent Hemodynamic Spectroscopy Model to Investigate Cardiac Arrest
by Vladislav Toronov, Nima Soltani, Leeanne Leung, Rohit Mohindra and Steve Lin
Algorithms 2025, 18(3), 128; https://doi.org/10.3390/a18030128 - 25 Feb 2025
Viewed by 404
Abstract
The Coherent Hemodynamic Spectroscopy (CHS) model provides a quantitative framework for modeling cerebral hemodynamics and metabolism, particularly in response to small physiological perturbations. However, in its original approximate formulation it was limited to conditions where parameter changes were constrained to 10–20%, making it [...] Read more.
The Coherent Hemodynamic Spectroscopy (CHS) model provides a quantitative framework for modeling cerebral hemodynamics and metabolism, particularly in response to small physiological perturbations. However, in its original approximate formulation it was limited to conditions where parameter changes were constrained to 10–20%, making it unsuitable for modeling extreme physiological disruptions such as cardiac arrest. In this study, we present a detailed discussion of the algorithm using the complete CHS model, which extends the original framework by solving partial differential equations without approximations to handle large non-periodic perturbations. This model was applied to data from a previously published cardiac arrest and cardiopulmonary resuscitation (CPR) study in pigs, where cerebral blood flow changed by 100%. While our prior work demonstrated the utility of this approach for analyzing cerebral microvascular and metabolic parameters, it did not include the algorithmic details necessary for reproducibility and broader application. Here, we address this gap by describing the algorithm’s workflow, including the use of non-linear multivariate optimization, and its ability to recover multiple physiological variables, such as the capillary and venule oxygen saturations, and parameters, such as the capillary oxygen diffusion rate, and arterial oxygen saturation. The latter can be valuable when the pulse oximetry measurements are unavailable due to unstable, weak or absent pulse. This study underscores the importance of non-linear modeling in advancing the application of CHS to extreme physiological conditions and highlights its potential for translational research and clinical innovation. Full article
(This article belongs to the Special Issue Advancements in Signal Processing and Machine Learning for Healthcare)
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16 pages, 794 KiB  
Article
A Machine Learning Approach to Identifying Risk Factors for Long COVID-19
by Rhea Machado, Reshen Soorinarain Dodhy, Atharve Sehgal, Kate Rattigan, Aparna Lalwani and David Waynforth
Algorithms 2024, 17(11), 485; https://doi.org/10.3390/a17110485 - 28 Oct 2024
Cited by 1 | Viewed by 1014
Abstract
Long-term sequelae of coronavirus disease 2019 (COVID-19) infection are common and can have debilitating consequences. There is a need to understand risk factors for Long COVID-19 to give impetus to the development of targeted yet holistic clinical and public health interventions to reduce [...] Read more.
Long-term sequelae of coronavirus disease 2019 (COVID-19) infection are common and can have debilitating consequences. There is a need to understand risk factors for Long COVID-19 to give impetus to the development of targeted yet holistic clinical and public health interventions to reduce its associated healthcare and economic burden. Given the large number and variety of predictors implicated spanning health-related and sociodemographic factors, machine learning becomes a valuable tool. As such, this study aims to employ machine learning to produce an algorithm to predict Long COVID-19 risk, and thereby identify key predisposing factors. Longitudinal cohort data were sourced from the UK’s “Understanding Society: COVID-19 Study” (n = 601 participants with past symptomatic COVID-19 infection confirmed by serology testing). The random forest classification algorithm demonstrated good overall performance with 97.4% sensitivity and modest specificity (65.4%). Significant risk factors included early timing of acute COVID-19 infection in the pandemic, greater number of hours worked per week, older age and financial insecurity. Loneliness and having uncommon health conditions were associated with lower risk. Sensitivity analysis suggested that COVID-19 vaccination is also associated with lower risk, and asthma with an increased risk. The results are discussed with emphasis on evaluating the value of machine learning; potential clinical utility; and some benefits and limitations of machine learning for health science researchers given its availability in commonly used statistical software. Full article
(This article belongs to the Special Issue Advancements in Signal Processing and Machine Learning for Healthcare)
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12 pages, 5052 KiB  
Protocol
Automated Measurement of Grid Cell Firing Characteristics
by Nate M. Sutton, Blanca E. Gutiérrez-Guzmán, Holger Dannenberg and Giorgio A. Ascoli
Algorithms 2025, 18(3), 139; https://doi.org/10.3390/a18030139 - 3 Mar 2025
Viewed by 408
Abstract
We describe GridMet as open-source software that automatically measures the spatial tuning parameters of grid cells, such as firing field size, spacing, and orientation angles. Applying these metrics to experimental data can help quantify changes in the geometric characteristics of grid cell firing [...] Read more.
We describe GridMet as open-source software that automatically measures the spatial tuning parameters of grid cells, such as firing field size, spacing, and orientation angles. Applying these metrics to experimental data can help quantify changes in the geometric characteristics of grid cell firing across experimental conditions. GridMet uses clustering and other advanced methods to detect and characterize fields, increasing accuracy compared to alternative methods such as those based on peak firing. Novel contributions of this work include an effective approach for automated field size estimation and an original method for estimating field spacing that can overcome challenges encountered in other software. The user-friendly yet flexible design of GridMet aims to facilitate widespread community adoption. Specifically, GridMet allows basic usage with default parameter settings while also enabling the expert configuration of many parameter values for more advanced applications. Free release of the MATLAB source code will encourage the development of custom variations or integration with other software packages. At the same time, we also provide a runtime version of GridMet, thus avoiding the requirement to purchase any separate licenses. We have optimized GridMet for batch scripting workflows to aid investigations of multi-trial data on multiple grid cells. Full article
(This article belongs to the Special Issue Advancements in Signal Processing and Machine Learning for Healthcare)
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