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Advanced Sensing Technologies in E-Health: Trends and Challenges
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Advanced Sensing Technologies in E-Health: Trends and Challenges

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

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 7082

Special Issue Editor

Dr. Muhammad Azizur Rahman

E-Mail Website
Guest Editor
Department of Computer Science, Cardiff Metropolitan University, Cardiff, UK
Interests: Electronic Health Records (EHL); health data analytics; healthcare domain; machine learning; network simulation methods and tools; modelling; communications software engineering; NS2/NAM; formal ontologies

Special Issue Information

Dear Colleagues,

Advanced Sensing Technologies in E-Health is a comprehensive exploration of the dynamic intersection of electronic health (e-health) and the integration of cutting-edge sensing technologies within the healthcare domain. For instance, wearable health devices like smartwatches and fitness trackers serve as prime examples of advanced sensing technologies. These devices continuously monitor vital signs such as heart rate, sleep patterns, and physical activity, delivering real-time data to users and healthcare professionals. Such data enables early detection of anomalies, empowering individuals to take proactive steps to maintain their health. This special issue explores the dynamic landscape of electronic health (e-health) and the integration of cutting-edge sensing technologies into healthcare. This dedicated issue digs in the challenges and opportunities presented by the proliferation of advanced sensors and data-driven healthcare solutions.

Prominent themes in the special issue include the need for data privacy and security in a connected healthcare environment, the potential for wearable and implantable sensors in remote patient monitoring, and the role of artificial intelligence in data analysis and decision-making. It addresses the regulatory hurdles, interoperability concerns, and ethical considerations associated with advanced sensing technologies in healthcare.

The key topics of interest include (but are not limited to):

  • E-Health
  • Healthcare Data
  • Machine learning, deep learning, and big data analytics in E-Health
  • Ethical Considerations
  • Sensing Technologies in E-Health
  • Healthcare Data Privacy
  • Remote Patient Monitoring
  • wearable sensors in healthcare
  • Artificial Intelligence in Healthcare
  • Healthcare Regulatory Challenges
  • Interoperability of Healthcare Data

Dr. Muhammad Azizur Rahman
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. 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

  • E-Health
  • healthcare data
  • sensing technologies in E-Health

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

Published Papers (4 papers)

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Research

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26 pages, 2093 KiB  
Article
Ultrasound Versus Elastography in the Diagnosis of Hepatic Steatosis: Evaluation of Traditional Machine Learning Versus Deep Learning
by Rodrigo Marques, Jaime Santos, Alexandra André and José Silva
Sensors 2024, 24(23), 7568; https://doi.org/10.3390/s24237568 - 27 Nov 2024
Viewed by 951
Abstract
The prevalence of fatty liver disease is on the rise, posing a significant global health concern. If left untreated, it can progress into more serious liver diseases. Therefore, accurately diagnosing the condition at an early stage is essential for more effective intervention and [...] Read more.
The prevalence of fatty liver disease is on the rise, posing a significant global health concern. If left untreated, it can progress into more serious liver diseases. Therefore, accurately diagnosing the condition at an early stage is essential for more effective intervention and management. This study uses images acquired via ultrasound and elastography to classify liver steatosis using classical machine learning classifiers, including random forest and support vector machine, as well as deep learning architectures, such as ResNet50V2 and DenseNet-201. The neural network demonstrated the most optimal performance, achieving an F1 score of 99.5% on the ultrasound dataset, 99.2% on the elastography dataset, and 98.9% on the mixed dataset. The results from the deep learning approach are comparable to those of machine learning, despite objectively not achieving the highest results. This research offers valuable insights into the domain of medical image classification and advocates the integration of advanced machine learning and deep learning technologies in diagnosing steatosis. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in E-Health: Trends and Challenges)
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17 pages, 496 KiB  
Article
Harnessing the Heart’s Magnetic Field for Advanced Diagnostic Techniques
by Tarek Elfouly and Ali Alouani
Sensors 2024, 24(18), 6017; https://doi.org/10.3390/s24186017 - 18 Sep 2024
Cited by 2 | Viewed by 2185
Abstract
Heart diseases remain one of the leading causes of morbidity and mortality worldwide, necessitating innovative diagnostic methods for early detection and intervention. An electrocardiogram (ECG) is a well-known technique for the preliminary diagnosis of heart conditions. However, it can not be used for [...] Read more.
Heart diseases remain one of the leading causes of morbidity and mortality worldwide, necessitating innovative diagnostic methods for early detection and intervention. An electrocardiogram (ECG) is a well-known technique for the preliminary diagnosis of heart conditions. However, it can not be used for continuous monitoring due to skin irritation. It is well known that every body organ generates a magnetic field, and the heart generates peak amplitudes of about 10 to 100 pT (measured at a distance of about 3 cm above the chest). This poses challenges to capturing such signals. This paper reviews the different techniques used to capture the heart’s magnetic signals along with their limitations. In addition, this paper provides a comprehensive review of the different approaches that use the heart-generated magnetic field to diagnose several heart diseases. This research reveals two aspects. First, as a noninvasive tool, the use of the heart’s magnetic field signal can lead to more sensitive advanced heart disease diagnosis tools, especially when continuous monitoring is possible and affordable. Second, its current use is limited due to the lack of accurate, affordable, and portable sensing technology. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in E-Health: Trends and Challenges)
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Review

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69 pages, 2881 KiB  
Review
Exploring Components, Sensors, and Techniques for Cancer Detection via eNose Technology: A Systematic Review
by Washington Ramírez, Verónica Pillajo, Eileen Ramírez, Ibeth Manzano and Doris Meza
Sensors 2024, 24(23), 7868; https://doi.org/10.3390/s24237868 - 9 Dec 2024
Cited by 2 | Viewed by 1300
Abstract
This paper offers a systematic review of advancements in electronic nose technologies for early cancer detection with a particular focus on the detection and analysis of volatile organic compounds present in biomarkers such as breath, urine, saliva, and blood. Our objective is to [...] Read more.
This paper offers a systematic review of advancements in electronic nose technologies for early cancer detection with a particular focus on the detection and analysis of volatile organic compounds present in biomarkers such as breath, urine, saliva, and blood. Our objective is to comprehensively explore how these biomarkers can serve as early indicators of various cancers, enhancing diagnostic precision and reducing invasiveness. A total of 120 studies published between 2018 and 2023 were examined through systematic mapping and literature review methodologies, employing the PICOS (Population, Intervention, Comparison, Outcome, and Study design) methodology to guide the analysis. Of these studies, 65.83% were ranked in Q1 journals, illustrating the scientific rigor of the included research. Our review synthesizes both technical and clinical perspectives, evaluating sensor-based devices such as gas chromatography–mass spectrometry and selected ion flow tube–mass spectrometry with reported incidences of 30 and 8 studies, respectively. Key analytical techniques including Support Vector Machine, Principal Component Analysis, and Artificial Neural Networks were identified as the most prevalent, appearing in 22, 24, and 13 studies, respectively. While substantial improvements in detection accuracy and sensitivity are noted, significant challenges persist in sensor optimization, data integration, and adaptation into clinical settings. This comprehensive analysis bridges existing research gaps and lays a foundation for the development of non-invasive diagnostic devices. By refining detection technologies and advancing clinical applications, this work has the potential to transform cancer diagnostics, offering higher precision and reduced reliance on invasive procedures. Our aim is to provide a robust knowledge base for researchers at all experience levels, presenting insights on sensor capabilities, metrics, analytical methodologies, and the transformative impact of emerging electronic nose technologies in clinical practice. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in E-Health: Trends and Challenges)
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Other

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12 pages, 473 KiB  
Perspective
Feasibility of Observing Cerebrovascular Disease Phenotypes with Smartphone Monitoring: Study Design Considerations for Real-World Studies
by Stephanie J. Zawada, Ali Ganjizadeh, Clint E. Hagen, Bart M. Demaerschalk and Bradley J. Erickson
Sensors 2024, 24(11), 3595; https://doi.org/10.3390/s24113595 - 2 Jun 2024
Cited by 2 | Viewed by 1687
Abstract
Accelerated by the adoption of remote monitoring during the COVID-19 pandemic, interest in using digitally captured behavioral data to predict patient outcomes has grown; however, it is unclear how feasible digital phenotyping studies may be in patients with recent ischemic stroke or transient [...] Read more.
Accelerated by the adoption of remote monitoring during the COVID-19 pandemic, interest in using digitally captured behavioral data to predict patient outcomes has grown; however, it is unclear how feasible digital phenotyping studies may be in patients with recent ischemic stroke or transient ischemic attack. In this perspective, we present participant feedback and relevant smartphone data metrics suggesting that digital phenotyping of post-stroke depression is feasible. Additionally, we proffer thoughtful considerations for designing feasible real-world study protocols tracking cerebrovascular dysfunction with smartphone sensors. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in E-Health: Trends and Challenges)
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