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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 24614

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Special Issue Editors

Dr. Theodore Kotsilieris

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Department of Business and Organizations Administration, University of The Peloponnese, 24100 Kalamata, Greece
Interests: computer science; medical informatics; information systems; artificial intelligence; e-learning
Special Issues, Collections and Topics in MDPI journals

Dr. Haralampos Karanikas

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Guest Editor

Department of Computer Science and Biomedical Informatics, University of Thessaly, 351-00 Lamia, Greece
Interests: health care systems; eHealth; telehealth; health data exchange; patient registries; medical prescription protocols; electronic health record; data & text mining

Dr. Thanasis Tsanas

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Guest Editor

Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh EH8 9YL, UK
Interests: time-series analysis; signal processing; pattern recognition; statistical machine learning; biomedical applications
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ioannis Anagnostopoulos

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Guest Editor

Department of Computer Science and Biomedical Informatics, University of Thessaly, 351-00 Lamia, Greece
Interests: internet technologies; health information systems; data management in bioinformatics; semantic interoperability; linked data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the implications of computer science, data analysis, and healthcare for society’s advancement and well-being are inexpugnable, health information technology constitutes an evolving research field that introduces several challenges and opportunities for the development of innovative healthcare services. Health applications and medical information systems have been established towards delivering high-quality, effective, and efficient health services that aim to make treatment efforts proactive, safer, and less expensive. Furthermore, last decade’s economic recession and the COVID-19 pandemic have highlighted the necessity to strengthen health applications’ data governance and access potential as well as medical information systems’ interoperability and planning in terms of satisfaction, performance, and usefulness.

The objective of this Special Issue is to provide an interdisciplinary platform where researchers will share knowledge and success stories, present ambitious system designs, and demonstrate state-of-the-art developments of emerging eHealth applications and medical information systems. Submissions are expected to include information and assessment metrics for the quantitative or qualitative evaluation of various health information technologies that will depict their impact. Research topics of interest include, but are not limited to, the following:

Telemedicine, telehealth, and telecare;
Evidence-based evaluation of eHealth interventions;
Ambient-assisted living and patient empowerment systems;
Smart sensors for eHealth;
Clinical decision-making support, and smart ePrescription;
Health data and text mining;
Artificial intelligence for eHealth;
Social media and online social networks for healthcare support;
Mobile healthcare applications and pervasive technologies;
Personalized medicine;
Big data and data management;
Wellness and prevention interventions;
Evaluation and modeling of healthcare service and mobile app usage;
Health information exchange and interoperability challenges related to EHRs and patient registries;
Public health informatics and population health;
Health data strategies and architectures.

Dr. Theodore Kotsilieris
Dr. Haralampos Karanikas
Dr. Thanasis Tsanas
Prof. Dr. Ioannis Anagnostopoulos
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. Electronics 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 2400 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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Published Papers (11 papers)

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Editorial

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6 pages, 192 KiB

Open AccessEditorial

Special Issue: Emerging E-Health Applications and Medical Information Systems

by Theodore Kotsilieris, Haralampos Karanikas, Athanasios Tsanas and Ioannis Anagnostopoulos

Electronics 2024, 13(13), 2470; https://doi.org/10.3390/electronics13132470 - 24 Jun 2024

Viewed by 753

Abstract

Over the last few decades, research on e-Health applications and medical information systems has significantly grown over time due to the need to reinforce health applications’ governance and access potential as well as medical information systems’ interoperability, satisfaction, performance, and usefulness [...] Full article

(This article belongs to the Special Issue Emerging E-health Applications and Medical Information Systems)

Research

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14 pages, 1837 KiB

Open AccessArticle

Predicting Gait Parameters of Leg Movement with sEMG and Accelerometer Using CatBoost Machine Learning

by Alok Kumar Sharma, Shing-Hong Liu, Xin Zhu and Wenxi Chen

Electronics 2024, 13(9), 1791; https://doi.org/10.3390/electronics13091791 - 6 May 2024

Cited by 1 | Viewed by 1163

Abstract

This study aims to evaluate leg movement by integrating gait analysis with surface electromyography (sEMG) and accelerometer (ACC) data from the lower limbs. We employed a wireless, self-made, and multi-channel measurement system in combination with commercial GaitUp Physilog^® 5 shoe-worn inertial sensors to record the walking patterns and muscle activations of 17 participants. This approach generated a comprehensive dataset comprising 1452 samples. To accurately predict gait parameters, a machine learning model was developed using features extracted from the sEMG signals of thigh and calf muscles, and ACCs from both legs. The study utilized evaluation metrics including accuracy (R²), Pearson correlation coefficient (PCC), root mean squared error (RMSE), mean absolute percentage error (MAPE), mean squared error (MSE), and mean absolute error (MAE) to evaluate the performance of the proposed model. The results highlighted the superiority of the CatBoost model over alternatives like XGBoost and Decision Trees. The CatBoost’s average PCCs for 17 temporospatial gait parameters of the left and right legs are 0.878 ± 0.169 and 0.921 ± 0.047, respectively, with MSE of 7.65, RMSE of 1.48, MAE of 1.00, MAPE of 0.03, and Accuracy (R²-Score) of 0.91. This research marks a significant advancement by providing a more comprehensive method for detecting and analyzing gait statuses. Full article

(This article belongs to the Special Issue Emerging E-health Applications and Medical Information Systems)

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12 pages, 4038 KiB

Open AccessArticle

VaccineHero: An Extended Reality System That Reduces Toddlers’ Discomfort during Vaccination

by Stavros Antonopoulos, Vasiliki Rentoula, Manolis Wallace, Vassilis Poulopoulos and Georgios Lepouras

Electronics 2023, 12(18), 3851; https://doi.org/10.3390/electronics12183851 - 12 Sep 2023

Viewed by 1699

Abstract

In this work, we present VaccineHero, an extended reality system that aims to reduce children’s discomfort during vaccination. In our proposal, the child wears a headset during vaccination and watches a VR short story. The story includes a hero touching the child’s arm, and the doctor synchronises the insertion of the needle with the VR content so that the child is efficiently distracted. A clinical trial has been carried out involving two doctors and a cohort of 16 children, which showed that the use of VaccineHero reduces children’s discomfort during vaccination by a staggering 40% and completely eliminates extreme discomfort. The implemented solution is extremely affordable, as it can be deployed on simple headsets or even Cardboard devices, a feature that makes it a realistic option for any paediatric practice. It can also be extended to support other medical activities that involve needles, such as blood drawing and blood donation. Full article

(This article belongs to the Special Issue Emerging E-health Applications and Medical Information Systems)

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15 pages, 4299 KiB

Open AccessArticle

Explainable Feature Extraction and Prediction Framework for 3D Image Recognition Applied to Pneumonia Detection

by Emmanuel Pintelas, Ioannis E. Livieris and Panagiotis Pintelas

Electronics 2023, 12(12), 2663; https://doi.org/10.3390/electronics12122663 - 14 Jun 2023

Cited by 7 | Viewed by 1856

Abstract

Explainable machine learning is an emerging new domain fundamental for trustworthy real-world applications. A lack of trust and understanding are the main drawbacks of deep learning models when applied to real-world decision systems and prediction tasks. Such models are considered as black boxes because they are unable to explain the reasons for their predictions in human terms; thus, they cannot be universally trusted. In critical real-world applications, such as in medical, legal, and financial ones, an explanation of machine learning (ML) model decisions is considered crucially significant and mandatory in order to acquire trust and avoid fatal ML bugs, which could disturb human safety, rights, and health. Nevertheless, explainable models are more than often less accurate; thus, it is essential to invent new methodologies for creating interpretable predictors that are almost as accurate as black-box ones. In this work, we propose a novel explainable feature extraction and prediction framework applied to 3D image recognition. In particular, we propose a new set of explainable features based on mathematical and geometric concepts, such as lines, vertices, contours, and the area size of objects. These features are calculated based on the extracted contours of every 3D input image slice. In order to validate the efficiency of the proposed approach, we apply it to a critical real-world application: pneumonia detection based on CT 3D images. In our experimental results, the proposed white-box prediction framework manages to achieve a performance similar to or marginally better than state-of-the-art 3D-CNN black-box models. Considering the fact that the proposed approach is explainable, such a performance is particularly significant. Full article

(This article belongs to the Special Issue Emerging E-health Applications and Medical Information Systems)

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21 pages, 2657 KiB

Open AccessArticle

Automated Multimodal Stress Detection in Computer Office Workspace

by Thelma Androutsou, Spyridon Angelopoulos, Evangelos Hristoforou, George K. Matsopoulos and Dimitrios D. Koutsouris

Electronics 2023, 12(11), 2528; https://doi.org/10.3390/electronics12112528 - 3 Jun 2023

Cited by 4 | Viewed by 1742

Abstract

Nowadays, changes in the conditions and nature of the workplace make it imperative to create unobtrusive systems for the automatic detection of occupational stress, which can be feasibly addressed through the adoption of Internet of Things (IoT) technologies and advances in data analysis. This paper presents the development of a multimodal automated stress detection system in an office environment that utilizes measurements derived from individuals’ interactions with the computer and its peripheral units. In our analysis, behavioral parameters of computer keyboard and mouse dynamics are combined with physiological parameters recorded by sensors embedded in a custom-made smart computer mouse device. To validate the system, we designed and implemented an experimental protocol simulating an office environment and included the most known work stressors. We applied known classifiers and different data labeling methods to the physiological and behavioral parameters extracted from the collected data, resulting in high-performance metrics. The feature-level fusion analysis of physiological and behavioral parameters successfully detected stress with an accuracy of 90.06% and F1 score of 0.90. The decision-level fusion analysis, combining the features extracted from both the computer mouse and keyboard, showed an average accuracy of 66% and an average F1 score of 0.56. Full article

(This article belongs to the Special Issue Emerging E-health Applications and Medical Information Systems)

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16 pages, 7953 KiB

Open AccessArticle

A Projected AR Serious Game for Shoulder Rehabilitation Using Hand-Finger Tracking and Performance Metrics: A Preliminary Study on Healthy Subjects

by Rosanna M. Viglialoro, Giuseppe Turini, Marina Carbone, Sara Condino, Virginia Mamone, Nico Coluccia, Stefania Dell’Agli, Gabriele Morucci, Larisa Ryskalin, Vincenzo Ferrari and Marco Gesi

Electronics 2023, 12(11), 2516; https://doi.org/10.3390/electronics12112516 - 2 Jun 2023

Cited by 4 | Viewed by 1860

Abstract

Research studies show that serious games can increase patient motivation regardless of age or illness and be an affordable and promising solution with respect to conventional physiotherapy. In this paper, we present the latest evolution of our system for shoulder rehabilitation based on hand-finger tracking and projected augmented reality. This version integrates metrics to assess patient performance, monitors the game progress, and allows the selection of the game visualization mode (standard on-screen or projected augmented reality). Additionally, the new software tracks the velocity, acceleration, and normalized jerk of the arm-hand movements of the user. Specifically, sixteen healthy volunteers (eight technical and eight rehabilitation experts) tested our current prototype. The results showed that the serious game is engaging, its design is ergonomically sound, and the overall system could be a useful tool in shoulder rehabilitation. However, clinical validation is needed to assess that the serious game has the same effects as the selected therapy. This is the preliminary step toward laying the foundation for future studies that investigate abnormalities in shoulder movements by using hand-finger tracking. Full article

(This article belongs to the Special Issue Emerging E-health Applications and Medical Information Systems)

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18 pages, 1129 KiB

Open AccessArticle

InSEption: A Robust Mechanism for Predicting FoG Episodes in PD Patients

by Dimitris Dimoudis, Nikos Tsolakis, Christoniki Magga-Nteve, Georgios Meditskos, Stefanos Vrochidis and Ioannis Kompatsiaris

Electronics 2023, 12(9), 2088; https://doi.org/10.3390/electronics12092088 - 3 May 2023

Cited by 3 | Viewed by 2175

Abstract

The integration of IoT and deep learning provides the opportunity for continuous monitoring and evaluation of patients’ health status, leading to more personalized treatment and improved quality of life. This study explores the potential of deep learning to predict episodes of freezing of gait (FoG) in Parkinson’s disease (PD) patients. Initially, a literature review was conducted to determine the state of the art; then, two inception-based models, namely LN-Inception and InSEption, were introduced and tested using the Daphnet dataset and an additional novel medium-sized dataset collected from an IMU (inertial measuring unit) sensor. The results show that both models performed very well, outperforming or achieving performance comparable to the state-of-the-art. In particular, the InSEption network showed exceptional performance, achieving a 6% increase in macro F1 score compared to the inception-only-based counterpart on the Daphnet dataset. In a newly introduced IMU dataset, InSEption scored 97.2% and 98.6% in terms of F1 and AUC, respectively. This can be attributed to the added squeeze and excitation blocks and the domain-specific oversampling methods used for training. The benefits of using the Inception mechanism for signal data and its potential for integration into wearable IoT are validated. Full article

(This article belongs to the Special Issue Emerging E-health Applications and Medical Information Systems)

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17 pages, 4069 KiB

Open AccessArticle

Preventing Keratoconus through Eye Rubbing Activity Detection: A Machine Learning Approach

by George Nokas and Theodore Kotsilieris

Electronics 2023, 12(4), 1028; https://doi.org/10.3390/electronics12041028 - 18 Feb 2023

Cited by 6 | Viewed by 2447

Abstract

Keratoconus is a non-inflammatory disease of the eyes diagnosed in more than 1/2000 people, making it significantly common. Among others, eye rubbing has been identified as a risk factor for the development of keratoconus. The severity of the disease strongly depends on the frequency and force of eye rubbing. Vast research efforts have focused on diagnosing keratoconus through the application of artificial intelligence techniques over optical coherence tomography images and corneal measurements. However, to the best of the authors’ knowledge, no studies have been conducted which provide an eye rubbing detection and alert mechanism for keratoconus prevention. This study intends to help close this research gap. An inertial measurement unit that is dedicated to collecting hand motion data and machine learning techniques are jointly employed for the early detection of potential problems and complications. Four conventional classification methods (support vector machines, decision trees, random forest, and XGBoost) were evaluated and compared. All methods attain high-quality accuracy results, with SVMs, RF, and XGBoost slightly outperforming DTs. As the results reveal, the performance of all methods is remarkable, allowing the integration of such a solution in wearable devices such as smartwatches to be considered for the early detection of eye rubbing and keratoconus prevention. Full article

(This article belongs to the Special Issue Emerging E-health Applications and Medical Information Systems)

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12 pages, 914 KiB

Open AccessArticle

Evaluation of the Predictive Ability and User Acceptance of Panoramix 2.0, an AI-Based E-Health Tool for the Detection of Cognitive Impairment

by Sonia Valladares-Rodríguez, Manuel J. Fernández-Iglesias, Luis E. Anido-Rifón and Moisés Pacheco-Lorenzo

Electronics 2022, 11(21), 3424; https://doi.org/10.3390/electronics11213424 - 22 Oct 2022

Cited by 4 | Viewed by 2123

Abstract

The high prevalence of Alzheimer-type dementia and the limitations of traditional neuropsychological tests motivate the introduction of new cognitive assessment methods. We discuss the validation of an all-digital, ecological and non-intrusive e-health application for the early detection of cognitive impairment, based on artificial intelligence for patient classification, and more specifically on machine learning algorithms. To evaluate the discrimination power of this application, a cross-sectional pilot study was carried out involving 30 subjects: 10 health control subjects (mean age: 75.62 years); 14 individuals with mild cognitive impairment (mean age: 81.24 years) and 6 early-stage Alzheimer’s patients (mean age: 80.44 years). The study was carried out in two separate sessions in November 2021 and January 2022. All participants completed the study, and no concerns were raised about the acceptability of the test. Analysis including socio-demographics and game data supports the prediction of participants’ cognitive status using machine learning algorithms. According to the performance metrics computed, best classification results are obtained a Multilayer Perceptron classifier, Support Vector Machines and Random Forest, respectively, with weighted recall values >= 0.9784 ± 0.0265 and F1-score = 0.9764 ± 0.0291. Furthermore, thanks to hyper-parameter optimization, false negative rates were dramatically reduced. Shapley’s additive planning (SHAP) applied according to the eXplicable AI (XAI) method, made it possible to visually and quantitatively evaluate the importance of the different features in the final classification. This is a relevant step ahead towards the use of machine learning and gamification to early detect cognitive impairment. In addition, this tool was designed to support self-administration, which could be a relevant aspect in confinement situations with limited access to health professionals. However, further research is required to identify patterns that may help to predict or estimate future cognitive damage and normative data. Full article

(This article belongs to the Special Issue Emerging E-health Applications and Medical Information Systems)

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17 pages, 1872 KiB

Open AccessArticle

Respiration-Based COPD Detection Using UWB Radar Incorporation with Machine Learning

by Hafeez Ur Rehman Siddiqui, Adil Ali Saleem, Imran Bashir, Kainat Zafar, Furqan Rustam, Isabel de la Torre Diez, Sandra Dudley and Imran Ashraf

Electronics 2022, 11(18), 2875; https://doi.org/10.3390/electronics11182875 - 11 Sep 2022

Cited by 15 | Viewed by 3011

Abstract

COPD is a progressive disease that may lead to death if not diagnosed and treated at an early stage. The examination of vital signs such as respiration rate is a promising approach for the detection of COPD. However, simultaneous consideration of the demographic and medical characteristics of patients is very important for better results. The objective of this research is to investigate the capability of UWB radar as a non-invasive approach to discriminate COPD patients from healthy subjects. The non-invasive approach is beneficial in pandemics such as the ongoing COVID-19 pandemic, where a safe distance between people needs to be maintained. The raw data are collected in a real environment (a hospital) non-invasively from a distance of 1.5 m. Respiration data are then extracted from the collected raw data using signal processing techniques. It was observed that the respiration rate of COPD patients alone is not enough for COPD patient detection. However, incorporating additional features such as age, gender, and smoking history with the respiration rate lead to robust performance. Different machine-learning classifiers, including Naïve Bayes, support vector machine, random forest, k nearest neighbor (KNN), Adaboost, and two deep-learning models—a convolutional neural network and a long short-term memory (LSTM) network—were utilized for COPD detection. Experimental results indicate that LSTM outperforms all employed models and obtained 93% accuracy. Performance comparison with existing studies corroborates the superior performance of the proposed approach. Full article

(This article belongs to the Special Issue Emerging E-health Applications and Medical Information Systems)

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Review

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18 pages, 846 KiB

Open AccessReview

Telemedicine and Robotic Surgery: A Narrative Review to Analyze Advantages, Limitations and Future Developments

by Paola Picozzi, Umberto Nocco, Greta Puleo, Chiara Labate and Veronica Cimolin

Electronics 2024, 13(1), 124; https://doi.org/10.3390/electronics13010124 - 28 Dec 2023

Cited by 4 | Viewed by 4144

Abstract

Today, the introduction and application of telemedicine are leading to a radical transformation in healthcare systems all over the world. In particular, the use of information and communication technologies (ICT) can have a positive impact on the containment of healthcare costs. The concept of telemedicine has also been applied to surgery, defining telesurgery as the use of robotic systems composed of one or more arms controlled via a console located in a remote position from the patient, where the surgeon sits and performs the surgical tasks. This revolution—made possible by technological advances in robotic systems and ICT—allows surgical care to be provided to patients in remote locations. Telesurgery, therefore, adds to the advantages of minimally invasive robotic surgery by overcoming geographical barriers and allowing patients to avoid traveling. Although there has been a rapid increase in interest and demand for telesurgery, its use in clinical practice is still rare. The purpose of this article is to review the advantages and benefits of the use of telesurgery, to identify the limitations that do not yet allow its use in current clinical practice, and to describe the existing challenges and possible solutions that are being explored by research. Full article

(This article belongs to the Special Issue Emerging E-health Applications and Medical Information Systems)

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