Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Signals in Health Care
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Signals in Health Care

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (15 July 2019) | Viewed by 27793

Special Issue Editors

Dr. Manuel Duarte Ortigueira

E-Mail Website
Guest Editor
Centre of Technology and Systems-UNINOVA, NOVA School of Science and Technology, NOVA University of Lisbon, Quinta da Torre, 2829-516 Caparica, Portugal
Interests: signal processing; fractional signals and systems; EEG and ECG processing
Special Issues, Collections and Topics in MDPI journals
Dr. Carla M. A. Pinto

E-Mail Website
Guest Editor
Instituto Superior de Engenharia do Porto, Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto, Portugal
Interests: epidemiological and within-host models; dynamical systems; fractional order models
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Arnaldo Guimarães Batista

E-Mail Website
Guest Editor
Center of Technology and Systems-UNINOVA and DEE/Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa Campus da FCT, Quinta da Torre, 2829-516 Caparica, Portugal
Interests: biomedical signal processing, wavelets, uterine electromyography signal processing, time-frequency analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical signals enjoy a place of privilege in our daily life. Disease risk evaluation and diagnostics are often based in the analysis of biomedical signals. Frequently, these diagnostic tools are non-invasive and simple to perform in hospitals and health centres. The acquired signals are then submitted to an array of processing algorithms either online or offline. These algorithms, which are the main core of these systems, undergo a validation process before being merged in commercial systems for the health care. Electrocardiography, electroencephalography, electromyography and computerized axial tomography are just examples of signals that are studied, modelled, and used to make predictions and suggest treatments.

Therefore, contributions regarding these signals are welcome:

  • Electrocardiography
  • Electroencephalography
  • Evoqued Potentials
  • Sleep Spindles
  • Epilepsy
  • Biomedical Image Processing
  • Brain-Computer Interface
  • Electromyography
  • Neuroprosthetics
  • Sleep Classification
  • Electrogastrography
  • Electroenterography
  • Electrocystography
  • Electrohysterography
  • Anal Sphincter Electromyography
  • Electrocorticography
Prof. Manuel Ortigueira
Prof. Dr. Carla Pinto
Prof. Dr. Arnaldo Guimarães Batista
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. Applied Sciences 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.

Keywords

  • biomedical signal processing 
  • biomedical image processing 
  • fluid modelling 
  • AIDS and non-AIDS related tumors modelling

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

15 pages, 1565 KiB  
Article
A Three-Class Classification of Cognitive Workload Based on EEG Spectral Data
by Małgorzata Plechawska-Wójcik, Mikhail Tokovarov, Monika Kaczorowska and Dariusz Zapała
Appl. Sci. 2019, 9(24), 5340; https://doi.org/10.3390/app9245340 - 6 Dec 2019
Cited by 48 | Viewed by 6002
Abstract
Evaluation of cognitive workload finds its application in many areas, from educational program assessment through professional driver health examination to monitoring the mental state of people carrying out jobs of high responsibility, such as pilots or airline traffic dispatchers. Estimation of multilevel cognitive [...] Read more.
Evaluation of cognitive workload finds its application in many areas, from educational program assessment through professional driver health examination to monitoring the mental state of people carrying out jobs of high responsibility, such as pilots or airline traffic dispatchers. Estimation of multilevel cognitive workload is a task usually realized in a subject-dependent way, while the present research is focused on developing the procedure of subject-independent evaluation of cognitive workload level. The aim of the paper is to estimate cognitive workload level in accordance with subject-independent approach, applying classical machine learning methods combined with feature selection techniques. The procedure of data acquisition was based on registering the EEG signal of the person performing arithmetical tasks divided into six intervals of advancement. The analysis included the stages of preprocessing, feature extraction, and selection, while the final step covered multiclass classification performed with several models. The results discussed show high maximal accuracies achieved: ~91% for both the validation dataset and for the cross-validation approach for kNN model. Full article
(This article belongs to the Special Issue Signals in Health Care)
►▼ Show Figures

Figure 1

28 pages, 5348 KiB  
Article
Enhancing Decision Making through Combined Classification Techniques and Probabilistic Data Analysis for Ubiquitous Healthcare Anomaly Monitoring
by Reda Chefira and Said Rakrak
Appl. Sci. 2019, 9(22), 4802; https://doi.org/10.3390/app9224802 - 10 Nov 2019
Cited by 3 | Viewed by 3215
Abstract
A multi-agent data-analytics-based approach to ubiquitous healthcare monitoring is presented in this paper. The proposed architecture gathers a patient’s vital data using wireless body area networks, and the transmitted information is separated into binary component parts and divided into related dataset categories using [...] Read more.
A multi-agent data-analytics-based approach to ubiquitous healthcare monitoring is presented in this paper. The proposed architecture gathers a patient’s vital data using wireless body area networks, and the transmitted information is separated into binary component parts and divided into related dataset categories using several classification techniques. A probabilistic procedure is then used that applies a normal (Gaussian) distribution to the analysis of new medical entries in order to assess the gravity of the anomalies detected. Finally, a data examination is carried out to gain insight. The results of the model and simulation show that the proposed architecture is highly efficient in applying smart technologies to a healthcare system, as an example of a research direction involving the Internet of Things, and offers a data platform that can be used for both medical decision making and the patient’s wellbeing and satisfaction with their medical treatment. Full article
(This article belongs to the Special Issue Signals in Health Care)
►▼ Show Figures

Figure 1

14 pages, 2809 KiB  
Article
Validating the Comparison Framework for the Finite Dimensions Model of Concentric Ring Electrodes Using Human Electrocardiogram Data
by Oleksandr Makeyev, Mark Musngi, Larry Moore, Yiyao Ye-Lin, Gema Prats-Boluda and Javier Garcia-Casado
Appl. Sci. 2019, 9(20), 4279; https://doi.org/10.3390/app9204279 - 12 Oct 2019
Cited by 7 | Viewed by 2261
Abstract
While progress has been made in design optimization of concentric ring electrodes maximizing the accuracy of the surface Laplacian estimation, it was based exclusively on the negligible dimensions model of the electrode. Recent proof of concept of the new finite dimensions model that [...] Read more.
While progress has been made in design optimization of concentric ring electrodes maximizing the accuracy of the surface Laplacian estimation, it was based exclusively on the negligible dimensions model of the electrode. Recent proof of concept of the new finite dimensions model that adds the radius of the central disc and the widths of concentric rings to the previously included number of rings and inter-ring distances provides an opportunity for more comprehensive design optimization. In this study, the aforementioned proof of concept was developed into a framework allowing direct comparison of any two concentric ring electrodes of the same size and with the same number of rings. The proposed framework is illustrated on constant and linearly increasing inter-ring distances tripolar concentric ring electrode configurations and validated on electrocardiograms from 20 human volunteers. In particular, ratios of truncation term coefficients between the two electrode configurations were used to demonstrate the similarity between the negligible and the finite dimension models analytically (p = 0.077). Laplacian estimates based on the two models were calculated on electrocardiogram data for emulation of linearly increasing inter-ring distances tripolar concentric ring electrode. The difference between the estimates was not statistically significant (p >> 0.05) which is consistent with the analytic result. Full article
(This article belongs to the Special Issue Signals in Health Care)
►▼ Show Figures

Figure 1

17 pages, 2683 KiB  
Article
Tonic Cold Pain Detection Using Choi–Williams Time-Frequency Distribution Analysis of EEG Signals: A Feasibility Study
by Rami Alazrai, Saifaldeen AL-Rawi, Hisham Alwanni and Mohammad I. Daoud
Appl. Sci. 2019, 9(16), 3433; https://doi.org/10.3390/app9163433 - 20 Aug 2019
Cited by 17 | Viewed by 3733
Abstract
Detecting pain based on analyzing electroencephalography (EEG) signals can enhance the ability of caregivers to characterize and manage clinical pain. However, the subjective nature of pain and the nonstationarity of EEG signals increase the difficulty of pain detection using EEG signals analysis. In [...] Read more.
Detecting pain based on analyzing electroencephalography (EEG) signals can enhance the ability of caregivers to characterize and manage clinical pain. However, the subjective nature of pain and the nonstationarity of EEG signals increase the difficulty of pain detection using EEG signals analysis. In this work, we present an EEG-based pain detection approach that analyzes the EEG signals using a quadratic time-frequency distribution, namely the Choi–Williams distribution (CWD). The use of the CWD enables construction of a time-frequency representation (TFR) of the EEG signals to characterize the time-varying spectral components of the EEG signals. The TFR of the EEG signals is analyzed to extract 12 time-frequency features for pain detection. These features are used to train a support vector machine classifier to distinguish between EEG signals that are associated with the no-pain and pain classes. To evaluate the performance of our proposed approach, we have recorded EEG signals for 24 healthy subjects under tonic cold pain stimulus. Moreover, we have developed two performance evaluation procedures—channel- and feature-based evaluation procedures—to study the effect of the utilized EEG channels and time-frequency features on the accuracy of pain detection. The experimental results show that our proposed approach achieved an average classification accuracy of 89.24% in distinguishing between the no-pain and pain classes. In addition, the classification performance achieved using our proposed approach outperforms the classification results reported in several existing EEG-based pain detection approaches. Full article
(This article belongs to the Special Issue Signals in Health Care)
►▼ Show Figures

Figure 1

18 pages, 2144 KiB  
Article
Estimation of Work of Breathing from Respiratory Muscle Activity In Spontaneous Ventilation: A Pilot Study
by Isabel Cristina Muñoz, Alher Mauricio Hernández and Miguel Ángel Mañanas
Appl. Sci. 2019, 9(10), 2007; https://doi.org/10.3390/app9102007 - 16 May 2019
Cited by 3 | Viewed by 2876
Abstract
Work of breathing (WOB) offers information that may be relevant to determine the patient’s status under spontaneous mechanical ventilation in Intensive Care Unit (ICU). Nowadays, the most reliable technique to measure WOB is based on the use of invasive catheters, but the use [...] Read more.
Work of breathing (WOB) offers information that may be relevant to determine the patient’s status under spontaneous mechanical ventilation in Intensive Care Unit (ICU). Nowadays, the most reliable technique to measure WOB is based on the use of invasive catheters, but the use of qualitative observations such as the level of dyspnea is preferred as a possible indicator of WOB level. In this pilot study, the activity of three respiratory muscles were recorded on healthy subjects through surface electromyography while they were under non-invasive mechanical ventilation, using restrictive and obstructive maneuvers to obtain different WOB levels. The respiratory pattern between restrictive and obstructive maneuvers was classified with the Nearest Neighbor Algorithm with a 91% accuracy and a neural network model helped classify the samples into three WOB levels with a 89% accuracy, Low: [0.3–0.8) J/L, Medium: [0.8–1.3] J/L and Elevated: (1.3–1.8] J/L, demonstrating the relationship between the respiratory muscle activity and WOB. This technique is a promising tool for the healthcare staff in the decision-making process when selecting the best ventilation settings to maintain a low WOB. This study identified a model to estimate the WOB in different ventilatory patterns, being an alternative to invasive conventional techniques. Full article
(This article belongs to the Special Issue Signals in Health Care)
►▼ Show Figures

Figure 1

17 pages, 2736 KiB  
Article
Effects of Symbol Component on the Identifying of Graphic Symbols from EEG for Young Children with and without Developmental Delays
by Chih-Hsiung Huang and Pei-Jung Lin
Appl. Sci. 2019, 9(6), 1260; https://doi.org/10.3390/app9061260 - 26 Mar 2019
Cited by 1 | Viewed by 4435
Abstract
Using Augmentative and Alternative Communication (AAC) to improve the communication skills of children with disabilities is generally supported by both domestic and foreign scholars. Graphic symbols that represent individual words or phrases are often used in conjunction with AAC; however, research on the [...] Read more.
Using Augmentative and Alternative Communication (AAC) to improve the communication skills of children with disabilities is generally supported by both domestic and foreign scholars. Graphic symbols that represent individual words or phrases are often used in conjunction with AAC; however, research on the reading and identifying of AAC graphic symbols is scant. Therefore, this study used electroencephalogram (EEG) to investigate the success rates of identifying AAC graphic symbols and brainwave changes of young children with and without developmental delays. The results revealed that the number of symbol components affected participants’ success rates of identifying AAC graphic symbols. The EEG Attention Index between the children with and without developmental delays varied during the test. By contrast, the EEG Relaxation Index exhibited no difference between the children with and without developmental delays. When the participants viewed the single-component animations, the children without developmental delays had a significantly higher Relaxation Index than those with developmental delays did. According to cognitive load theory, the children with developmental delays and low cognitive capacities may feel stressed. Full article
(This article belongs to the Special Issue Signals in Health Care)
►▼ Show Figures

Figure 1

Other

Jump to: Research

18 pages, 3900 KiB  
Technical Note
A Multiparameter Approach to Evaluate Post-Stroke Patients: An Application on Robotic Rehabilitation
by Antonella Belfatto, Alessandro Scano, Andrea Chiavenna, Alfonso Mastropietro, Simona Mrakic-Sposta, Simone Pittaccio, Lorenzo Molinari Tosatti, Franco Molteni and Giovanna Rizzo
Appl. Sci. 2018, 8(11), 2248; https://doi.org/10.3390/app8112248 - 14 Nov 2018
Cited by 27 | Viewed by 4102
Abstract
Multidomain instrumental evaluation of post-stroke chronic patients, coupled with standard clinical assessments, has rarely been exploited in the literature. Such an approach may be valuable to provide comprehensive insight regarding patients’ status, as well as orienting the rehabilitation therapies. Therefore, we propose a [...] Read more.
Multidomain instrumental evaluation of post-stroke chronic patients, coupled with standard clinical assessments, has rarely been exploited in the literature. Such an approach may be valuable to provide comprehensive insight regarding patients’ status, as well as orienting the rehabilitation therapies. Therefore, we propose a multidomain analysis including clinically compliant methods as electroencephalography (EEG), electromyography (EMG), kinematics, and clinical scales. The framework of upper-limb robot-assisted rehabilitation is selected as a challenging and promising scenario to test the multi-parameter evaluation, with the aim to assess whether and in which domains modifications may take place. Instrumental recordings and clinical scales were administered before and after a month of intensive robotic therapy of the impaired upper limb, on five post-stroke chronic hemiparetic patients. After therapy, all patients showed clinical improvement and presented pre/post modifications in one or several of the other domains as well. All patients performed the motor task in a smoother way; two of them appeared to change their muscle synergies activation strategies, and most subjects showed variations in their brain activity, both in the ipsi- and contralateral hemispheres. Changes highlighted by the new multiparametric instrumental approach suggest a recovery trend in agreement with clinical scales. In addition, by jointly demonstrating lateralization of brain activations, changes in muscle recruitment and the execution of smoother trajectories, the new approach may help distinguish between true functional recovery and the adoption of suboptimal compensatory strategies. In the light of these premises, the multi-domain approach may allow a finer patient characterization, providing a deeper insight into the mechanisms underlying the relearning procedure and the level (neuro/muscular) at which it occurred, at a relatively low expenditure. The role of this quantitative description in defining a personalized treatment strategy is of great interest and should be addressed in future studies. Full article
(This article belongs to the Special Issue Signals in Health Care)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop