Signals in Health Care and Monitoring

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 (20 June 2023) | Viewed by 24438

Special Issue Editors


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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
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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

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Guest Editor
Nursing and Physiotherapy Department, Faculty of Health Sciences, University of Castilla-La Mancha, 45600 Talavera de la Reina, Toledo, Spain
Interests: diabetic foot; risk foot; medical thermography; infrared thermography; health sciences; surgery
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China
Interests: biomedical signal processing

Special Issue Information

Dear Colleagues,

Disease diagnostics and risk evaluation are often based on signal analysis. Biomedical signal processing has a privileged importance in our daily lives as a powerful tool in the diagnostic and treatment process of innumerous health conditions. In most cases, the acquisition tools are non-invasive and simple to use in hospitals and healthcare centres. The acquired signals are typically submitted to an array of processing algorithms either online or offline, which then undergo a thorough validation process, before being merged in commercial systems for healthcare. ECG, EEG, EMG, TAC and NMR are examples of signals that are studied, modelled and used to make predictions and recommend medical procedures.

With these ideas in mind, we welcome novel research works on signals in all biomedical signal processing fields, including the following:

  • Electrocardiography;
  • Electroencephalography;
  • Evoked potentials;
  • Sleep spindles;
  • Epilepsy;
  • Diabetes;
  • COVID-19;
  • Signals in Inflammation
  • Biomedical image processing;
  • Computer vision
  • Brain–computer interface;
  • Electromyography;
  • Neuroprosthetics;
  • Sleep classification;
  • Electrogastrography;
  • Electroenterography;
  • Electrocystography;
  • Electrohysterography;
  • Anal sphincter electromyography;

Prof. Dr. Manuel Ortigueira
Prof. Dr. Carla M.A. Pinto
Prof. Dr. Arnaldo Guimarães Batista
Prof. Dr. Álvaro Astasio Picado
Dr. Jia Chen
Guest Editors

Manuscript Submission Information

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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 and tumor modelling
  • AIDS and non-AIDS related tumors modelling
  • diabetes
  • COVID-19

Published Papers (6 papers)

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Research

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18 pages, 5550 KiB  
Article
Smart Piezoelectric-Based Wearable System for Calorie Intake Estimation Using Machine Learning
by Ghulam Hussain, Bander Ali Saleh Al-rimy, Saddam Hussain, Abdullah M. Albarrak, Sultan Noman Qasem and Zeeshan Ali
Appl. Sci. 2022, 12(12), 6135; https://doi.org/10.3390/app12126135 - 16 Jun 2022
Cited by 8 | Viewed by 2138
Abstract
Eating an appropriate food volume, maintaining the required calorie count, and making good nutritional choices are key factors for reducing the risk of obesity, which has many consequences such as Osteoarthritis (OA) that affects the patient’s knee. In this paper, we present a [...] Read more.
Eating an appropriate food volume, maintaining the required calorie count, and making good nutritional choices are key factors for reducing the risk of obesity, which has many consequences such as Osteoarthritis (OA) that affects the patient’s knee. In this paper, we present a wearable sensor in the form of a necklace embedded with a piezoelectric sensor, that detects skin movement from the lower trachea while eating. In contrast to the previous state-of-the-art piezoelectric sensor-based system that used spectral features, our system fully exploits temporal amplitude-varying signals for optimal features, and thus classifies foods more accurately. Through evaluation of the frame length and the position of swallowing in the frame, we found the best performance was with a frame length of 30 samples (1.5 s), with swallowing located towards the end of the frame. This demonstrates that the chewing sequence carries important information for classification. Additionally, we present a new approach in which the weight of solid food can be estimated from the swallow count, and the calorie count of food can be calculated from their estimated weight. Our system based on a smartphone app helps users live healthily by providing them with real-time feedback about their ingested food types, volume, and calorie count. Full article
(This article belongs to the Special Issue Signals in Health Care and Monitoring)
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12 pages, 1322 KiB  
Article
3D Virtual Modeling for Morphological Characterization of Pituitary Tumors: Preliminary Results on Its Predictive Role in Tumor Resection Rate
by Laura Cercenelli, Matteo Zoli, Barbara Bortolani, Nico Curti, Davide Gori, Arianna Rustici, Diego Mazzatenta and Emanuela Marcelli
Appl. Sci. 2022, 12(9), 4275; https://doi.org/10.3390/app12094275 - 23 Apr 2022
Cited by 1 | Viewed by 1535
Abstract
Among potential factors affecting the surgical resection in pituitary tumors, the role of tumor three-dimensional (3D) features is still unexplored. The aim of this study is to introduce the use of 3D virtual modeling for geometrical and morphological characterization of pituitary tumors and [...] Read more.
Among potential factors affecting the surgical resection in pituitary tumors, the role of tumor three-dimensional (3D) features is still unexplored. The aim of this study is to introduce the use of 3D virtual modeling for geometrical and morphological characterization of pituitary tumors and to evaluate its role as a predictor of total tumor removal. A total of 75 patients operated for a pituitary tumor have been retrospectively reviewed. Starting from patient imaging, a 3D tumor model was reconstructed, and 3D characterization based on tumor volume (Vol), area, sphericity (Spher), and convexity (Conv) was provided. The extent of tumor removal was then evaluated at post-operative imaging. Mean values were obtained for Vol (9117 ± 8423 mm3), area (2352 ± 1571 mm2), Spher (0.86 ± 0.08), and Conv (0.88 ± 0.08). Total tumor removal was achieved in 57 (75%) cases. The standard prognostic Knosp grade, Vol, and Conv were found to be independent factors, significantly predicting the extent of tumor removal. Total tumor resection correlated with lower Knosp grades (p = 0.032) and smaller Vol (p = 0.015). Conversely, tumors with a more irregular shape (low Conv) have an increased chance of incomplete tumor removal (p = 0.022). 3D geometrical and morphological features represent significant independent prognostic factors for pituitary tumor resection, and they should be considered in pre-operative planning to allow a more accurate decision-making process. Full article
(This article belongs to the Special Issue Signals in Health Care and Monitoring)
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10 pages, 3274 KiB  
Communication
Improving Image Quality Assessment Based on the Combination of the Power Spectrum of Fingerprint Images and Prewitt Filter
by Ting-Wei Shen, Ching-Chuan Li, Wan-Fu Lin, Yu-Hao Tseng, Wen-Fang Wu, Sean Wu, Zong-Liang Tseng and Mao-Hsiu Hsu
Appl. Sci. 2022, 12(7), 3320; https://doi.org/10.3390/app12073320 - 24 Mar 2022
Cited by 3 | Viewed by 1948
Abstract
The assessment of fingerprint image quality is critical for most fingerprint applications. It has an impact on the performance and compatibility of fingerprint recognition, authentication, and built-in cryptosystems. This paper developed an improved fingerprint image quality assessment derived from the image power spectrum [...] Read more.
The assessment of fingerprint image quality is critical for most fingerprint applications. It has an impact on the performance and compatibility of fingerprint recognition, authentication, and built-in cryptosystems. This paper developed an improved fingerprint image quality assessment derived from the image power spectrum approach and combined it with the Prewitt filter and an improved weighting method. The conventional image power spectrum approach and our proposed approach were implemented for accuracy and reliability tests using good, faulty, and blurred fingerprint images. The experimental results showed the proposed algorithm accurately identified the sharpness of fingerprint images and improved the average difference in FIQMs to 61% between three different levels of blurred fingerprints compared with that achieved by a conventional algorithm. Full article
(This article belongs to the Special Issue Signals in Health Care and Monitoring)
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13 pages, 2181 KiB  
Article
A LIME-Based Explainable Machine Learning Model for Predicting the Severity Level of COVID-19 Diagnosed Patients
by Freddy Gabbay, Shirly Bar-Lev, Ofer Montano and Noam Hadad
Appl. Sci. 2021, 11(21), 10417; https://doi.org/10.3390/app112110417 - 05 Nov 2021
Cited by 24 | Viewed by 3897
Abstract
The fast and seemingly uncontrollable spread of the novel coronavirus disease (COVID-19) poses great challenges to an already overloaded health system worldwide. It thus exemplifies an urgent need for fast and effective triage. Such triage can help in the implementation of the necessary [...] Read more.
The fast and seemingly uncontrollable spread of the novel coronavirus disease (COVID-19) poses great challenges to an already overloaded health system worldwide. It thus exemplifies an urgent need for fast and effective triage. Such triage can help in the implementation of the necessary measures to prevent patient deterioration and conserve strained hospital resources. We examine two types of machine learning models, a multilayer perceptron artificial neural networks and decision trees, to predict the severity level of illness for patients diagnosed with COVID-19, based on their medical history and laboratory test results. In addition, we combine the machine learning models with a LIME-based explainable model to provide explainability of the model prediction. Our experimental results indicate that the model can achieve up to 80% prediction accuracy for the dataset we used. Finally, we integrate the explainable machine learning models into a mobile application to enable the usage of the proposed models by medical staff worldwide. Full article
(This article belongs to the Special Issue Signals in Health Care and Monitoring)
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Review

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17 pages, 4899 KiB  
Review
Therapeutic Targets in the Virological Mechanism and in the Hyperinflammatory Response of Severe Acute Respiratory Syndrome Coronavirus Type 2 (SARS-CoV-2)
by Álvaro Astasio-Picado, María del Carmen Zabala-Baños and Jesús Jurado-Palomo
Appl. Sci. 2023, 13(7), 4471; https://doi.org/10.3390/app13074471 - 31 Mar 2023
Cited by 1 | Viewed by 1570
Abstract
This work is a bibliographic review. The search for the necessary information was carried out in the months of November 2022 and January 2023. The databases used were as follows: Pubmed, Academic Google, Scielo, Scopus, and Cochrane library. Results: In total, 101 articles [...] Read more.
This work is a bibliographic review. The search for the necessary information was carried out in the months of November 2022 and January 2023. The databases used were as follows: Pubmed, Academic Google, Scielo, Scopus, and Cochrane library. Results: In total, 101 articles were selected after a review of 486 articles from databases and after applying the inclusion and exclusion criteria. The update on the molecular mechanism of human coronavirus (HCoV) infection was reviewed, describing possible therapeutic targets in the viral response phase. There are different strategies to prevent or hinder the introduction of the viral particle, as well as the replicative mechanism ((protease inhibitors and RNA-dependent RNA polymerase (RdRp)). The second phase of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) involves the activation of hyperinflammatory cascades of the host’s immune system. It is concluded that there are potential therapeutic targets and drugs under study in different proinflammatory pathways such as hydroxychloroquine, JAK inhibitors, interleukin 1 and 6 inhibitors, and interferons. Full article
(This article belongs to the Special Issue Signals in Health Care and Monitoring)
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20 pages, 1589 KiB  
Review
Phobia Exposure Therapy Using Virtual and Augmented Reality: A Systematic Review
by Ghaida Albakri, Rahma Bouaziz, Wallaa Alharthi, Slim Kammoun, Mohammed Al-Sarem, Faisal Saeed and Mohammed Hadwan
Appl. Sci. 2022, 12(3), 1672; https://doi.org/10.3390/app12031672 - 05 Feb 2022
Cited by 22 | Viewed by 11745
Abstract
A specific phobia is a common anxiety-related disorder that can be treated efficiently using different therapies including exposure therapy or cognitive therapy. One of the most famous methods to treat a specific phobia is exposure therapy. Exposure therapy involves exposing the target patient [...] Read more.
A specific phobia is a common anxiety-related disorder that can be treated efficiently using different therapies including exposure therapy or cognitive therapy. One of the most famous methods to treat a specific phobia is exposure therapy. Exposure therapy involves exposing the target patient to the anxiety source or its context without the intention to cause any danger. One promising track of research lies in VR exposure therapy (VRET) and/or AR exposure therapy (ARET), where gradual exposure to a negative stimulus is used to reduce anxiety. In order to review existing works in this field, a systematic search was completed using the following databases: PubMed, ProQuest, Scopus, Web of Science, and Google Scholar. All studies that present VRET and/or ARET solutions were selected. By reviewing the article, each author then applied the inclusion and exclusion criteria, and 18 articles were selected. This systematic review aims to investigate the previous studies that used either VR and/or AR to treat any type of specific phobia in the last five years. The results demonstrated a positive outcome of virtual reality exposure treatment in the treatment of most phobias. In contrast, some of these treatments did not work for a few specific phobias in which the standard procedures were more effective. Besides, the study will also discuss the best of both technologies to treat a specific phobia. Furthermore, this review will present the limitations and future enhancements in this field. Full article
(This article belongs to the Special Issue Signals in Health Care and Monitoring)
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