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Bioengineering
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The Power of Biosignal and Bioimage Processing in Human Healthcare:...
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The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems (Volume II)

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (17 March 2023) | Viewed by 12374

Special Issue Editors

Dr. Alfonso Maria Ponsiglione

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Guest Editor
Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, 80125 Naples, Italy
Interests: nanomedicine; microfluidics; drug delivery systems; biomaterials
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Francesco Amato

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80134 Naples, Italy
Interests: applications of systems and control theory to bioengineering; computational biology; modeling and control of biomedical devices; computational analysis and robust control of dynamic systems in biomedical engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maria Romano

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80134 Naples, Italy
Interests: processing of biomedical signals and data; biomedical imaging; statistical and nonlinear biomedical signal analysis and processing; electrocardiography; heart rate variability; electromyography; electrical impedance spectroscopy; healthcare management; telemedicine
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Giovanni Improta

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Guest Editor
Department of Public Health, University of Naples Federico II, 80134 Naples, Italy
Interests: bioengineering; public health; healthcare decision making; machine learning and data mining for healthcare; modeling and analysis of biomedical data; health technology assessment; quality improvement in healthcare; lean six sigma; biomedical signal processing and analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are grateful to all authors, reviewers and readers for their responses to the first volume of our Special Issue on "The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems". You can access these articles for free via the link:

The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems (Volume I)

Biosignals are generated by mostly vital physiologic phenomena and provide valuable information regarding the status and function of a biological system. Being time or space–time records of biological events, biosignals, as well as bioimages, come from diversified sources (such as cardiovascular, muscular, respiratory, cerebral, etc.) and find a wide range of applications: from the identification of digital biomarkers and indicators of health outcome to the design of biosensors, wearable devices, and computational tools aimed at detecting, processing, and analyzing biomedical signals and images.

The growth in these research fields, together with the rapid development of new artificial intelligence techniques, have further boosted interest toward the study and use of biosignals and bioimages to give insights into the complex dynamics involved in the control of physiological systems in both healthy and diseased states.

In this regard, it becomes extremely important to develop robust methodological approaches and innovative algorithms and tools to enhance the clinical value of biosignals and biomages in order to take advantage of their full potential in health monitoring, enriching the information that can be extracted from them and used in the healthcare context.

We, therefore, invite you to submit original research papers and comprehensive reviews on advances in biosignal and bioimage processing, from acquisition to theoretical analysis, computational simulations, and clinical applications.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • New approaches for biosignal/bioimage processing and analysis;
  • Methods for the classification of biosignals;
  • Biosignal/bioimage feature extraction;
  • Algorithms to improve the quality of biosignals;
  • Artificial Intelligence tools for biosignal/bioimage analysis;
  • Modeling and simulation of physiological signals;
  • Biosensors and wearable devices for biosignal acquisition and measurements;
  • Biomedical signal-based biomarkers.

Your contributions will help to improve and advance methodologies for biomedical signal and image processing with key implications in the fields of medicine and healthcare.

Dr. Alfonso Maria Ponsiglione
Prof. Dr. Francesco Amato
Prof. Dr. Maria Romano
Prof. Dr. Giovanni Improta
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. Bioengineering 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 2700 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

  • biosignals
  • bioimages
  • biomedical signal/image processing and analysis
  • artificial intelligence for biosignal/bioimage analysis and classification
  • pattern recognition
  • feature extraction
  • biosensors
  • wearable devices
  • modeling and simulation of biosignals

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

Published Papers (5 papers)

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Research

29 pages, 1413 KiB  
Article
A General Approach for the Modelling of Negative Feedback Physiological Control Systems
by Alfonso Maria Ponsiglione, Francesco Montefusco, Leandro Donisi, Annarita Tedesco, Carlo Cosentino, Alessio Merola, Maria Romano and Francesco Amato
Bioengineering 2023, 10(7), 835; https://doi.org/10.3390/bioengineering10070835 - 14 Jul 2023
Cited by 2 | Viewed by 1794
Abstract
Mathematical models can improve the understanding of physiological systems behaviour, which is a fundamental topic in the bioengineering field. Having a reliable model enables researchers to carry out in silico experiments, which require less time and resources compared to their in vivo and [...] Read more.
Mathematical models can improve the understanding of physiological systems behaviour, which is a fundamental topic in the bioengineering field. Having a reliable model enables researchers to carry out in silico experiments, which require less time and resources compared to their in vivo and in vitro counterparts. This work’s objective is to capture the characteristics that a nonlinear dynamical mathematical model should exhibit, in order to describe physiological control systems at different scales. The similarities among various negative feedback physiological systems have been investigated and a unique general framework to describe them has been proposed. Within such a framework, both the existence and stability of equilibrium points are investigated. The model here introduced is based on a closed-loop topology, on which the homeostatic process is based. Finally, to validate the model, three paradigmatic examples of physiological control systems are illustrated and discussed: the ultrasensitivity mechanism for achieving homeostasis in biomolecular circuits, the blood glucose regulation, and the neuromuscular reflex arc (also referred to as muscle stretch reflex). The results show that, by a suitable choice of the modelling functions, the dynamic evolution of the systems under study can be described through the proposed general nonlinear model. Furthermore, the analysis of the equilibrium points and dynamics of the above-mentioned systems are consistent with the literature. Full article
(This article belongs to the Special Issue The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems (Volume II))
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16 pages, 3020 KiB  
Article
Quantification of the Phenomena Affecting Reflective Arterial Photoplethysmography
by Georgios Rovas, Vasiliki Bikia and Nikolaos Stergiopulos
Bioengineering 2023, 10(4), 460; https://doi.org/10.3390/bioengineering10040460 - 10 Apr 2023
Cited by 5 | Viewed by 2176
Abstract
Photoplethysmography (PPG) is a widely emerging method to assess vascular health in humans. The origins of the signal of reflective PPG on peripheral arteries have not been thoroughly investigated. We aimed to identify and quantify the optical and biomechanical processes that influence the [...] Read more.
Photoplethysmography (PPG) is a widely emerging method to assess vascular health in humans. The origins of the signal of reflective PPG on peripheral arteries have not been thoroughly investigated. We aimed to identify and quantify the optical and biomechanical processes that influence the reflective PPG signal. We developed a theoretical model to describe the dependence of reflected light on the pressure, flow rate, and the hemorheological properties of erythrocytes. To verify the theory, we designed a silicone model of a human radial artery, inserted it in a mock circulatory circuit filled with porcine blood, and imposed static and pulsatile flow conditions. We found a positive, linear relationship between the pressure and the PPG and a negative, non-linear relationship, of comparable magnitude, between the flow and the PPG. Additionally, we quantified the effects of the erythrocyte disorientation and aggregation. The theoretical model based on pressure and flow rate yielded more accurate predictions, compared to the model using pressure alone. Our results indicate that the PPG waveform is not a suitable surrogate for intraluminal pressure and that flow rate significantly affects PPG. Further validation of the proposed methodology in vivo could enable the non-invasive estimation of arterial pressure from PPG and increase the accuracy of health-monitoring devices. Full article
(This article belongs to the Special Issue The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems (Volume II))
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14 pages, 3134 KiB  
Article
Cuff-Less Blood Pressure Prediction Based on Photoplethysmography and Modified ResNet
by Caijie Qin, Yong Li, Chibiao Liu and Xibo Ma
Bioengineering 2023, 10(4), 400; https://doi.org/10.3390/bioengineering10040400 - 24 Mar 2023
Cited by 15 | Viewed by 2428
Abstract
Cardiovascular disease (CVD) has become a common health problem of mankind, and the prevalence and mortality of CVD are rising on a year-to-year basis. Blood pressure (BP) is an important physiological parameter of the human body and also an important physiological indicator for [...] Read more.
Cardiovascular disease (CVD) has become a common health problem of mankind, and the prevalence and mortality of CVD are rising on a year-to-year basis. Blood pressure (BP) is an important physiological parameter of the human body and also an important physiological indicator for the prevention and treatment of CVD. Existing intermittent measurement methods do not fully indicate the real BP status of the human body and cannot get rid of the restraining feeling of a cuff. Accordingly, this study proposed a deep learning network based on the ResNet34 framework for continuous prediction of BP using only the promising PPG signal. The high-quality PPG signals were first passed through a multi-scale feature extraction module after a series of pre-processing to expand the perceptive field and enhance the perception ability on features. Subsequently, useful feature information was then extracted by stacking multiple residual modules with channel attention to increase the accuracy of the model. Lastly, in the training stage, the Huber loss function was adopted to stabilize the iterative process and obtain the optimal solution of the model. On a subset of the MIMIC dataset, the errors of both SBP and DBP predicted by the model met the AAMI standards, while the accuracy of DBP reached Grade A of the BHS standard, and the accuracy of SBP almost reached Grade A of the BHS standard. The proposed method verifies the potential and feasibility of PPG signals combined with deep neural networks in the field of continuous BP monitoring. Furthermore, the method is easy to deploy in portable devices, and it is more consistent with the future trend of wearable blood-pressure-monitoring devices (e.g., smartphones and smartwatches). Full article
(This article belongs to the Special Issue The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems (Volume II))
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17 pages, 4287 KiB  
Article
Detection of Suspicious Cardiotocographic Recordings by Means of a Machine Learning Classifier
by Carlo Ricciardi, Francesco Amato, Annarita Tedesco, Donatella Dragone, Carlo Cosentino, Alfonso Maria Ponsiglione and Maria Romano
Bioengineering 2023, 10(2), 252; https://doi.org/10.3390/bioengineering10020252 - 15 Feb 2023
Cited by 6 | Viewed by 2838
Abstract
Cardiotocography (CTG) is one of the fundamental prenatal diagnostic methods for both antepartum and intrapartum fetal surveillance. Although it has allowed a significant reduction in intrapartum and neonatal mortality and morbidity, its diagnostic accuracy is, however, still far from being fully satisfactory. In [...] Read more.
Cardiotocography (CTG) is one of the fundamental prenatal diagnostic methods for both antepartum and intrapartum fetal surveillance. Although it has allowed a significant reduction in intrapartum and neonatal mortality and morbidity, its diagnostic accuracy is, however, still far from being fully satisfactory. In particular, the identification of uncertain and suspicious CTG traces remains a challenging task for gynecologists. The introduction of computerized analysis systems has enabled more objective evaluations, possibly leading to more accurate diagnoses. In this work, the problem of classifying suspicious CTG recordings was addressed through a machine learning approach. A machine-based labeling was proposed, and a binary classification was carried out using a support vector machine (SVM) classifier to distinguish between suspicious and normal CTG traces. The best classification metrics showed accuracy, sensitivity, and specificity values of 92%, 92%, and 90%, respectively. The main results were compared both with results obtained by considering a more unbalanced dataset and with relevant literature studies in the field. The use of the SVM proved to be promising in the field of CTG classification. However, appropriate feature selection and dataset balancing are crucial to achieve satisfactory performance of the classifier. Full article
(This article belongs to the Special Issue The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems (Volume II))
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21 pages, 640 KiB  
Article
Enhancing Electrocardiogram Classification with Multiple Datasets and Distant Transfer Learning
by Kwok Tai Chui, Brij B. Gupta, Mingbo Zhao, Areej Malibari, Varsha Arya, Wadee Alhalabi and Miguel Torres Ruiz
Bioengineering 2022, 9(11), 683; https://doi.org/10.3390/bioengineering9110683 - 11 Nov 2022
Cited by 4 | Viewed by 2069
Abstract
Electrocardiogram classification is crucial for various applications such as the medical diagnosis of cardiovascular diseases, the level of heart damage, and stress. One of the typical challenges of electrocardiogram classification problems is the small size of the datasets, which may lead to limitation [...] Read more.
Electrocardiogram classification is crucial for various applications such as the medical diagnosis of cardiovascular diseases, the level of heart damage, and stress. One of the typical challenges of electrocardiogram classification problems is the small size of the datasets, which may lead to limitation in the performance of the classification models, particularly for models based on deep-learning algorithms. Transfer learning has demonstrated effectiveness in transferring knowledge from a source model with a similar domain and can enhance the performance of the target model. Nevertheless, the consideration of datasets with similar domains restricts the selection of source domains. In this paper, electrocardiogram classification was enhanced by distant transfer learning where a generative-adversarial-network-based auxiliary domain with a domain-feature-classifier negative-transfer-avoidance (GANAD-DFCNTA) algorithm was proposed to bridge the knowledge transfer from distant sources to target domains. To evaluate the performance of the proposed algorithm, eight benchmark datasets were chosen, with four from electrocardiogram datasets and four from the following distant domains: ImageNet, COCO, WordNet, and Sentiment140. The results showed an average accuracy improvement of 3.67 to 4.89%. The proposed algorithm was also compared with existing works using traditional transfer learning, revealing an average accuracy improvement of 0.303–5.19%. Ablation studies confirmed the effectiveness of the components of GANAD-DFCNTA. Full article
(This article belongs to the Special Issue The Power of Biosignal and Bioimage Processing in Human Healthcare: Advances in the Analysis and Control of Physiological Systems (Volume II))
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