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Advanced Signal Processing and Human-Machine Interface for Healthcare Diagnostics and Bioengineering Applications

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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 13087

Special Issue Editors

Dr. Oana Geman

E-Mail Website1 Website2
Guest Editor
Associate Professor, Faculty of Electrical Engineering and Computer Science, "Stefan cel Mare" University of Suceava, Suceava, Romania
Interests: non-invasive measurements of biomedical signals; wireless sensors; signal processing; data mining; deep learning; intelligent systems; biomedical applications
Special Issues, Collections and Topics in MDPI journals
Dr. V. Dhilip Kumar

E-Mail Website
Guest Editor
Department of CSE, Vel Tech University, Chennai, India
Interests: IOT; vehicular communication; machine learning
Dr. Muhammad Arif

E-Mail Website
Guest Editor
Department of Computer Science and Information Technology University of Lahore, Lahore, Pakistan
Interests: cyber security; privacy; sensor networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advanced Signal Processing techniques are used in human–machine interface (HMI) applications to achieve real-time synchronised communication between the human body and machine functions. HMI technology not only allows for real-time control, but also allows for the simultaneous control of numerous functions with minimal human input and enhanced efficiency. Health monitoring, medical diagnostics, the creation of prosthetic and assistive devices, the automobile and aerospace industries, robotic controls, and many more applications benefit from HMI technologies. Various bioelectrical and physiological signals, signal acquisition and processing approaches, and their applications in various HMI systems have been explored.

Academic research regarding biomedical signals, image processing, and the HMI has been established as a dynamic area of expertise. Signal and image processing principles have been widely applied in the extraction of physiological data in a variety of clinical procedures for advanced medical practices and applications. The link between electrophysiological signals (such as an electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) signals) and functional image processing, as well as their interactions, has been examined. Diverse case studies, such as those on the topic of neurosciences, functional imaging, and the cardiovascular system, have been carried out by utilizing various algorithms and approaches. The interaction between data retrieved from numerous signals and modalities appears to be quite promising. Advanced algorithms and approaches for use in time-frequency representation-based information retrieval have been investigated. Finally, some techniques that effectively extract electrophysiological signals and functional pictures and have a major impact on many biomedical applications have been explored.

The objective of this Special Issue is to bring together innovative techniques and computer models for use in biomedical signals and medical image analysis. Explicitly, we aim to explore unique contributions that: (1) advance machine learning models to be used in bio-signal processing and medical image analysis for HMI models; (2) design novel bio-signal processes for healthcare applications; (3) contain reviews of recent developments in this field; and (4) contain the creation of standard datasets.

The themes of the Special Issue include, but are not constrained to, the following:

  • Bio-medical signal processing for healthcare diagnostics;
  • Deep learning models for the analysis of biomedical signal data;
  • Medical image analysis for HMI computational models;
  • The implementation of novel algorithms or methodology to analyze biomedical images;
  • The development of machine learning (ML) models to analyze advanced signal processing statistical data;
  • Simulation models to train various bio-medical signals for healthcare diagnostics;
  • The development of a Human Machine Interface (HMI) using bio-signal datasets;
  • The continuous monitoring and prediction of diseases using advanced bio-signal processing;
  • Classification of diseases using bio-medical imaging and advanced bio-signal processing;
  • Advanced signal processing and bioengineering applications.

Dr. Oana Geman
Dr. V. Dhilip Kumar
Dr. Muhammad Arif
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. 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

  • signal processing
  • human-machine interaction
  • healthcare
  • bioengineering
  • biomedical imaging
  • deep learning
  • machine learning
  • biomedical applications

Published Papers (3 papers)

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Research

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12 pages, 464 KiB  
Article
Machine Learning Model Validated to Predict Outcomes of Liver Transplantation Recipients with Hepatitis C: The Romanian National Transplant Agency Cohort Experience
by Mihai Lucian Zabara, Irinel Popescu, Alexandru Burlacu, Oana Geman, Radu Adrian Crisan Dabija, Iolanda Valentina Popa and Cristian Lupascu
Sensors 2023, 23(4), 2149; https://doi.org/10.3390/s23042149 - 14 Feb 2023
Cited by 1 | Viewed by 1847
Abstract
Background and Objectives: In the early period after liver transplantation, patients are exposed to a high rate of complications and several scores are currently available to predict adverse postoperative outcomes. However, an ideal, universally accepted and validated score to predict adverse events in [...] Read more.
Background and Objectives: In the early period after liver transplantation, patients are exposed to a high rate of complications and several scores are currently available to predict adverse postoperative outcomes. However, an ideal, universally accepted and validated score to predict adverse events in liver transplant recipients with hepatitis C is lacking. Therefore, we aimed to establish and validate a machine learning (ML) model to predict short-term outcomes of hepatitis C patients who underwent liver transplantation. Materials and Methods: We conducted a retrospective observational two-center cohort study involving hepatitis C patients who underwent liver transplantation. Based on clinical and laboratory parameters, the dataset was used to train a deep-learning model for predicting short-term postoperative complications (within one month following liver transplantation). Adverse events prediction in the postoperative setting was the primary study outcome. Results: A total of 90 liver transplant recipients with hepatitis C were enrolled in the present study, 80 patients in the training cohort and ten in the validation cohort, respectively. The age range of the participants was 12–68 years, 51 (56,7%) were male, and 39 (43.3%) were female. Throughout the 85 training epochs, the model achieved a very good performance, with the accuracy ranging between 99.76% and 100%. After testing the model on the validation set, the deep-learning classifier confirmed the performance in predicting postoperative complications, achieving an accuracy of 100% on unseen data. Conclusions: We successfully developed a ML model to predict postoperative complications following liver transplantation in hepatitis C patients. The model demonstrated an excellent performance for accurate adverse event prediction. Consequently, the present study constitutes the foundation for careful and non-invasive identification of high-risk patients who might benefit from a more intensive postoperative monitoring strategy. Full article
(This article belongs to the Special Issue Advanced Signal Processing and Human-Machine Interface for Healthcare Diagnostics and Bioengineering Applications)
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24 pages, 7900 KiB  
Article
Incorporating a Novel Dual Transfer Learning Approach for Medical Images
by Abdulrahman Abbas Mukhlif, Belal Al-Khateeb and Mazin Abed Mohammed
Sensors 2023, 23(2), 570; https://doi.org/10.3390/s23020570 - 04 Jan 2023
Cited by 15 | Viewed by 5138
Abstract
Recently, transfer learning approaches appeared to reduce the need for many classified medical images. However, these approaches still contain some limitations due to the mismatch of the domain between the source domain and the target domain. Therefore, this study aims to propose a [...] Read more.
Recently, transfer learning approaches appeared to reduce the need for many classified medical images. However, these approaches still contain some limitations due to the mismatch of the domain between the source domain and the target domain. Therefore, this study aims to propose a novel approach, called Dual Transfer Learning (DTL), based on the convergence of patterns between the source and target domains. The proposed approach is applied to four pre-trained models (VGG16, Xception, ResNet50, MobileNetV2) using two datasets: ISIC2020 skin cancer images and ICIAR2018 breast cancer images, by fine-tuning the last layers on a sufficient number of unclassified images of the same disease and on a small number of classified images of the target task, in addition to using data augmentation techniques to balance classes and to increase the number of samples. According to the obtained results, it has been experimentally proven that the proposed approach has improved the performance of all models, where without data augmentation, the performance of the VGG16 model, Xception model, ResNet50 model, and MobileNetV2 model are improved by 0.28%, 10.96%, 15.73%, and 10.4%, respectively, while, with data augmentation, the VGG16 model, Xception model, ResNet50 model, and MobileNetV2 model are improved by 19.66%, 34.76%, 31.76%, and 33.03%, respectively. The Xception model obtained the highest performance compared to the rest of the models when classifying skin cancer images in the ISIC2020 dataset, as it obtained 96.83%, 96.919%, 96.826%, 96.825%, 99.07%, and 94.58% for accuracy, precision, recall, F1-score, sensitivity, and specificity respectively. To classify the images of the ICIAR 2018 dataset for breast cancer, the Xception model obtained 99%, 99.003%, 98.995%, 99%, 98.55%, and 99.14% for accuracy, precision, recall, F1-score, sensitivity, and specificity, respectively. Through these results, the proposed approach improved the models’ performance when fine-tuning was performed on unclassified images of the same disease. Full article
(This article belongs to the Special Issue Advanced Signal Processing and Human-Machine Interface for Healthcare Diagnostics and Bioengineering Applications)
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Review

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22 pages, 1945 KiB  
Review
Recent Advances in Non-Invasive Blood Pressure Monitoring and Prediction Using a Machine Learning Approach
by Siti Nor Ashikin Ismail, Nazrul Anuar Nayan, Rosmina Jaafar and Zazilah May
Sensors 2022, 22(16), 6195; https://doi.org/10.3390/s22166195 - 18 Aug 2022
Cited by 14 | Viewed by 4002
Abstract
Blood pressure (BP) monitoring can be performed either invasively via arterial catheterization or non-invasively through a cuff sphygmomanometer. However, for conscious individuals, traditional cuff-based BP monitoring devices are often uncomfortable, intermittent, and impractical for frequent measurements. Continuous and non-invasive BP (NIBP) monitoring is [...] Read more.
Blood pressure (BP) monitoring can be performed either invasively via arterial catheterization or non-invasively through a cuff sphygmomanometer. However, for conscious individuals, traditional cuff-based BP monitoring devices are often uncomfortable, intermittent, and impractical for frequent measurements. Continuous and non-invasive BP (NIBP) monitoring is currently gaining attention in the human health monitoring area due to its promising potentials in assessing the health status of an individual, enabled by machine learning (ML), for various purposes such as early prediction of disease and intervention treatment. This review presents the development of a non-invasive BP measuring tool called sphygmomanometer in brief, summarizes state-of-the-art NIBP sensors, and identifies extended works on continuous NIBP monitoring using commercial devices. Moreover, the NIBP predictive techniques including pulse arrival time, pulse transit time, pulse wave velocity, and ML are elaborated on the basis of bio-signals acquisition from these sensors. Additionally, the different BP values (systolic BP, diastolic BP, mean arterial pressure) of the various ML models adopted in several reported studies are compared in terms of the international validation standards developed by the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) for clinically-approved BP monitors. Finally, several challenges and possible solutions for the implementation and realization of continuous NIBP technology are addressed. Full article
(This article belongs to the Special Issue Advanced Signal Processing and Human-Machine Interface for Healthcare Diagnostics and Bioengineering Applications)
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