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Bioengineering
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Featured Papers in Computer Methods in Biomedicine
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Featured Papers in Computer Methods in Biomedicine

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

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 16982

Special Issue Editors

Prof. Dr. Yuling Yan

E-Mail Website
Guest Editor
School of Engineering, Santa Clara University, Santa Clara, CA 95053, USA
Interests: biosignal processing; bioimaging; AI-assisted disease classification; laryngeal dynamics and physiology; biomedical visualization; brain-computer Interface
Special Issues, Collections and Topics in MDPI journals
Dr. Luca Mesin

E-Mail Website
Guest Editor
Department of Electronics and Telecommunications, Polytechnic University of Turin, Turin, Italy
Interests: biomedical signal and image processing and classification; biophysical modelling; clinical studies; mathematical biology and physiology; noninvasive monitoring of the volemic status of patients; nonlinear biomedical signal processing; optimal non-uniform down-sampling; systems for human–machine interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The journal Bioengineering would like to compile a collection of papers to report on the advancements in the field of computer methods in biomedicine.

The aim of this Special Issue, entitled Featured Papers in Computer Methods in Biomedicine, is to make relevant work known to our colleagues in the field. To achieve this, the Special Issue, edited by Prof. Luca Mesin and Prof. Yuling Yan, invites scientists to submit research articles, review articles, and short communications focused on this topic.

We look forward to your valued contributions to make this Special Issue a reference resource for future researchers in the field of computer methods in biomedicine.

Prof. Dr. Yuling Yan
Prof. Dr. Luca Mesin
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.

Published Papers (7 papers)

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Research

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10 pages, 969 KiB  
Article
Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach
by Atsuyuki Inui, Hanako Nishimoto, Yutaka Mifune, Tomoya Yoshikawa, Issei Shinohara, Takahiro Furukawa, Tatsuo Kato, Shuya Tanaka, Masaya Kusunose and Ryosuke Kuroda
Bioengineering 2023, 10(3), 277; https://doi.org/10.3390/bioengineering10030277 - 21 Feb 2023
Cited by 3 | Viewed by 1540
Abstract
The diagnosis of osteoporosis is made by measuring bone mineral density (BMD) using dual-energy X-ray absorptiometry (DXA). Machine learning, one of the artificial intelligence methods, was used to predict low BMD without using DXA in elderly women. Medical records from 2541 females who [...] Read more.
The diagnosis of osteoporosis is made by measuring bone mineral density (BMD) using dual-energy X-ray absorptiometry (DXA). Machine learning, one of the artificial intelligence methods, was used to predict low BMD without using DXA in elderly women. Medical records from 2541 females who visited the osteoporosis clinic were used in this study. As hyperparameters for machine learning, patient age, body mass index (BMI), and blood test data were used. As machine learning models, logistic regression, decision tree, random forest, gradient boosting trees, and lightGBM were used. Each model was trained to classify and predict low-BMD patients. The model performance was compared using a confusion matrix. The accuracy of each trained model was 0.772 in logistic regression, 0.739 in the decision tree, 0.775 in the random forest, 0.800 in gradient boosting, and 0.834 in lightGBM. The area under the curve (AUC) was 0.595 in the decision tree, 0.673 in logistic regression, 0.699 in the random forest, 0.840 in gradient boosting, and 0.961, which was the highest, in the lightGBM model. Important features were BMI, age, and the number of platelets. Shapley additive explanation scores in the lightGBM model showed that BMI, age, and ALT were ranked as important features. Among several machine learning models, the lightGBM model showed the best performance in the present research. Full article
(This article belongs to the Special Issue Featured Papers in Computer Methods in Biomedicine)
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18 pages, 5770 KiB  
Article
Leveraging Scheme for Cross-Study Microbiome Machine Learning Prediction and Feature Evaluations
by Kuncheng Song and Yi-Hui Zhou
Bioengineering 2023, 10(2), 231; https://doi.org/10.3390/bioengineering10020231 - 08 Feb 2023
Cited by 1 | Viewed by 1563
Abstract
The microbiota has proved to be one of the critical factors for many diseases, and researchers have been using microbiome data for disease prediction. However, models trained on one independent microbiome study may not be easily applicable to other independent studies due to [...] Read more.
The microbiota has proved to be one of the critical factors for many diseases, and researchers have been using microbiome data for disease prediction. However, models trained on one independent microbiome study may not be easily applicable to other independent studies due to the high level of variability in microbiome data. In this study, we developed a method for improving the generalizability and interpretability of machine learning models for predicting three different diseases (colorectal cancer, Crohn’s disease, and immunotherapy response) using nine independent microbiome datasets. Our method involves combining a smaller dataset with a larger dataset, and we found that using at least 25% of the target samples in the source data resulted in improved model performance. We determined random forest as our top model and employed feature selection to identify common and important taxa for disease prediction across the different studies. Our results suggest that this leveraging scheme is a promising approach for improving the accuracy and interpretability of machine learning models for predicting diseases based on microbiome data. Full article
(This article belongs to the Special Issue Featured Papers in Computer Methods in Biomedicine)
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15 pages, 3050 KiB  
Article
Developing Patient-Specific Statistical Reconstructions of Healthy Anatomical Structures to Improve Patient Outcomes
by Matthew A. Wysocki, Steven A. Lewis and Scott T. Doyle
Bioengineering 2023, 10(2), 123; https://doi.org/10.3390/bioengineering10020123 - 17 Jan 2023
Cited by 2 | Viewed by 1247
Abstract
There are still numerous problems with modern joint replacement prostheses, which negatively influence patient health and recovery. For example, it is especially important to avoid failures and complications following hip arthroplasty because the loss of hip joint function is commonly associated with increased [...] Read more.
There are still numerous problems with modern joint replacement prostheses, which negatively influence patient health and recovery. For example, it is especially important to avoid failures and complications following hip arthroplasty because the loss of hip joint function is commonly associated with increased demand on the healthcare system, reoperation, loss of independence, physical disability, and death. The current study uses hip arthroplasty as a model system to present a new strategy of computationally generating patient-specific statistical reconstructions of complete healthy anatomical structures from computed tomography (CT) scans of damaged anatomical structures. The 3D model morphological data were evaluated from damaged femurs repaired with prosthetic devices and the respective damaged femurs that had been restored using statistical reconstruction. The results from all morphological measurements (i.e., maximum femoral length, Hausdorff distance, femoral neck anteversion, length of rotational center divergence, and angle of inclination) indicated that the values of femurs repaired with traditional prostheses did not fall within the +/−3 standard deviations of the respective patient-specific healthy anatomical structures. These results demonstrate that there are quantitative differences in the morphology of femurs repaired with traditional prostheses and the morphology of patient-specific statistical reconstructions. This approach of generating patient-specific statistical reconstructions of healthy anatomical structures might help to inform prosthetic designs so that new prostheses more closely resemble natural healthy morphology and preserve biomechanical function. Additionally, the patient-specific statistical reconstructions of healthy anatomical structures might be valuable for surgeons in that prosthetic devices could be selected and positioned to more accurately restore natural biomechanical function. All in all, this contribution establishes the novel approach of generating patient-specific statistical reconstructions of healthy anatomical structures from the CT scans of individuals’ damaged anatomical structures to improve treatments and patient outcomes. Full article
(This article belongs to the Special Issue Featured Papers in Computer Methods in Biomedicine)
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14 pages, 33802 KiB  
Article
Automated Atrial Fibrillation Detection with ECG
by Ting-Ruen Wei, Senbao Lu and Yuling Yan
Bioengineering 2022, 9(10), 523; https://doi.org/10.3390/bioengineering9100523 - 05 Oct 2022
Cited by 4 | Viewed by 1881
Abstract
An electrocardiography system records electrical activities of the heart, and it is used to assist doctors in the diagnosis of cardiac arrhythmia such as atrial fibrillation. This study presents a fast, automated deep-learning algorithm that predicts atrial fibrillation with excellent performance (F-1 score [...] Read more.
An electrocardiography system records electrical activities of the heart, and it is used to assist doctors in the diagnosis of cardiac arrhythmia such as atrial fibrillation. This study presents a fast, automated deep-learning algorithm that predicts atrial fibrillation with excellent performance (F-1 score 88.2% and accuracy 97.3%). Our approach involves the pre-processing of ECG signals, followed by an alternative representation of the signals using a spectrogram, which is then fed to a fine-tuned EfficientNet B0, a pre-trained convolution neural network model, for the classification task. Using the transfer learning approach and with fine-tuning of the EfficientNet, we optimize the model to achieve highly efficient and effective classification of the atrial fibrillation. Full article
(This article belongs to the Special Issue Featured Papers in Computer Methods in Biomedicine)
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14 pages, 710 KiB  
Article
EMD-Based Method for Supervised Classification of Parkinson’s Disease Patients Using Balance Control Data
by Khaled Safi, Wael Hosny Fouad Aly, Mouhammad AlAkkoumi, Hassan Kanj, Mouna Ghedira and Emilie Hutin
Bioengineering 2022, 9(7), 283; https://doi.org/10.3390/bioengineering9070283 - 28 Jun 2022
Cited by 8 | Viewed by 1510
Abstract
There has recently been increasing interest in postural stability aimed at gaining a better understanding of the human postural system. This system controls human balance in quiet standing and during locomotion. Parkinson’s disease (PD) is the most common degenerative movement disorder that affects [...] Read more.
There has recently been increasing interest in postural stability aimed at gaining a better understanding of the human postural system. This system controls human balance in quiet standing and during locomotion. Parkinson’s disease (PD) is the most common degenerative movement disorder that affects human stability and causes falls and injuries. This paper proposes a novel methodology to differentiate between healthy individuals and those with PD through the empirical mode decomposition (EMD) method. EMD enables the breaking down of a complex signal into several elementary signals called intrinsic mode functions (IMFs). Three temporal parameters and three spectral parameters are extracted from each stabilometric signal as well as from its IMFs. Next, the best five features are selected using the feature selection method. The classification task is carried out using four known machine-learning methods, KNN, decision tree, Random Forest and SVM classifiers, over 10-fold cross validation. The used dataset consists of 28 healthy subjects (14 young adults and 14 old adults) and 32 PD patients (12 young adults and 20 old adults). The SVM method has a performance of 92% and the Dempster–Sahfer formalism method has an accuracy of 96.51%. Full article
(This article belongs to the Special Issue Featured Papers in Computer Methods in Biomedicine)
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Review

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48 pages, 4845 KiB  
Review
Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends
by Giovanni Chiarion, Laura Sparacino, Yuri Antonacci, Luca Faes and Luca Mesin
Bioengineering 2023, 10(3), 372; https://doi.org/10.3390/bioengineering10030372 - 17 Mar 2023
Cited by 19 | Viewed by 6501
Abstract
Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and [...] Read more.
Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros–cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks. Full article
(This article belongs to the Special Issue Featured Papers in Computer Methods in Biomedicine)
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Other

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20 pages, 604 KiB  
Systematic Review
Electroencephalography-Based Brain–Machine Interfaces in Older Adults: A Literature Review
by Luca Mesin, Giuseppina Elena Cipriani and Martina Amanzio
Bioengineering 2023, 10(4), 395; https://doi.org/10.3390/bioengineering10040395 - 23 Mar 2023
Cited by 3 | Viewed by 1494
Abstract
The aging process is a multifaceted phenomenon that affects cognitive-affective and physical functioning as well as interactions with the environment. Although subjective cognitive decline may be part of normal aging, negative changes objectified as cognitive impairment are present in neurocognitive disorders and functional [...] Read more.
The aging process is a multifaceted phenomenon that affects cognitive-affective and physical functioning as well as interactions with the environment. Although subjective cognitive decline may be part of normal aging, negative changes objectified as cognitive impairment are present in neurocognitive disorders and functional abilities are most impaired in patients with dementia. Electroencephalography-based brain–machine interfaces (BMI) are being used to assist older people in their daily activities and to improve their quality of life with neuro-rehabilitative applications. This paper provides an overview of BMI used to assist older adults. Both technical issues (detection of signals, extraction of features, classification) and application-related aspects with respect to the users’ needs are considered. Full article
(This article belongs to the Special Issue Featured Papers in Computer Methods in Biomedicine)
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