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Applied Sciences
Special Issues
Application of Machine Learning and Deep Learning in Biomedical Engineering
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Application of Machine Learning and Deep Learning in Biomedical Engineering

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 (30 April 2023) | Viewed by 2934

Special Issue Editor

Dr. Soojeong Lee

E-Mail Website
Guest Editor
Department of Computer Engineering, Sejong University, Seoul 05006, Korea
Interests: deep and machine learning; uncertainty estimation for biomedical processing

Special Issue Information

Dear Colleagues,

After COVID-19, the machine learning and deep learning currently implemented in biomedical engineering is used to accurately measure and analyze biometric data, including blood pressure, respiration rate, and heart rate, to predict prognostic signs. Therefore, machine learning (ML) is becoming an essential factor in solving the problem of the analysis and integration of various types of sensor data. ML is also increasingly important for biometric data analysis and classification.

In this Special Issue, we plan to demonstrate the usefulness of machine learning in solving these growing computing challenges by providing a primer for applying machine learning and deep learning to diverse biometric data sets. We invite your contributions (original research articles, reviews, or short perspective articles) on all aspects of the topic “Biomedical Engineering, Bio-measurement and Estimation, Machine Learning and Classification.” Articles with sound methodologies and scientific practices are especially welcome. Related topics include, but are not limited to:

  • Biomedical engineering and machine learning;
  • Biosignal processing;
  • Biomedical text classification;
  • Integrative analysis of biomedical data;
  • Machine learning in bio-measurement integrated with biological domain knowledge;
  • Deep learning approaches.

Dr. Soojeong Lee
Guest Editor

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.

Published Papers (2 papers)

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Research

24 pages, 9650 KiB  
Article
Early Detection of Late Onset Sepsis in Extremely Preterm Infants Using Machine Learning: Towards an Early Warning System
by Arno G. Garstman, Cristian Rodriguez Rivero and Wes Onland
Appl. Sci. 2023, 13(16), 9049; https://doi.org/10.3390/app13169049 - 08 Aug 2023
Viewed by 951
Abstract
A significant proportion of babies that are admitted to the neonatal intensive care unit (NICU) suffer from late onset sepsis (LOS). In order to prevent mortality and morbidity, the early detection of LOS is of the utmost importance. Recent works have found that [...] Read more.
A significant proportion of babies that are admitted to the neonatal intensive care unit (NICU) suffer from late onset sepsis (LOS). In order to prevent mortality and morbidity, the early detection of LOS is of the utmost importance. Recent works have found that the use of machine learning techniques might help detect LOS at an early stage. Some works have shown that linear methods (i.e., logistic regression) display a superior performance when predicting LOS. Nevertheless, as research on this topic is still in an early phase, it has not been ruled out that non-linear machine learning (ML) techniques can improve the predictive performance. Moreover, few studies have assessed the effect of parameters other than heart rate variability (HRV). Therefore, the current study investigates the effect of non-linear methods and assesses whether other vital parameters such as respiratory rate, perfusion index, and oxygen saturation could be of added value when predicting LOS. In contrast with the findings in the literature, it was found that non-linear methods showed a superior performance compared with linear models. In particular, it was found that random forest performed best (AUROC: 0.973), 24% better than logistic regression (AUROC: 0.782). Nevertheless, logistic regression was found to perform similarly to some non-linear models when trained with a short training window. Furthermore, when also taking training time into account, K-Nearest Neighbors was found to be the most beneficial (AUROC: 0.950). In line with the literature, we found that training the models on HRV features yielded the best results. Lastly, the results revealed that non-linear methods demonstrated a superior performance compared with linear methods when adding respiratory features to the HRV feature set, which ensured the greatest improvement in terms of AUROC score. Full article
(This article belongs to the Special Issue Application of Machine Learning and Deep Learning in Biomedical Engineering)
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20 pages, 567 KiB  
Article
Cuffless Blood Pressure Estimation with Confidence Intervals using Hybrid Feature Selection and Decision Based on Gaussian Process
by Soojeong Lee, Gyanendra Prasad Joshi, Anish Prasad Shrestha, Chang-Hwan Son and Gangseong Lee
Appl. Sci. 2023, 13(2), 1221; https://doi.org/10.3390/app13021221 - 16 Jan 2023
Viewed by 1567
Abstract
Cuffless blood pressure (BP) monitoring is crucial for patients with cardiovascular disease and hypertension. However, conventional BP monitors provide only single-point estimates without confidence intervals. Therefore, the statistical variability in the estimates is indistinguishable from the intrinsic variability caused by physiological processes. This [...] Read more.
Cuffless blood pressure (BP) monitoring is crucial for patients with cardiovascular disease and hypertension. However, conventional BP monitors provide only single-point estimates without confidence intervals. Therefore, the statistical variability in the estimates is indistinguishable from the intrinsic variability caused by physiological processes. This study introduced a novel method for improving the reliability of BP and confidence intervals (CIs) estimations using a hybrid feature selection and decision method based on a Gaussian process. F-test and robust neighbor component analysis were applied as feature selection methods for obtaining a set of highly weighted features to estimate accurate BP and CIs. Akaike’s information criterion algorithm was used to select the best feature subset. The performance of the proposed algorithm was confirmed through experiments. Comparisons with conventional algorithms indicated that the proposed algorithm provided the most accurate BP and CIs estimates. To the best of the authors’ knowledge, the proposed method is currently the only one that provides highly reliable BP and CIs estimates. Therefore, the proposed algorithm may be robust for concurrently estimating BP and CIs. Full article
(This article belongs to the Special Issue Application of Machine Learning and Deep Learning in Biomedical Engineering)
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