Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.2
4.6
Journals
Bioengineering
Special Issues
Artificial Intelligence for Biomedical Signal Processing
share announcement

Artificial Intelligence for Biomedical Signal Processing

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 6325

Special Issue Editors

Dr. Fernando Vaquerizo-Villar

E-Mail Website
Guest Editor
Department of Theory of Signal and Communications and Telematic Engineering, Universidad de Valladolid, Valladolid, Spain
Interests: biomedical signal processing; sleep apnea; deep learning; explainable artificial intelligence
Dr. Verónica Barroso-García

E-Mail Website
Guest Editor
Department of Theory of Signal and Communications and Telematic Engineering, Universidad de Valladolid, Valladolid, Spain
Interests: sleep apnea; biomedical engineering; biomedical signal processing; machine learning; deep learning

Special Issue Information

Dear Colleagues,

Biomedical signals and images provide meaningful information about the status and function of a biological system. This fact, along with the rapid progress in the field of artificial intelligence (AI), has increased the development of AI-based applications that use this information to automatically diagnose diseases, create personalized medicine systems, and even remotely monitor patients' healthcare.

Therefore, this Special Issue aims to attract researchers with an interest in applying AI methods to different biomedical signals and images (such as electrocardiogram, electroencephalogram, magnetoencephalogram, electromyogram, galvanic skin response, pulse oximetry, computed tomography scan, magnetic resonance imaging, etc.) to assist physicians in these tasks. Topics of interest include but are not limited to the following:

  1. Applications of AI in biomedical engineering.
  2. AI-based systems for automatic prognosis and diagnosis of diseases.
  3. Biomedical signal and image analysis using machine learning and deep learning algorithms.
  4. Explainable artificial intelligence in biomedicine.
  5. Remote healthcare monitoring using AI-based systems.
  6. Biomarkers extracted from biosignals and bioimages through AI techniques.
  7. Physiological time series forecasting.

Original papers that describe new research on these subjects are welcomed. Your contributions will enhance the development of new biosignal processing methodologies with key implications for medicine and healthcare. So, we look forward to your participation in this Special Issue.

Dr. Fernando Vaquerizo-Villar
Dr. Verónica Barroso-García
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

  • aid diagnosis
  • automatic diagnosis
  • biomedical signals
  • biomedical signal processing
  • deep learning
  • diseases
  • explainable artificial intelligence
  • machine learning
  • physiological time series
  • predictive models

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 1861 KiB  
Article
BioDiffusion: A Versatile Diffusion Model for Biomedical Signal Synthesis
by Xiaomin Li, Mykhailo Sakevych, Gentry Atkinson and Vangelis Metsis
Bioengineering 2024, 11(4), 299; https://doi.org/10.3390/bioengineering11040299 - 22 Mar 2024
Viewed by 705
Abstract
Machine learning tasks involving biomedical signals frequently grapple with issues such as limited data availability, imbalanced datasets, labeling complexities, and the interference of measurement noise. These challenges often hinder the optimal training of machine learning algorithms. Addressing these concerns, we introduce BioDiffusion, a [...] Read more.
Machine learning tasks involving biomedical signals frequently grapple with issues such as limited data availability, imbalanced datasets, labeling complexities, and the interference of measurement noise. These challenges often hinder the optimal training of machine learning algorithms. Addressing these concerns, we introduce BioDiffusion, a diffusion-based probabilistic model optimized for the synthesis of multivariate biomedical signals. BioDiffusion demonstrates excellence in producing high-fidelity, non-stationary, multivariate signals for a range of tasks including unconditional, label-conditional, and signal-conditional generation. Leveraging these synthesized signals offers a notable solution to the aforementioned challenges. Our research encompasses both qualitative and quantitative assessments of the synthesized data quality, underscoring its capacity to bolster accuracy in machine learning tasks tied to biomedical signals. Furthermore, when juxtaposed with current leading time-series generative models, empirical evidence suggests that BioDiffusion outperforms them in biomedical signal generation quality. Full article
(This article belongs to the Special Issue Artificial Intelligence for Biomedical Signal Processing)
►▼ Show Figures

Figure 1

16 pages, 3538 KiB  
Article
Contactless Blood Oxygen Saturation Estimation from Facial Videos Using Deep Learning
by Chun-Hong Cheng, Zhikun Yuen, Shutao Chen, Kwan-Long Wong, Jing-Wei Chin, Tsz-Tai Chan and Richard H. Y. So
Bioengineering 2024, 11(3), 251; https://doi.org/10.3390/bioengineering11030251 - 04 Mar 2024
Viewed by 1119
Abstract
Blood oxygen saturation (SpO2) is an essential physiological parameter for evaluating a person’s health. While conventional SpO2 measurement devices like pulse oximeters require skin contact, advanced computer vision technology can enable remote SpO2 monitoring through a regular camera without [...] Read more.
Blood oxygen saturation (SpO2) is an essential physiological parameter for evaluating a person’s health. While conventional SpO2 measurement devices like pulse oximeters require skin contact, advanced computer vision technology can enable remote SpO2 monitoring through a regular camera without skin contact. In this paper, we propose novel deep learning models to measure SpO2 remotely from facial videos and evaluate them using a public benchmark database, VIPL-HR. We utilize a spatial–temporal representation to encode SpO2 information recorded by conventional RGB cameras and directly pass it into selected convolutional neural networks to predict SpO2. The best deep learning model achieves 1.274% in mean absolute error and 1.71% in root mean squared error, which exceed the international standard of 4% for an approved pulse oximeter. Our results significantly outperform the conventional analytical Ratio of Ratios model for contactless SpO2 measurement. Results of sensitivity analyses of the influence of spatial–temporal representation color spaces, subject scenarios, acquisition devices, and SpO2 ranges on the model performance are reported with explainability analyses to provide more insights for this emerging research field. Full article
(This article belongs to the Special Issue Artificial Intelligence for Biomedical Signal Processing)
►▼ Show Figures

Figure 1

17 pages, 2302 KiB  
Article
Evaluation of the Photoplethysmogram-Based Deep Learning Model for Continuous Respiratory Rate Estimation in Surgical Intensive Care Unit
by Chi Shin Hwang, Yong Hwan Kim, Jung Kyun Hyun, Joon Hwang Kim, Seo Rak Lee, Choong Min Kim, Jung Woo Nam and Eun Young Kim
Bioengineering 2023, 10(10), 1222; https://doi.org/10.3390/bioengineering10101222 - 19 Oct 2023
Viewed by 1096
Abstract
The respiratory rate (RR) is a significant indicator to evaluate a patient’s prognosis and status; however, it requires specific instrumentation or estimates from other monitored signals. A photoplethysmogram (PPG) is extensively used in clinical environments as well as in intensive care units (ICUs) [...] Read more.
The respiratory rate (RR) is a significant indicator to evaluate a patient’s prognosis and status; however, it requires specific instrumentation or estimates from other monitored signals. A photoplethysmogram (PPG) is extensively used in clinical environments as well as in intensive care units (ICUs) to primarily monitor peripheral circulation while capturing indirect information about intrathoracic pressure changes. This study aims to apply and evaluate several deep learning models using a PPG for the continuous and accurate estimation of the RRs of patients. The dataset was collected twice for 2 min each in 100 patients aged 18 years and older from the surgical intensive care unit of a tertiary referral hospital. The BIDMC and CapnoBase public datasets were also analyzed. The collected dataset was preprocessed and split according to the 5-fold cross-validation. We used seven deep learning models, including our own Dilated Residual Neural Network, to check how accurately the RR estimates match the ground truth using the mean absolute error (MAE). As a result, when validated using the collected dataset, our model showed the best results with a 1.2628 ± 0.2697 MAE on BIDMC and RespNet and with a 3.1268 ± 0.6363 MAE on our dataset, respectively. In conclusion, RR estimation using PPG-derived models is still challenging and has many limitations. However, if there is an equal amount of data from various breathing groups to train, we expect that various models, including our Dilated ResNet model, which showed good results, can achieve better results than the current ones. Full article
(This article belongs to the Special Issue Artificial Intelligence for Biomedical Signal Processing)
►▼ Show Figures

Graphical abstract

16 pages, 2453 KiB  
Article
Development of a Machine Learning Model for Predicting Weaning Outcomes Based Solely on Continuous Ventilator Parameters during Spontaneous Breathing Trials
by Ji Eun Park, Do Young Kim, Ji Won Park, Yun Jung Jung, Keu Sung Lee, Joo Hun Park, Seung Soo Sheen, Kwang Joo Park, Myung Hoon Sunwoo and Wou Young Chung
Bioengineering 2023, 10(10), 1163; https://doi.org/10.3390/bioengineering10101163 - 05 Oct 2023
Viewed by 1272
Abstract
Discontinuing mechanical ventilation remains challenging. We developed a machine learning model to predict weaning outcomes using only continuous monitoring parameters obtained from ventilators during spontaneous breathing trials (SBTs). Patients who received mechanical ventilation in the medical intensive care unit at a tertiary university [...] Read more.
Discontinuing mechanical ventilation remains challenging. We developed a machine learning model to predict weaning outcomes using only continuous monitoring parameters obtained from ventilators during spontaneous breathing trials (SBTs). Patients who received mechanical ventilation in the medical intensive care unit at a tertiary university hospital from 2019–2021 were included in this study. During the SBTs, three waveforms and 25 numerical data were collected as input variables. The proposed convolutional neural network (CNN)-based weaning prediction model extracts features from input data with diverse lengths. Among 138 enrolled patients, 35 (25.4%) experienced weaning failure. The dataset was randomly divided into training and test sets (8:2 ratio). The area under the receiver operating characteristic curve for weaning success by the prediction model was 0.912 (95% confidence interval [CI], 0.795–1.000), with an area under the precision-recall curve of 0.767 (95% CI, 0.434–0.983). Furthermore, we used gradient-weighted class activation mapping technology to provide visual explanations of the model’s prediction, highlighting influential features. This tool can assist medical staff by providing intuitive information regarding readiness for extubation without requiring any additional data collection other than SBT data. The proposed predictive model can assist clinicians in making ventilator weaning decisions in real time, thereby improving patient outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence for Biomedical Signal Processing)
►▼ Show Figures

Figure 1

13 pages, 2574 KiB  
Article
Predicting Respiratory Rate from Electrocardiogram and Photoplethysmogram Using a Transformer-Based Model
by Qi Zhao, Fang Liu, Yide Song, Xiaoya Fan, Yu Wang, Yudong Yao, Qian Mao and Zheng Zhao
Bioengineering 2023, 10(9), 1024; https://doi.org/10.3390/bioengineering10091024 - 30 Aug 2023
Viewed by 1384
Abstract
The respiratory rate (RR) serves as a critical physiological parameter in the context of both diagnostic and prognostic evaluations. Due to the challenges of direct measurement, RR is still predominantly measured through the traditional manual counting-breaths method in clinic practice. Numerous algorithms and [...] Read more.
The respiratory rate (RR) serves as a critical physiological parameter in the context of both diagnostic and prognostic evaluations. Due to the challenges of direct measurement, RR is still predominantly measured through the traditional manual counting-breaths method in clinic practice. Numerous algorithms and machine learning models have been developed to predict RR using physiological signals, such as electrocardiogram (ECG) or/and photoplethysmogram (PPG) signals. Yet, the accuracy of these existing methods on available datasets remains limited, and their prediction on new data is also unsatisfactory for actual clinical applications. In this paper, we proposed an enhanced Transformer model with inception blocks for predicting RR based on both ECG and PPG signals. To evaluate the generalization capability on new data, our model was trained and tested using subject-level ten-fold cross-validation using data from both BIDMC and CapnoBase datasets. On the test set, our model achieved superior performance over five popular deep-learning-based methods with mean absolute error (1.2) decreased by 36.5% and correlation coefficient (0.85) increased by 84.8% compared to the best results of these models. In addition, we also proposed a new pipeline to preprocess ECG and PPG signals to improve model performance. We believe that the development of the TransRR model is expected to further expedite the clinical implementation of automatic RR estimation. Full article
(This article belongs to the Special Issue Artificial Intelligence for Biomedical Signal Processing)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop