Advanced Applications of Artificial Intelligence and Machine Learning in Biomedical Engineering

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematical Biology".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 4136

Special Issue Editors


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Department of Applied Mathematics to ICT, ETSI Telecomunicación and CeDInt, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: biometric system; cryptography; machine learning; deep learning; mathematical processing of medical signals and images; neuroscience
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: virtual reality; computer vision; medical education and assessment; patient rehabilitation
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Centre for Biomedical Technology, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: video-based tracking; biomedical signal processing; virtual reality; augmented reality machine/deep learning; wearables; natural language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and its different approaches, from machine learning to deep learning, have become an indispensable tool for the development of new developments in medical signal and image processing and analysis. However, it is not only in this field where advances in AI have been obtained that affect the field of Biomedical Engineering. In this Special Issue, some important topics are covered, such as the challenges currently open in the mathematical foundations of deep neural networks, the problems derived from data processing, the challenges still open related to clinical trials, smart hospitals, as well as ethical and procedural aspects.

Potential topics of interest include:

  • Data Science in Biomedicine
  • Machine learning-based biosignal processing and analysis
  • Deep Learning in biosignal processing and analysis
  • Machine learning-based medical image processing and analysis
  • Deep Learning in medical image processing and analysis
  • Mathematical advances and practical applications of spiking neural networks
  • Challenges in clinical trial methodologies and evaluation
  • Challenges in models' benchmarking
  • Challenges in models' interpretability/explainability
  • Smart hospitals
  • Ethical considerations.

We invite all researchers in any of these areas to submit their recent work, which will undoubtedly be very valuable.

Prof. Dr. Carmen Sánchez Ávila
Dr. Patricia Sánchez-González
Dr. Ignacio Oropesa
Guest Editors

Manuscript Submission Information

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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

  • machine learning
  • deep learning
  • biomedical engineering
  • biomedical signals
  • medical imaging
  • smart hospitals
  • data science in healthcare

Published Papers (4 papers)

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Research

24 pages, 9831 KiB  
Article
A Novel Computational Instrument Based on a Universal Mixture Density Network with a Gaussian Mixture Model as a Backbone for Predicting COVID-19 Variants’ Distributions
by Yas Al-Hadeethi, Intesar F. El Ramley, Hiba Mohammed, Nada M. Bedaiwi and Abeer Z. Barasheed
Mathematics 2024, 12(8), 1254; https://doi.org/10.3390/math12081254 - 20 Apr 2024
Viewed by 573
Abstract
Various published COVID-19 models have been used in epidemiological studies and healthcare planning to model and predict the spread of the disease and appropriately realign health measures and priorities given the resource limitations in the field of healthcare. However, a significant issue arises [...] Read more.
Various published COVID-19 models have been used in epidemiological studies and healthcare planning to model and predict the spread of the disease and appropriately realign health measures and priorities given the resource limitations in the field of healthcare. However, a significant issue arises when these models need help identifying the distribution of the constituent variants of COVID-19 infections. The emergence of such a challenge means that, given limited healthcare resources, health planning would be ineffective and cost lives. This work presents a universal neural network (NN) computational instrument for predicting the mainstream symptomatic infection rate of COVID-19 and models of the distribution of its associated variants. The NN is based on a mixture density network (MDN) with a Gaussian mixture model (GMM) object as a backbone. Twelve use cases were used to demonstrate the validity and reliability of the proposed MDN. The use cases included COVID-19 data for Canada and Saudi Arabia, two date ranges (300 and 500 days), two input data modes, and three activation functions, each with different implementations of the batch size and epoch value. This array of scenarios provided an opportunity to investigate the impacts of epistemic uncertainty (EU) and aleatoric uncertainty (AU) on the prediction model’s fitting. The model accuracy readings were in the high nineties based on a tolerance margin of 0.0125. The primary outcome of this work indicates that this easy-to-use universal MDN helps provide reliable predictions of COVID-19 variant distributions and the corresponding synthesized profile of the mainstream infection rate. Full article
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19 pages, 41408 KiB  
Article
Adaptive Normalization and Feature Extraction for Electrodermal Activity Analysis
by Miguel Viana-Matesanz and Carmen Sánchez-Ávila
Mathematics 2024, 12(2), 202; https://doi.org/10.3390/math12020202 - 8 Jan 2024
Viewed by 839
Abstract
Electrodermal Activity (EDA) has shown great potential for emotion recognition and the early detection of physiological anomalies associated with stress. However, its non-stationary nature limits the capability of current analytical and detection techniques, which are highly dependent on signal stability and controlled environmental [...] Read more.
Electrodermal Activity (EDA) has shown great potential for emotion recognition and the early detection of physiological anomalies associated with stress. However, its non-stationary nature limits the capability of current analytical and detection techniques, which are highly dependent on signal stability and controlled environmental conditions. This paper proposes a framework for EDA normalization based on the exponential moving average (EMA) with outlier removal applicable to non-stationary heteroscedastic signals and a novel set of features for analysis. The normalized time series preserves the morphological and statistical properties after transformation. Meanwhile, the proposed features expand on typical time-domain EDA features and profit from the resulting normalized signal properties. Parameter selection and validation were performed using two different EDA databases on stress assessment, accomplishing trend preservation using windows between 5 and 20 s. The proposed normalization and feature extraction framework for EDA analysis showed promising results for the identification of noisy, relaxed and arousal-like patterns in data with conventional clustering approaches like K-means over the aforementioned normalized features. Full article
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17 pages, 908 KiB  
Article
Motion Sequence Analysis Using Adaptive Coding with Ensemble Hidden Markov Models
by Xiangzeng Kong, Xinyue Liu, Shimiao Chen, Wenxuan Kang, Zhicong Luo, Jianjun Chen and Tao Wu
Mathematics 2024, 12(2), 185; https://doi.org/10.3390/math12020185 - 5 Jan 2024
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Abstract
Motion sequence data comprise a chronologically organized recording of a series of movements or actions carried out by a human being. Motion patterns found in such data holds significance for research and applications across multiple fields. In recent years, various feature representation techniques [...] Read more.
Motion sequence data comprise a chronologically organized recording of a series of movements or actions carried out by a human being. Motion patterns found in such data holds significance for research and applications across multiple fields. In recent years, various feature representation techniques have been proposed to carry out sequence analysis. However, many of these methods have not fully uncovered the correlations between elements in sequences nor the internal interrelated structures among different dimensions, which are crucial to the recognition of motion patterns. This study proposes a novel Adaptive Sequence Coding (ASC) feature representation with ensemble hidden Markov models for motion sequence analysis. The ASC adopts the dual symbolization integrating first-order differential symbolization and event sequence encoding to effectively represent individual motion sequences. Subsequently, an adaptive boost algorithm based on a hidden Markov model is presented to distinguish the coded sequence data into different motion patterns. The experimental results on several publicly available datasets demonstrate that the proposed methodology outperforms other competing techniques. Meanwhile, ablation studies conducted on ASC and the adaptive boost approach further verify their significant potential in motion sequence analysis. Full article
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14 pages, 1544 KiB  
Article
Advanced Analysis of Electrodermal Activity Measures to Detect the Onset of ON State in Parkinson’s Disease
by Mercedes Barrachina-Fernández, Laura Valenzuela-López, Marcos Moreno-Verdú, Francisco José Sánchez-Cuesta, Yeray González-Zamorano, Juan Pablo Romero and Carmen Sánchez-Ávila
Mathematics 2023, 11(23), 4822; https://doi.org/10.3390/math11234822 - 29 Nov 2023
Viewed by 1200
Abstract
Background: Electrodermal activity (EDA) serves as a prominent biosignal for assessing sympathetic activation across various scenarios. Prior research has suggested a connection between EDA and fluctuations in Parkinson’s disease (PD), but its precise utility in reliably detecting these fluctuations has remained unexplored. This [...] Read more.
Background: Electrodermal activity (EDA) serves as a prominent biosignal for assessing sympathetic activation across various scenarios. Prior research has suggested a connection between EDA and fluctuations in Parkinson’s disease (PD), but its precise utility in reliably detecting these fluctuations has remained unexplored. This study aims to evaluate the efficacy of both basic and advanced analyses of EDA changes in identifying the transition to the ON state following dopaminergic medication administration in individuals with PD. Methods: In this observational study, 19 individuals with PD were enrolled. EDA was continuously recorded using the Empatica E4 device, worn on the wrist, during the transition from the OFF state to the ON state following levodopa intake. The raw EDA signal underwent preprocessing and evaluation through three distinct approaches. A logistic regression model was constructed to assess the significance of variables predicting the ON/OFF state, and support vector machine (SVM) models along with various Neural Network (NN) configurations were developed for accurate state prediction. Results: Differences were identified between the ON and OFF states in both the time and frequency domains, as well as through the utilization of convex optimization techniques. SVM and NN models demonstrated highly promising results in effectively distinguishing between the OFF and ON states. Conclusions: Evaluating sympathetic activation changes via EDA measures holds substantial promise for detecting non-motor fluctuations in PD. The SVM algorithm, in particular, yields precise outcomes for predicting these non-motor fluctuation states. Full article
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