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Machine Learning for Biomedical Applications, Volume II

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 19782

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Special Issue Editors

Dr. Giuseppe Cesarelli

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Department of Chemical, Materials and Production Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy
Interests: biomedical engineering; bioengineering; biomedical data analysis; biomedical signal processing; drug delivery systems; biomaterials; polymer microparticles; lean six sigma in healthcare
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Dr. Alfonso Maria Ponsiglione

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Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, 80125 Naples, Italy
Interests: nanomedicine; microfluidics; drug delivery systems; biomaterials
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Dr. Carlo Ricciardi

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Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy
Interests: machine learning; statistics; gait analysis; health technology assessment; lean six sigma; biomedical engineering
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Dr. Leandro Donisi

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Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, Piazza Luigi Miraglia, 2, 80138 Naples, Italy
Interests: biomedical engineering; biosignal and bioimage processing; ergonomics; rehabilitation engineering, gait analysis, wearable sensors; telemedicine; machine learning; biostatistics
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Special Issue Information

Dear Colleagues,

The collection and analysis of medical data coupled with the advances in artificial intelligence has enabled the possible achievement of promising advancements in the biomedical field. In this context, machine learning has been demonstrated as a hopeful tool to both gain new insights from medical-related data, in order to pursue different objectives, and to find new alternative solutions that may be applicable for solving complex biomedical issues in a more automated, favorable, and rapid way. Volumes I and II of the Special Issue “Machine Learning for Biomedical Applications” seek high-quality contributions (articles, reviews, communications, etc.) presenting novel research and addressing recent progress in applying machine learning strategies to the biomedical field, including, but not limited to, image and signal processing aimed at diagnosis and rehabilitation.

Topics of interest include, but are not limited to, the following application fields for machine learning:

Biomedical and health informatics;
Biomedical data processing from wearable sensors;
Biomedical signal processing;
Healthcare innovation;
Image processing applications (e.g., radiomics)
Internet of Things;
Machine learning for biorobotics and biomechanics;
Neural and rehabilitation engineering;
Support in decision making for medical diagnoses.

Dr. Giuseppe Cesarelli
Dr. Alfonso Maria Ponsiglione
Dr. Carlo Ricciardi
Dr. Leandro Donisi
Guest Editors

Manuscript Submission Information

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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

biomedical informatics
biomedical signal processing
computer-aided diagnosis
human activity recognition
image processing
inertial measurements units and sensors for IoT
machine learning
medical diagnoses
modeling and simulation
neural networks
rehabilitation medicine

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Published Papers (8 papers)

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Research

13 pages, 535 KiB

Open AccessArticle

Improved Hypertension Risk Assessment with Photoplethysmographic Recordings Combining Deep Learning and Calibration

by Jesús Cano, Vicente Bertomeu-González, Lorenzo Fácila, Fernando Hornero, Raúl Alcaraz and José J. Rieta

Bioengineering 2023, 10(12), 1439; https://doi.org/10.3390/bioengineering10121439 - 18 Dec 2023

Cited by 1 | Viewed by 1455

Abstract

Hypertension, a primary risk factor for various cardiovascular diseases, is a global health concern. Early identification and effective management of hypertensive individuals are vital for reducing associated health risks. This study explores the potential of deep learning (DL) techniques, specifically GoogLeNet, ResNet-18, and ResNet-50, for discriminating between normotensive (NTS) and hypertensive (HTS) individuals using photoplethysmographic (PPG) recordings. The research assesses the impact of calibration at different time intervals between measurements, considering intervals less than 1 h, 1–6 h, 6–24 h, and over 24 h. Results indicate that calibration is most effective when measurements are closely spaced, with an accuracy exceeding 90% in all the DL strategies tested. For calibration intervals below 1 h, ResNet-18 achieved the highest accuracy (93.32%), sensitivity (84.09%), specificity (97.30%), and F1-score (88.36%). As the time interval between calibration and test measurements increased, classification performance gradually declined. For intervals exceeding 6 h, accuracy dropped below 81% but with all models maintaining accuracy above 71% even for intervals above 24 h. This study provides valuable insights into the feasibility of using DL for hypertension risk assessment, particularly through PPG recordings. It demonstrates that closely spaced calibration measurements can lead to highly accurate classification, emphasizing the potential for real-time applications. These findings may pave the way for advanced, non-invasive, and continuous blood pressure monitoring methods that are both efficient and reliable. Full article

(This article belongs to the Special Issue Machine Learning for Biomedical Applications, Volume II)

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17 pages, 5048 KiB

Open AccessArticle

sEMG Spectral Analysis and Machine Learning Algorithms Are Able to Discriminate Biomechanical Risk Classes Associated with Manual Material Liftings

by Leandro Donisi, Deborah Jacob, Lorena Guerrini, Giuseppe Prisco, Fabrizio Esposito, Mario Cesarelli, Francesco Amato and Paolo Gargiulo

Bioengineering 2023, 10(9), 1103; https://doi.org/10.3390/bioengineering10091103 - 20 Sep 2023

Cited by 2 | Viewed by 1827

Abstract

Manual material handling and load lifting are activities that can cause work-related musculoskeletal disorders. For this reason, the National Institute for Occupational Safety and Health proposed an equation depending on the following parameters: intensity, duration, frequency, and geometric characteristics associated with the load lifting. In this paper, we explore the feasibility of several Machine Learning (ML) algorithms, fed with frequency-domain features extracted from electromyographic (EMG) signals of back muscles, to discriminate biomechanical risk classes defined by the Revised NIOSH Lifting Equation. The EMG signals of the multifidus and erector spinae muscles were acquired by means of a wearable device for surface EMG and then segmented to extract several frequency-domain features relating to the Total Power Spectrum of the EMG signal. These features were fed to several ML algorithms to assess their prediction power. The ML algorithms produced interesting results in the classification task, with the Support Vector Machine algorithm outperforming the others with accuracy and Area under the Receiver Operating Characteristic Curve values of up to 0.985. Moreover, a correlation between muscular fatigue and risky lifting activities was found. These results showed the feasibility of the proposed methodology—based on wearable sensors and artificial intelligence—to predict the biomechanical risk associated with load lifting. A future investigation on an enriched study population and additional lifting scenarios could confirm the potential of the proposed methodology and its applicability in the field of occupational ergonomics. Full article

(This article belongs to the Special Issue Machine Learning for Biomedical Applications, Volume II)

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21 pages, 3626 KiB

Open AccessArticle

Empirical Myoelectric Feature Extraction and Pattern Recognition in Hemiplegic Distal Movement Decoding

by Alexey Anastasiev, Hideki Kadone, Aiki Marushima, Hiroki Watanabe, Alexander Zaboronok, Shinya Watanabe, Akira Matsumura, Kenji Suzuki, Yuji Matsumaru and Eiichi Ishikawa

Bioengineering 2023, 10(7), 866; https://doi.org/10.3390/bioengineering10070866 - 21 Jul 2023

Viewed by 2383

Abstract

In myoelectrical pattern recognition (PR), the feature extraction methods for stroke-oriented applications are challenging and remain discordant due to a lack of hemiplegic data and limited knowledge of skeletomuscular function. Additionally, technical and clinical barriers create the need for robust, subject-independent feature generation while using supervised learning (SL). To the best of our knowledge, we are the first study to investigate the brute-force analysis of individual and combinational feature vectors for acute stroke gesture recognition using surface electromyography (EMG) of 19 patients. Moreover, post-brute-force singular vectors were concatenated via a Fibonacci-like spiral net ranking as a novel, broadly applicable concept for feature selection. This semi-brute-force navigated amalgamation in linkage (SNAiL) of EMG features revealed an explicit classification rate performance advantage of 10–17% compared to canonical feature sets, which can drastically extend PR capabilities in biosignal processing. Full article

(This article belongs to the Special Issue Machine Learning for Biomedical Applications, Volume II)

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17 pages, 1489 KiB

Open AccessArticle

ECG Recordings as Predictors of Very Early Autism Likelihood: A Machine Learning Approach

by Deepa Tilwani, Jessica Bradshaw, Amit Sheth and Christian O’Reilly

Bioengineering 2023, 10(7), 827; https://doi.org/10.3390/bioengineering10070827 - 11 Jul 2023

Cited by 2 | Viewed by 2966

Abstract

In recent years, there has been a rise in the prevalence of autism spectrum disorder (ASD). The diagnosis of ASD requires behavioral observation and standardized testing completed by highly trained experts. Early intervention for ASD can begin as early as 1–2 years of age, but ASD diagnoses are not typically made until ages 2–5 years, thus delaying the start of intervention. There is an urgent need for non-invasive biomarkers to detect ASD in infancy. While previous research using physiological recordings has focused on brain-based biomarkers of ASD, this study investigated the potential of electrocardiogram (ECG) recordings as an ASD biomarker in 3–6-month-old infants. We recorded the heart activity of infants at typical and elevated familial likelihood for ASD during naturalistic interactions with objects and caregivers. After obtaining the ECG signals, features such as heart rate variability (HRV) and sympathetic and parasympathetic activities were extracted. Then we evaluated the effectiveness of multiple machine learning classifiers for classifying ASD likelihood. Our findings support our hypothesis that infant ECG signals contain important information about ASD familial likelihood. Amongthe various machine learning algorithms tested, KNN performed best according to sensitivity (0.70 ± 0.117), F1-score (0.689 ± 0.124), precision (0.717 ± 0.128), accuracy (0.70 ± 0.117, p-value = 0.02), and ROC (0.686 ± 0.122, p-value = 0.06). These results suggest that ECG signals contain relevant information about the likelihood of an infant developing ASD. Future studies should consider the potential of information contained in ECG, and other indices of autonomic control, for the development of biomarkers of ASD in infancy. Full article

(This article belongs to the Special Issue Machine Learning for Biomedical Applications, Volume II)

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38 pages, 3658 KiB

Open AccessArticle

Extreme Gradient Boosting Tuned with Metaheuristic Algorithms for Predicting Myeloid NGS Onco-Somatic Variant Pathogenicity

by Eric Pellegrino, Clara Camilla, Norman Abbou, Nathalie Beaufils, Christel Pissier, Jean Gabert, Isabelle Nanni-Metellus and L’Houcine Ouafik

Bioengineering 2023, 10(7), 753; https://doi.org/10.3390/bioengineering10070753 - 23 Jun 2023

Viewed by 1914

Abstract

The advent of next-generation sequencing (NGS) technologies has revolutionized the field of bioinformatics and genomics, particularly in the area of onco-somatic genetics. NGS has provided a wealth of information about the genetic changes that underlie cancer and has considerably improved our ability to diagnose and treat cancer. However, the large amount of data generated by NGS makes it difficult to interpret the variants. To address this, machine learning algorithms such as Extreme Gradient Boosting (XGBoost) have become increasingly important tools in the analysis of NGS data. In this paper, we present a machine learning tool that uses XGBoost to predict the pathogenicity of a mutation in the myeloid panel. We optimized the performance of XGBoost using metaheuristic algorithms and compared our predictions with the decisions of biologists and other prediction tools. The myeloid panel is a critical component in the diagnosis and treatment of myeloid neoplasms, and the sequencing of this panel allows for the identification of specific genetic mutations, enabling more accurate diagnoses and tailored treatment plans. We used datasets collected from our myeloid panel NGS analysis to train the XGBoost algorithm. It represents a data collection of 15,977 mutations variants composed of a collection of 13,221 Single Nucleotide Variants (SNVs), 73 Multiple Nucleoid Variants (MNVs), and 2683 insertion deletions (INDELs). The optimal XGBoost hyperparameters were found with Differential Evolution (DE), with an accuracy of 99.35%, precision of 98.70%, specificity of 98.71%, and sensitivity of 1. Full article

(This article belongs to the Special Issue Machine Learning for Biomedical Applications, Volume II)

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20 pages, 2464 KiB

Open AccessArticle

Model-Driven Analysis of ECG Using Reinforcement Learning

by Christian O’Reilly, Sai Durga Rithvik Oruganti, Deepa Tilwani and Jessica Bradshaw

Bioengineering 2023, 10(6), 696; https://doi.org/10.3390/bioengineering10060696 - 7 Jun 2023

Cited by 2 | Viewed by 2392

Abstract

Modeling is essential to better understand the generative mechanisms responsible for experimental observations gathered from complex systems. In this work, we are using such an approach to analyze the electrocardiogram (ECG). We present a systematic framework to decompose ECG signals into sums of overlapping lognormal components. We use reinforcement learning to train a deep neural network to estimate the modeling parameters from an ECG recorded in babies from 1 to 24 months of age. We demonstrate this model-driven approach by showing how the extracted parameters vary with age. From the 751,510 PQRST complexes modeled, 82.7% provided a signal-to-noise ratio that was sufficient for further analysis (>5 dB). After correction for multiple tests, 10 of the 24 modeling parameters exhibited statistical significance below the 0.01 threshold, with absolute Kendall rank correlation coefficients in the [0.27, 0.51] range. These results confirm that this model-driven approach can capture sensitive ECG parameters. Due to its physiological interpretability, this approach can provide a window into latent variables which are important for understanding the heart-beating process and its control by the autonomous nervous system. Full article

(This article belongs to the Special Issue Machine Learning for Biomedical Applications, Volume II)

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18 pages, 3352 KiB

Open AccessArticle

Pancreatic Mass Characterization Using IVIM-DKI MRI and Machine Learning-Based Multi-Parametric Texture Analysis

by Archana Vadiraj Malagi, Sivachander Shivaji, Devasenathipathy Kandasamy, Raju Sharma, Pramod Garg, Siddhartha Datta Gupta, Shivanand Gamanagatti and Amit Mehndiratta

Bioengineering 2023, 10(1), 83; https://doi.org/10.3390/bioengineering10010083 - 8 Jan 2023

Cited by 5 | Viewed by 2466

Abstract

Non-invasive characterization of pancreatic masses aids in the management of pancreatic lesions. Intravoxel incoherent motion-diffusion kurtosis imaging (IVIM-DKI) and machine learning-based texture analysis was used to differentiate pancreatic masses such as pancreatic ductal adenocarcinoma (PDAC), pancreatic neuroendocrine tumor (pNET), solid pseudopapillary epithelial neoplasm (SPEN), and mass-forming chronic pancreatitis (MFCP). A total of forty-eight biopsy-proven patients with pancreatic masses were recruited and classified into pNET (n = 13), MFCP (n = 6), SPEN (n = 4), and PDAC (n = 25) groups. All patients were scanned for IVIM-DKI sequences acquired with 14 b-values (0 to 2500 s/mm²) on a 1.5T MRI. An IVIM-DKI model with a 3D total variation (TV) penalty function was implemented to estimate the precise IVIM-DKI parametric maps. Texture analysis (TA) of the apparent diffusion coefficient (ADC) and IVIM-DKI parametric map was performed and reduced using the chi-square test. These features were fed to an artificial neural network (ANN) for characterization of pancreatic mass subtypes and validated by 5-fold cross-validation. Receiver operator characteristics (ROC) analyses were used to compute the area under curve (AUC). Perfusion fraction (f) was significantly higher (p < 0.05) in pNET than PDAC. The f showed better diagnostic performance for PDAC vs. MFCP with AUC:0.77. Both pseudo-diffusion coefficient (D*) and f for PDAC vs. pNET showed an AUC of 0.73. ADC and diffusion coefficient (D) showed good diagnostic performance for pNET vs. MFCP with AUC: 0.79 and 0.76, respectively. In the TA of PDAC vs. non-PDAC, f and combined IVIM-DKI parameters showed high accuracy ≥ 84.3% and AUC ≥ 0.84. Mean f and combined IVIM-DKI parameters estimated that the IVIM-DKI model with TV texture features has the potential to be helpful in characterizing pancreatic masses. Full article

(This article belongs to the Special Issue Machine Learning for Biomedical Applications, Volume II)

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24 pages, 13838 KiB

Open AccessArticle

Collinearity and Dimensionality Reduction in Radiomics: Effect of Preprocessing Parameters in Hypertrophic Cardiomyopathy Magnetic Resonance T1 and T2 Mapping

by Chiara Marzi, Daniela Marfisi, Andrea Barucci, Jacopo Del Meglio, Alessio Lilli, Claudio Vignali, Mario Mascalchi, Giancarlo Casolo, Stefano Diciotti, Antonio Claudio Traino, Carlo Tessa and Marco Giannelli

Bioengineering 2023, 10(1), 80; https://doi.org/10.3390/bioengineering10010080 - 6 Jan 2023

Cited by 7 | Viewed by 3066

Abstract

Radiomics and artificial intelligence have the potential to become a valuable tool in clinical applications. Frequently, radiomic analyses through machine learning methods present issues caused by high dimensionality and multicollinearity, and redundant radiomic features are usually removed based on correlation analysis. We assessed the effect of preprocessing—in terms of voxel size resampling, discretization, and filtering—on correlation-based dimensionality reduction in radiomic features from cardiac T1 and T2 maps of patients with hypertrophic cardiomyopathy. For different combinations of preprocessing parameters, we performed a dimensionality reduction of radiomic features based on either Pearson’s or Spearman’s correlation coefficient, followed by the computation of the stability index. With varying resampling voxel size and discretization bin width, for both T1 and T2 maps, Pearson’s and Spearman’s dimensionality reduction produced a slightly different percentage of remaining radiomic features, with a relatively high stability index. For different filters, the remaining features’ stability was instead relatively low. Overall, the percentage of eliminated radiomic features through correlation-based dimensionality reduction was more dependent on resampling voxel size and discretization bin width for textural features than for shape or first-order features. Notably, correlation-based dimensionality reduction was less sensitive to preprocessing when considering radiomic features from T2 compared with T1 maps. Full article

(This article belongs to the Special Issue Machine Learning for Biomedical Applications, Volume II)

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