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Detection and Modelling of Biosignals
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Detection and Modelling of Biosignals

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Biomedical Information and Health".

Deadline for manuscript submissions: closed (1 January 2025) | Viewed by 14861

Special Issue Editors

Dr. Sami Azam

E-Mail Website
Guest Editor
College of Engineering, IT and Environment, Charles Darwin University, Ellengowan Drive, Casuarina, NT 0909, Australia
Interests: biomedical engineering; health informatics; machine learning; software engineering; privacy and security
Dr. Zubaer Ibna Mannan

E-Mail Website
Guest Editor
Global Campus, Kyungdong University, Gangwon-do 24764, Korea
Interests: memristor; neuromorphic; bioelectronics; machine learning
Dr. Kawsar Ahmad

E-Mail Website
Guest Editor
1. Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
2. Group of Bio-photomatiχ, Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh
Interests: biomedical engineering; bioinformatics; biosensor design; federated learning; health informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

To sustain life, several systems in the human body collaborate in a closed loop to monitor and assess electrical, chemical, and mechanical activities that occur during biological events. These systems communicate using bio-signals, which are the primary source of information regarding their behavior. Although bio-signals can be measured from biological sources, external physiological instruments are frequently employed to measure heart rate, blood pressure, oxygen saturation levels, blood glucose, nerve conduction, brain activity, etc. The analysis of these measurements could extract useful information that clinicians can use to make quick and accurate decisions. Most medical treatments in the real world are based on information provided by the patient. This information could be biased, subjective, or incomplete. Performing medical examinations such as electroencephalogram (EEG) signals, magnetoencephalography (MEG) signals, electromyography (EMG) signals, ECG signals, and others is often necessary to obtain an accurate diagnosis when required. Modern technologies such as IoT and machine learning are gaining popularity for collecting and processing patient bio-signals autonomously and automatically to provide a detailed picture of their health status. This improves the utility of bio-signals in detecting, predicting, and recommending critical events and treatments based on hidden information.

Dr. Sami Azam
Dr. Zubaer Ibna Mannan
Dr. Kawsar Ahmad
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. Information 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 1600 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

  • bio-signals
  • biomedical signal processing
  • healthcare
  • IoT
  • machine learning

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Published Papers (7 papers)

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Research

27 pages, 2569 KiB  
Article
Cognitive Handwriting Insights for Alzheimer’s Diagnosis: A Hybrid Framework
by Shafiq Ul Rehman and Uddalak Mitra
Information 2025, 16(3), 249; https://doi.org/10.3390/info16030249 - 20 Mar 2025
Viewed by 398
Abstract
Alzheimer’s disease (AD) is a persistent neurologic disorder that has no cure. For a successful treatment to be implemented, it is essential to diagnose AD at an early stage, which may occur up to eight years before dementia manifests. In this regard, a [...] Read more.
Alzheimer’s disease (AD) is a persistent neurologic disorder that has no cure. For a successful treatment to be implemented, it is essential to diagnose AD at an early stage, which may occur up to eight years before dementia manifests. In this regard, a new predictive machine learning model is proposed that works in two stages and takes advantage of both unsupervised and supervised learning approaches to provide a fast, affordable, yet accurate solution. The first stage involved fuzzy partitioning of a gold-standard dataset, DARWIN (Diagnosis AlzheimeR WIth haNdwriting). This dataset consists of clinical features and is designed to detect Alzheimer’s disease through handwriting analysis. To determine the optimal number of clusters, four Clustering Validity Indices (CVIs) were averaged, which we refer to as cognitive features. During the second stage, a predictive model was constructed exclusively from these cognitive features. In comparison to models relying on datasets featuring clinical attributes, models incorporating cognitive features showed substantial performance enhancements, ranging from 12% to 26%. Our proposed model surpassed all current state-of-the-art models, achieving a mean accuracy of 99%, mean sensitivity of 98%, mean specificity of 100%, mean precision of 100%, and mean MCC and Cohen’s Kappa of 98%, along with a mean AUC-ROC score of 99%. Hence, integrating the output of unsupervised learning into supervised machine learning models significantly improved their performance. In the process of crafting early interventions for individuals with a heightened risk of disease onset, our prognostic framework can aid in both the recruitment and advancement of clinical trials. Full article
(This article belongs to the Special Issue Detection and Modelling of Biosignals)
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26 pages, 4394 KiB  
Article
Neural Network Models for Prostate Zones Segmentation in Magnetic Resonance Imaging
by Saman Fouladi, Luca Di Palma, Fatemeh Darvizeh, Deborah Fazzini, Alessandro Maiocchi, Sergio Papa, Gabriele Gianini and Marco Alì
Information 2025, 16(3), 186; https://doi.org/10.3390/info16030186 - 28 Feb 2025
Viewed by 484
Abstract
Prostate cancer (PCa) is one of the most common tumors diagnosed in men worldwide, with approximately 1.7 million new cases expected by 2030. Most cancerous lesions in PCa are located in the peripheral zone (PZ); therefore, accurate identification of the location of the [...] Read more.
Prostate cancer (PCa) is one of the most common tumors diagnosed in men worldwide, with approximately 1.7 million new cases expected by 2030. Most cancerous lesions in PCa are located in the peripheral zone (PZ); therefore, accurate identification of the location of the lesion is essential for effective diagnosis and treatment. Zonal segmentation in magnetic resonance imaging (MRI) scans is critical and plays a key role in pinpointing cancerous regions and treatment strategies. In this work, we report on the development of three advanced neural network-based models: one based on ensemble learning, one on Meta-Net, and one on YOLO-V8. They were tailored for the segmentation of the central gland (CG) and PZ using a small dataset of 90 MRI scans for training, 25 MRIs for validation, and 24 scans for testing. The ensemble learning method, combining U-Net-based models (Attention-Res-U-Net, Vanilla-Net, and V-Net), achieved an IoU of 79.3% and DSC of 88.4% for CG and an IoU of 54.5% and DSC of 70.5% for PZ on the test set. Meta-Net, used for the first time in segmentation, demonstrated an IoU of 78% and DSC of 88% for CG, while YOLO-V8 outperformed both models with an IoU of 80% and DSC of 89% for CG and an IoU of 58% and DSC of 73% for PZ. Full article
(This article belongs to the Special Issue Detection and Modelling of Biosignals)
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19 pages, 2887 KiB  
Article
A Graduate Level Personalized Learning Environment in the Field of f-NIRS Signal Processing
by Dominique Persano Adorno and Giuseppe Costantino Giaconia
Information 2025, 16(3), 162; https://doi.org/10.3390/info16030162 - 21 Feb 2025
Viewed by 532
Abstract
Active student involvement and instruction through experience in everyday contexts are pedagogical approaches suitable to promote inquiry-based learning and improve learners’ cognitive skills. Nevertheless, many university and postgraduate courses offer lecture-based instructions of theoretical concepts to the students; little attention is still devoted [...] Read more.
Active student involvement and instruction through experience in everyday contexts are pedagogical approaches suitable to promote inquiry-based learning and improve learners’ cognitive skills. Nevertheless, many university and postgraduate courses offer lecture-based instructions of theoretical concepts to the students; little attention is still devoted to design hands-on activities, to improve practical/technical competencies and enhance students’ effective understanding of the concepts. The development of a personalized, student-centered learning environment that encourages teamwork and inquiry-based learning aligns with the contemporary push for interdisciplinary education in bioengineering fields. This is particularly relevant for fostering expertise in emerging technologies like functional Near-Infrared Spectroscopy (f-NIRS). In this framework, this paper reports a lab activity for bioelectronic engineering and/or biomedical science students focused on analyzing prefrontal cortex activation during a memory task, processing the f-NIRS signals. This pilot activity, conducted at the University of Palermo (Italy), involved Master’s and Ph.D. students working in teams to address challenges in experimental design. The study combines cutting-edge biosignal detection techniques with innovative educational strategies, offering substantial contributions to both bioengineering and educational research. The outcomes suggest that a hands-on and student-centered laboratory, experienced through a methodical sequence of self-directed learning activities, could considerably boost the student motivation to learn and the level of engagement in bioengineering and biosciences. Full article
(This article belongs to the Special Issue Detection and Modelling of Biosignals)
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19 pages, 4794 KiB  
Article
An Efficient Ensemble Approach for Brain Tumors Classification Using Magnetic Resonance Imaging
by Zubair Saeed, Tarraf Torfeh, Souha Aouadi, (Jim) Xiuquan Ji and Othmane Bouhali
Information 2024, 15(10), 641; https://doi.org/10.3390/info15100641 - 15 Oct 2024
Cited by 1 | Viewed by 2009
Abstract
Tumors in the brain can be life-threatening, making early and precise detection crucial for effective treatment and improved patient outcomes. Deep learning (DL) techniques have shown significant potential in automating the early diagnosis of brain tumors by analyzing magnetic resonance imaging (MRI), offering [...] Read more.
Tumors in the brain can be life-threatening, making early and precise detection crucial for effective treatment and improved patient outcomes. Deep learning (DL) techniques have shown significant potential in automating the early diagnosis of brain tumors by analyzing magnetic resonance imaging (MRI), offering a more efficient and accurate approach to classification. Deep convolutional neural networks (DCNNs), which are a sub-field of DL, have the potential to analyze rapidly and accurately MRI data and, as such, assist human radiologists, facilitating quicker diagnoses and earlier treatment initiation. This study presents an ensemble of three high-performing DCNN models, i.e., DenseNet169, EfficientNetB0, and ResNet50, for accurate classification of brain tumors and non-tumor MRI samples. Our proposed ensemble model demonstrates significant improvements over various evaluation parameters compared to individual state-of-the-art (SOTA) DCNN models. We implemented ten SOTA DCNN models, i.e., EfficientNetB0, ResNet50, DenseNet169, DenseNet121, SqueezeNet, ResNet34, ResNet18, VGG16, VGG19, and LeNet5, and provided a detailed performance comparison. We evaluated these models using two learning rates (LRs) of 0.001 and 0.0001 and two batch sizes (BSs) of 64 and 128 and identified the optimal hyperparameters for each model. Our findings indicate that the ensemble approach outperforms individual models, having 92% accuracy, 90% precision, 92% recall, and an F1 score of 91% at a 64 BS and 0.0001 LR. This study not only highlights the superior performance of the ensemble technique but also offers a comprehensive comparison with the latest research. Full article
(This article belongs to the Special Issue Detection and Modelling of Biosignals)
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13 pages, 3191 KiB  
Article
Modeling the Biocatalytic Method of Lipid Extraction Using Artificial Neural Networks
by Anton V. Shafrai, Alexander Yu. Prosekov and Elena A. Vechtomova
Information 2023, 14(8), 452; https://doi.org/10.3390/info14080452 - 9 Aug 2023
Cited by 1 | Viewed by 1440
Abstract
The paper presents the data on lipid fraction extraction from the raw fat of hibernating hunting animals. The processing of valuable raw materials must be maximized. For this purpose, various methods of rendering are used. As a result of temperature exposure, the protein [...] Read more.
The paper presents the data on lipid fraction extraction from the raw fat of hibernating hunting animals. The processing of valuable raw materials must be maximized. For this purpose, various methods of rendering are used. As a result of temperature exposure, the protein part of raw fat undergoes significant changes. The protein denatures under the influence of temperature, and the dross formed during the rendering process absorbs and retains up to 30% of the fat. The authors propose using proteolytic enzyme preparations for a more complete extraction of fats, as the enzymes will hydrolyze the protein into compounds of lower molecular weight both before and during the rendering process. The experiment proved that the biocatalytic method allows achieving a fat yield of more than 95%. The best result can be obtained if the rendering is carried out at optimal parameters, which can be defined using a mathematical model. Mathematical modeling was carried out using an artificial neural network. During the study, a fully connected neural network was designed; it had eight hidden layers with 64 neurons in each, and its accuracy was measured by mean relative error, which amounted to 5.16%. With the help of the network, the optimal values of applied concentration, temperature and duration of rendering, at which a fat yield of more than 98% is achieved, were determined for each enzyme preparation. After that, the obtained values were confirmed experimentally. Thus, the study showed the efficiency of using artificial neural networks for modeling the biocatalytic method of lipid extraction. Full article
(This article belongs to the Special Issue Detection and Modelling of Biosignals)
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25 pages, 3601 KiB  
Article
A New ECG Data Processing Approach to Developing an Accurate Driving Fatigue Detection Framework with Heart Rate Variability Analysis and Ensemble Learning
by Junartho Halomoan, Kalamullah Ramli, Dodi Sudiana, Teddy Surya Gunawan and Muhammad Salman
Information 2023, 14(4), 210; https://doi.org/10.3390/info14040210 - 30 Mar 2023
Cited by 12 | Viewed by 3641
Abstract
More than 1.3 million people are killed in traffic accidents annually. Road traffic accidents are mostly caused by human error. Therefore, an accurate driving fatigue detection system is required for drivers. Most driving fatigue detection studies concentrated on improving feature engineering and classification [...] Read more.
More than 1.3 million people are killed in traffic accidents annually. Road traffic accidents are mostly caused by human error. Therefore, an accurate driving fatigue detection system is required for drivers. Most driving fatigue detection studies concentrated on improving feature engineering and classification methods. We propose a novel driving fatigue detection framework concentrating on the development of the preprocessing, feature extraction, and classification stages to improve the classification accuracy of fatigue states. The proposed driving fatigue detection framework measures fatigue using a two-electrode ECG. The resampling method and heart rate variability analysis were used to extract features from the ECG data, and an ensemble learning model was utilized to classify fatigue states. To achieve the best model performance, 40 possible scenarios were applied: a combination of 5 resampling scenarios, 2 feature extraction scenarios, and 4 classification model scenarios. It was discovered that the combination of a resampling method with a window duration of 300 s and an overlap of 270 s, 54 extracted features, and AdaBoost yielded an optimum accuracy of 98.82% for the training dataset and 81.82% for the testing dataset. Furthermore, the preprocessing resampling method had the greatest impact on the model’s performance; it is a new approach presented in this study. Full article
(This article belongs to the Special Issue Detection and Modelling of Biosignals)
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14 pages, 2904 KiB  
Article
Analysis of Diabetic Retinopathy (DR) Based on the Deep Learning
by Abdul Muiz Fayyaz, Muhammad Imran Sharif, Sami Azam, Asif Karim and Jamal El-Den
Information 2023, 14(1), 30; https://doi.org/10.3390/info14010030 - 4 Jan 2023
Cited by 34 | Viewed by 4983
Abstract
If Diabetic Retinopathy (DR) patients do not receive quick diagnosis and treatment, they may lose vision. DR, an eye disorder caused by high blood glucose, is becoming more prevalent worldwide. Once early warning signs are detected, the severity of the disease must be [...] Read more.
If Diabetic Retinopathy (DR) patients do not receive quick diagnosis and treatment, they may lose vision. DR, an eye disorder caused by high blood glucose, is becoming more prevalent worldwide. Once early warning signs are detected, the severity of the disease must be validated before choosing the best treatment. In this research, a deep learning network is used to automatically detect and classify DR fundus images depending on severity using AlexNet and Resnet101-based feature extraction. Interconnected layers helps to identify the critical features or characteristics; in addition, Ant Colony systems also help choose the characteristics. Passing these chosen attributes through SVM with multiple kernels yielded the final classification model with promising accuracy. The experiment based on 750 features proves that the proposed approach has achieved an accuracy of 93%. Full article
(This article belongs to the Special Issue Detection and Modelling of Biosignals)
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