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 2024) | Viewed by 6645

Special Issue Editors

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

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Guest Editor
Global Campus, Kyungdong University, Gangwon-do 24764, Korea
Interests: memristor; neuromorphic; bioelectronics; machine learning

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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 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

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Keywords

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

Published Papers (3 papers)

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Research

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 - 09 Aug 2023
Viewed by 842
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 6 | Viewed by 1996
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 - 04 Jan 2023
Cited by 15 | Viewed by 3034
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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