AI Enabled Medical Data Analysis and Processing in Internet of Medical Things (IoMT)

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

Deadline for manuscript submissions: closed (7 April 2023) | Viewed by 18922

Special Issue Editors


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Guest Editor
Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun 248002, India
Interests: medical Imaging; medical signal processing and classification; Internet of Medical Things

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Guest Editor
School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India

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Guest Editor
Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia

Special Issue Information

Dear Colleagues,

An intelligent, networked medical device, known as the IoMT, connects people from all around the world. This enables the monitoring of a significant amount of medical data that were previously unknown. The demand for medical data, particularly visual depictions of health, such as signals and images, has recently increased. IoT applications, including as wearables, prescription tracking systems, remote patient monitoring, and networks for the medical supply chain, are widely used in the medical and healthcare sectors. IoMT aids physicians in providing more accurate diagnoses by maintaining a permanent record of a patient's current state of health. Patients can communicate with their doctors and nurses via smartphone applications that are Internet of Things (IoT)-enabled . They make it possible for medical personnel to treat many patients in a short period of time. Many studies have been conducted in the field of medical image and signal processing as a result of these types of issues. Medical imaging and signal processing have advanced significantly in recent years, but many questions still need to be resolved. Few IoMT apps have looked into the prospect of recording medical images as data and sending them through a wireless sensor network, while most IoMT apps concentrate on power efficiency (WSN). This is due to the fact that a WSN has a strict limit on how much bandwidth can be used at once. We are compiling this collection of current articles that examine the nature of the issue, and the various solutions that people have proposed over time. We want to use a WSN to send images of medical concerns instead of raw sensor data due to the persuasive power of visual proof. In situations such as medical data surveillance, when failing to do so could lead to a significant number of false positives, the significance of this cannot be overstated. To realise the full potential of the Internet of Medical Things (IoMT), research in image processing, wireless sensor networks (WSN), and other areas is required. We are particularly interested in the difficulties of image data transmission over the Internet of Things, hence we are looking for researchers who use IoMT in the field of medical imaging and are eager to address these problems. We hope to collect their creative solutions for overcoming the challenges of delivering visual data across the Internet of Things as part of this collection. This Special Issue will cover topics including medical imaging, the transmission of medical images and signals over a secure WSN, and the use of the Internet of Things to analyse medical activity in real-time.

The topics covered in this volume are given below, but this Special Issue is not limited to the mentioned ones:
  • Medical Image/signal processing such as enhancement, restoration, and so on;
  • Machine/deep learning for medical image processing and sensor network in IoMT;
  • Energy efficient algorithms for medical image processing in IoMT;
  • EEG/ECG based anomaly detection and analysis in IoMT;
  • Classification over medical signals such as ECG, EEG, and so on;
  • AI-assisted methodologies in bioimage informatics applications;
  • Intelligent bioimage informatics in health management;
  • Deep learning-based medical image analysis and enhancement;
  • Development in healthcare applications using deep learning;
  • Medical image forensics based on deep learning.

Dr. Manoj Diwakar
Dr. Prabhishek Singh
Dr. Vinayakumar Ravi
Guest Editors

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Keywords

  • EEG
  • ECG
  • medical imaging
  • deep learning and artificial intelligence

Published Papers (7 papers)

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Editorial

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4 pages, 194 KiB  
Editorial
Medical Data Analysis Meets Artificial Intelligence (AI) and Internet of Medical Things (IoMT)
by Manoj Diwakar, Prabhishek Singh and Vinayakumar Ravi
Bioengineering 2023, 10(12), 1370; https://doi.org/10.3390/bioengineering10121370 - 29 Nov 2023
Viewed by 1338
Abstract
AI is a contemporary methodology rooted in the field of computer science [...] Full article

Research

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17 pages, 2728 KiB  
Article
Prediction of COVID-19 Using a WOA-BILSTM Model
by Xinyue Yang and Shuangyin Li
Bioengineering 2023, 10(8), 883; https://doi.org/10.3390/bioengineering10080883 - 25 Jul 2023
Cited by 3 | Viewed by 1234
Abstract
The COVID-19 pandemic has had a significant impact on the world, highlighting the importance of the accurate prediction of infection numbers. Given that the transmission of SARS-CoV-2 is influenced by temporal and spatial factors, numerous researchers have employed neural networks to address this [...] Read more.
The COVID-19 pandemic has had a significant impact on the world, highlighting the importance of the accurate prediction of infection numbers. Given that the transmission of SARS-CoV-2 is influenced by temporal and spatial factors, numerous researchers have employed neural networks to address this issue. Accordingly, we propose a whale optimization algorithm–bidirectional long short-term memory (WOA-BILSTM) model for predicting cumulative confirmed cases. In the model, we initially input regional epidemic data, including cumulative confirmed, cured, and death cases, as well as existing cases and daily confirmed, cured, and death cases. Subsequently, we utilized the BILSTM as the base model and incorporated WOA to optimize the specific parameters. Our experiments employed epidemic data from Beijing, Guangdong, and Chongqing in China. We then compared our model with LSTM, BILSTM, GRU, CNN, CNN-LSTM, RNN-GRU, DES, ARIMA, linear, Lasso, and SVM models. The outcomes demonstrated that our model outperformed these alternatives and retained the highest accuracy in complex scenarios. In addition, we also used Bayesian and grid search algorithms to optimize the BILSTM model. The results showed that the WOA model converged fast and found the optimal solution more easily. Thus, our model can assist governments in developing more effective control measures. Full article
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17 pages, 3913 KiB  
Article
GIT-Net: An Ensemble Deep Learning-Based GI Tract Classification of Endoscopic Images
by Hemalatha Gunasekaran, Krishnamoorthi Ramalakshmi, Deepa Kanmani Swaminathan, Andrew J and Manuel Mazzara
Bioengineering 2023, 10(7), 809; https://doi.org/10.3390/bioengineering10070809 - 5 Jul 2023
Cited by 9 | Viewed by 2281
Abstract
This paper presents an ensemble of pre-trained models for the accurate classification of endoscopic images associated with Gastrointestinal (GI) diseases and illnesses. In this paper, we propose a weighted average ensemble model called GIT-NET to classify GI-tract diseases. We evaluated the model on [...] Read more.
This paper presents an ensemble of pre-trained models for the accurate classification of endoscopic images associated with Gastrointestinal (GI) diseases and illnesses. In this paper, we propose a weighted average ensemble model called GIT-NET to classify GI-tract diseases. We evaluated the model on a KVASIR v2 dataset with eight classes. When individual models are used for classification, they are often prone to misclassification since they may not be able to learn the characteristics of all the classes adequately. This is due to the fact that each model may learn the characteristics of specific classes more efficiently than the other classes. We propose an ensemble model that leverages the predictions of three pre-trained models, DenseNet201, InceptionV3, and ResNet50 with accuracies of 94.54%, 88.38%, and 90.58%, respectively. The predictions of the base learners are combined using two methods: model averaging and weighted averaging. The performances of the models are evaluated, and the model averaging ensemble has an accuracy of 92.96% whereas the weighted average ensemble has an accuracy of 95.00%. The weighted average ensemble outperforms the model average ensemble and all individual models. The results from the evaluation demonstrate that utilizing an ensemble of base learners can successfully classify features that were incorrectly learned by individual base learners. Full article
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16 pages, 464 KiB  
Article
ECG Heartbeat Classification Using Machine Learning and Metaheuristic Optimization for Smart Healthcare Systems
by Mahmoud Hassaballah, Yaser M. Wazery, Ibrahim E. Ibrahim and Aly Farag
Bioengineering 2023, 10(4), 429; https://doi.org/10.3390/bioengineering10040429 - 28 Mar 2023
Cited by 14 | Viewed by 5692
Abstract
Early diagnosis and classification of arrhythmia from an electrocardiogram (ECG) plays a significant role in smart healthcare systems for the health monitoring of individuals with cardiovascular diseases. Unfortunately, the nonlinearity and low amplitude of ECG recordings make the classification process difficult. Thus, the [...] Read more.
Early diagnosis and classification of arrhythmia from an electrocardiogram (ECG) plays a significant role in smart healthcare systems for the health monitoring of individuals with cardiovascular diseases. Unfortunately, the nonlinearity and low amplitude of ECG recordings make the classification process difficult. Thus, the performance of most traditional machine learning (ML) classifiers is questionable, as the interrelationship between the learning parameters is not well modeled, especially for data features with high dimensions. To address the limitations of ML classifiers, this paper introduces an automatic arrhythmia classification approach based on the integration of a recent metaheuristic optimization (MHO) algorithm and ML classifiers. The role of the MHO is to optimize the search parameters of the classifiers. The approach consists of three steps: the preprocessing of the ECG signal, the extraction of the features, and the classification. The learning parameters of four supervised ML classifiers were utilized for the classification task; support vector machine (SVM), k-nearest neighbors (kNNs), gradient boosting decision tree (GBDT), and random forest (RF) were optimized using the MHO algorithm. To validate the advantage of the proposed approach, several experiments were conducted on three common databases, including the Massachusetts Institute of Technology (MIT-BIH), the European Society of Cardiology ST-T (EDB), and the St. Petersburg Institute of Cardiological Techniques 12-lead Arrhythmia (INCART). The obtained results showed that the performance of all the tested classifiers were significantly improved after integrating the MHO algorithm, with the average ECG arrhythmia classification accuracy reaching 99.92% and a sensitivity of 99.81%, outperforming the state-of the-art methods. Full article
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14 pages, 1801 KiB  
Article
Compression of Bio-Signals Using Block-Based Haar Wavelet Transform and COVIDOA for IoMT Systems
by Doaa Sami Khafaga, Eman Abdullah Aldakheel, Asmaa M. Khalid, Hanaa M. Hamza and Khaid M. Hosny
Bioengineering 2023, 10(4), 406; https://doi.org/10.3390/bioengineering10040406 - 24 Mar 2023
Cited by 3 | Viewed by 1195
Abstract
Background: Bio-signals are the essential data that smart healthcare systems require for diagnosing and treating common diseases. However, the amount of these signals that need to be processed and analyzed by healthcare systems is huge. Dealing with such a vast amount of data [...] Read more.
Background: Bio-signals are the essential data that smart healthcare systems require for diagnosing and treating common diseases. However, the amount of these signals that need to be processed and analyzed by healthcare systems is huge. Dealing with such a vast amount of data presents difficulties, such as the need for high storage and transmission capabilities. In addition, retaining the most useful clinical information in the input signal is essential while applying compression. Methods: This paper proposes an algorithm for the efficient compression of bio-signals for IoMT applications. This algorithm extracts the features of the input signal using block-based HWT and then selects the most important features for reconstruction using the novel COVIDOA. Results: We utilized two different public datasets for evaluation: MIT-BIH arrhythmia and EEG Motor Movement/Imagery, for ECG and EEG signals, respectively. The proposed algorithm’s average values for CR, PRD, NCC, and QS are 18.06, 0.2470, 0.9467, and 85.366 for ECG signals and 12.6668, 0.4014, 0.9187, and 32.4809 for EEG signals. Further, the proposed algorithm shows its efficiency over other existing techniques regarding processing time. Conclusions: Experiments show that the proposed method successfully achieved a high CR while maintaining an excellent level of signal reconstruction in addition to its reduced processing time compared with the existing techniques. Full article
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13 pages, 5691 KiB  
Article
Method for Solving Difficulties in Rhythm Classification Caused by Few Samples and Similar Characteristics in Electrocardiograms
by Jaewon Lee and Miyoung Shin
Bioengineering 2023, 10(2), 196; https://doi.org/10.3390/bioengineering10020196 - 2 Feb 2023
Cited by 1 | Viewed by 1497
Abstract
A method for accurately analyzing electrocardiograms (ECGs), which are obtained from electrical signals generated by cardiac activity, is essential in heart disease diagnosis. However, rhythms are typically obtained with relatively few data samples and similar characteristics, making them difficult to classify. To solve [...] Read more.
A method for accurately analyzing electrocardiograms (ECGs), which are obtained from electrical signals generated by cardiac activity, is essential in heart disease diagnosis. However, rhythms are typically obtained with relatively few data samples and similar characteristics, making them difficult to classify. To solve these issues, we proposed a novel method that distinguishes a given ECG rhythm using a beat score map (BSM) image. Through the proposed method, the associations between beats and previously used features, such as the R–R interval, were considered. Rhythm classification was implemented by training a convolutional neural network model and using transfer learning with the created BSM image. As a result, the proposed method for ECG rhythms with small data samples showed significant results. It also showed good performance in differentiating atrial fibrillation (AFIB) and atrial flutter (AFL) rhythms, which are difficult to distinguish due to their similar characteristics. The performance for rhythms with a small number of samples of the proposed method is 20% better than an existing method. In addition, the performance based on the F-1 score for classifying AFIB and AFL of the proposed method is 30% better than the existing method. This study solved the previous limitations caused by small sample numbers and similar rhythms. Full article
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21 pages, 5814 KiB  
Article
Assessment of Model Accuracy in Eyes Open and Closed EEG Data: Effect of Data Pre-Processing and Validation Methods
by Jamolbek Mattiev, Jakob Sajovic, Gorazd Drevenšek and Peter Rogelj
Bioengineering 2023, 10(1), 42; https://doi.org/10.3390/bioengineering10010042 - 29 Dec 2022
Cited by 2 | Viewed by 2523
Abstract
Eyes open and eyes closed data is often used to validate novel human brain activity classification methods. The cross-validation of models trained on minimally preprocessed data is frequently utilized, regardless of electroencephalography data comprised of data resulting from muscle activity and environmental noise, [...] Read more.
Eyes open and eyes closed data is often used to validate novel human brain activity classification methods. The cross-validation of models trained on minimally preprocessed data is frequently utilized, regardless of electroencephalography data comprised of data resulting from muscle activity and environmental noise, affecting classification accuracy. Moreover, electroencephalography data of a single subject is often divided into smaller parts, due to limited availability of large datasets. The most frequently used method for model validation is cross-validation, even though the results may be affected by overfitting to the specifics of brain activity of limited subjects. To test the effects of preprocessing and classifier validation on classification accuracy, we tested fourteen classification algorithms implemented in WEKA and MATLAB, tested on comprehensively and simply preprocessed electroencephalography data. Hold-out and cross-validation were used to compare the classification accuracy of eyes open and closed data. The data of 50 subjects, with four minutes of data with eyes closed and open each was used. The algorithms trained on simply preprocessed data were superior to the ones trained on comprehensively preprocessed data in cross-validation testing. The reverse was true when hold-out accuracy was examined. Significant increases in hold-out accuracy were observed if the data of different subjects was not strictly separated between the test and training datasets, showing the presence of overfitting. The results show that comprehensive data preprocessing can be advantageous for subject invariant classification, while higher subject-specific accuracy can be attained with simple preprocessing. Researchers should thus state the final intended use of their classifier. Full article
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