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Diagnostics
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Artificial Intelligence in Medical Signal Processing and Analysis
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Artificial Intelligence in Medical Signal Processing and Analysis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 18191

Special Issue Editor

Dr. Vadim V. Grubov

E-Mail Website
Guest Editor
Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Universitetskaya Str. 1, 420500 Innopolis, Russia
Interests: neurological diseases; EEG pattern recognition; absence epilepsy; removal of EEG artifacts

Special Issue Information

Dear Colleagues, 

Artificial intelligence (AI) field is commonly associated with the studying of “intelligent agents”—systems capable of perceiving the environment and taking actions to maximize chances of achieving system’s goals. AI finds many applications in science and engineering, and it is certainly capable of analyzing complex medical data.

Actively developing approaches for medical data acquisition provides a possibility of accumulating massive amounts of biomedical data. For example, numerous neuroimaging techniques such as EEG, MEG, fNIRS, fMRI, etc., can be used to obtain multimodal information about various states of the human brain. There are certain well-known methods for medical signal processing and analysis; however, the complexity of the data calls for more advanced approaches.

Since AI “mimics” cognitive functions of natural intelligence to some degree, it has the potential to discover hidden meaningful relations within medical datasets that are hard to find with classic approaches. Knowledge on such relations can be used in clinical studies for diagnostics, treatment, and possible outcome predictions, as well as in engineering, for example, in “brain–computer” interfaces, or even at the junction of these two fields, in medical expert systems.

Thus, this Special Issue on “Artificial Intelligence in Medical Signal Processing and Analysis” aims to attract high-quality research studies from scientists and specialists that advance the application of state-of-the-art AI techniques in medical data processing.

Potential areas of interest include but are not limited to the following directions:

  • Innovative methods of AI for biomedical big data processing and analysis including artificial neural networks, fuzzy expert systems, evolutionary computation, hybrid intelligent systems, etc.;
  • Fundamental biomedical studies with AI-based methods;
  • AI in clinical diagnostics and treatment;
  • Development of brain–computer interfaces;
  • Medical expert systems based on the principles of AI.

Dr. Vadim V. Grubov
Guest Editor

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. Diagnostics is an international peer-reviewed open access semimonthly 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 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

  • neuroscience
  • artificial intelligence
  • machine learning
  • artificial neural networks
  • fuzzy expert systems
  • evolutionary computation
  • hybrid intelligent systems
  • neuroimaging techniques
  • biomedical big data
  • brain–computer interfaces
  • medical expert systems

Published Papers (7 papers)

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Research

14 pages, 270 KiB  
Article
Sepsis Trajectory Prediction Using Privileged Information and Continuous Physiological Signals
by Olivia P. Alge, Jonathan Gryak, J. Scott VanEpps and Kayvan Najarian
Diagnostics 2024, 14(3), 234; https://doi.org/10.3390/diagnostics14030234 - 23 Jan 2024
Viewed by 586
Abstract
The aim of this research is to apply the learning using privileged information paradigm to sepsis prognosis. We used signal processing of electrocardiogram and electronic health record data to construct support vector machines with and without privileged information to predict an increase in [...] Read more.
The aim of this research is to apply the learning using privileged information paradigm to sepsis prognosis. We used signal processing of electrocardiogram and electronic health record data to construct support vector machines with and without privileged information to predict an increase in a given patient’s quick-Sequential Organ Failure Assessment score, using a retrospective dataset. We applied this to both a small, critically ill cohort and a broader cohort of patients in the intensive care unit. Within the smaller cohort, privileged information proved helpful in a signal-informed model, and across both cohorts, electrocardiogram data proved to be informative to creating the prediction. Although learning using privileged information did not significantly improve results in this study, it is a paradigm worth studying further in the context of using signal processing for sepsis prognosis. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medical Signal Processing and Analysis)
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10 pages, 1605 KiB  
Article
Characterization of Anesthesia in Rats from EEG in Terms of Long-Range Correlations
by Inna A. Blokhina, Alexander A. Koronovskii, Jr., Alexander V. Dmitrenko, Inna V. Elizarova, Tatyana V. Moiseikina, Matvey A. Tuzhilkin, Oxana V. Semyachkina-Glushkovskaya and Alexey N. Pavlov
Diagnostics 2023, 13(3), 426; https://doi.org/10.3390/diagnostics13030426 - 24 Jan 2023
Cited by 2 | Viewed by 1146
Abstract
Long-range correlations are often used as diagnostic markers in physiological research. Due to the limitations of conventional techniques, their characterizations are typically carried out with alternative approaches, such as the detrended fluctuation analysis (DFA). In our previous works, we found EEG-related markers of [...] Read more.
Long-range correlations are often used as diagnostic markers in physiological research. Due to the limitations of conventional techniques, their characterizations are typically carried out with alternative approaches, such as the detrended fluctuation analysis (DFA). In our previous works, we found EEG-related markers of the blood–brain barrier (BBB), which limits the penetration of major drugs into the brain. However, anesthetics can penetrate the BBB, affecting its function in a dose-related manner. Here, we study two types of anesthesia widely used in experiments on animals, including zoletil/xylazine and isoflurane in optimal doses not associated with changes in the BBB. Based on DFA, we reveal informative characteristics of the electrical activity of the brain during such doses that are important for controlling the depth of anesthesia in long-term experiments using magnetic resonance imaging, multiphoton microscopy, etc., which are crucial for the interpretation of experimental results. These findings provide an important informative platform for the enhancement and refinement of surgery, since the EEG-based DFA analysis of BBB can easily be used during surgery as a tool for characterizing normal BBB functions under anesthesia. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medical Signal Processing and Analysis)
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10 pages, 1032 KiB  
Article
An Improved Diagnostic of the Mycobacterium tuberculosis Drug Resistance Status by Applying a Decision Tree to Probabilities Assigned by the CatBoost Multiclassifier of Matrix Metalloproteinases Biomarkers
by Anastasia I. Lavrova and Eugene B. Postnikov
Diagnostics 2022, 12(11), 2847; https://doi.org/10.3390/diagnostics12112847 - 17 Nov 2022
Cited by 2 | Viewed by 1119
Abstract
In this work, we discuss an opportunity to use a set of the matrix metalloproteinases MMP-1, MMP-8, and MMP-9 and the tissue inhibitor TIMP, the concentrations of which can be easily obtained via a blood test from patients suffering from tuberculosis, as the [...] Read more.
In this work, we discuss an opportunity to use a set of the matrix metalloproteinases MMP-1, MMP-8, and MMP-9 and the tissue inhibitor TIMP, the concentrations of which can be easily obtained via a blood test from patients suffering from tuberculosis, as the biomarker for a fast diagnosis of the drug resistance status of Mycobacterium tuberculosis. The diagnostic approach is based on machine learning with the CatBoost system, which has been supplied with additional postprocessing. The latter refers not only to the simple probabilities of ML-predicted outcomes but also to the decision tree-like procedure, which takes into account the presence of strict zeros in the primary set of probabilities. It is demonstrated that this procedure significantly elevates the accuracy of distinguishing between sensitive, multi-, and extremely drug-resistant strains. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medical Signal Processing and Analysis)
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13 pages, 2658 KiB  
Article
Mental Strategies in a P300-BCI: Visuomotor Transformation Is an Option
by Nikolay Syrov, Lev Yakovlev, Varvara Nikolaeva, Alexander Kaplan and Mikhail Lebedev
Diagnostics 2022, 12(11), 2607; https://doi.org/10.3390/diagnostics12112607 - 27 Oct 2022
Cited by 2 | Viewed by 1819
Abstract
Currently, P300-BCIs are mostly used for spelling tasks, where the number of commands is equal to the number of stimuli that evoke event-related potentials (ERPs). Increasing this number slows down the BCI operation because each stimulus has to be presented several times for [...] Read more.
Currently, P300-BCIs are mostly used for spelling tasks, where the number of commands is equal to the number of stimuli that evoke event-related potentials (ERPs). Increasing this number slows down the BCI operation because each stimulus has to be presented several times for better classification. Furthermore, P300 spellers typically do not utilize potentially useful imagery-based approaches, such as the motor imagery successfully practiced in motor rehabilitation. Here, we tested a P300-BCI with a motor-imagery component. In this BCI, the number of commands was increased by adding mental strategies instead of increasing the number of targets. Our BCI had only two stimuli and four commands. The subjects either counted target appearances mentally or imagined hand movements toward the targets. In this design, the motor-imagery paradigm enacted a visuomotor transformation known to engage cortical and subcortical networks participating in motor control. The operation of these networks suffers in neurological conditions such as stroke, so we view this BCI as a potential tool for the rehabilitation of patients. As an initial step toward the development of this clinical method, sixteen healthy participants were tested. Consistent with our expectation that mental strategies would result in distinct EEG activities, ERPs were different depending on whether subjects counted stimuli or imagined movements. These differences were especially clear in the late ERP components localized in the frontal and centro-parietal regions. We conclude that (1) the P300 paradigm is suitable for enacting visuomotor transformations and (2) P300-based BCIs with multiple mental strategies could be used in applications where the number of possible outputs needs to be increased while keeping the number of targets constant. As such, our approach adds to both the development of versatile BCIs and clinical approaches to rehabilitation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medical Signal Processing and Analysis)
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14 pages, 972 KiB  
Article
TMP19: A Novel Ternary Motif Pattern-Based ADHD Detection Model Using EEG Signals
by Prabal Datta Barua, Sengul Dogan, Mehmet Baygin, Turker Tuncer, Elizabeth Emma Palmer, Edward J. Ciaccio and U. Rajendra Acharya
Diagnostics 2022, 12(10), 2544; https://doi.org/10.3390/diagnostics12102544 - 20 Oct 2022
Cited by 7 | Viewed by 2021
Abstract
Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition worldwide. In this research, we used an ADHD electroencephalography (EEG) dataset containing more than 4000 EEG signals. Moreover, these EEGs are noisy signals. A new hand-modeled EEG classification model has been proposed to [...] Read more.
Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition worldwide. In this research, we used an ADHD electroencephalography (EEG) dataset containing more than 4000 EEG signals. Moreover, these EEGs are noisy signals. A new hand-modeled EEG classification model has been proposed to separate healthy versus ADHD individuals using the EEG signals. In this model, a new ternary motif pattern (TMP) has been incorporated. We have mimicked deep learning networks to create this hand-modeled classification method. The Tunable Q Wavelet Transform (TQWT) has been utilized to generate wavelet subbands. We applied the proposed TMP and statistics to construct informative features from both raw EEG signals and wavelet bands by generating TQWT. Herein, features have been generated by 18 subbands and the original EEG signal. Thus, this model is named TMP19. The most informative features have been chosen by deploying neighborhood component analysis (NCA), and the selected features have been classified using the k-nearest neighbor (kNN) classifier. The used ADHD EEG dataset has 14 channels. Thus, these three phases—(i) feature extraction with TQWT, TMP, and statistics; (ii) feature selection by deploying NCA; and (iii) classification with kNN—have been applied to each channel. Iterative hard majority voting (IHMV) has been applied to obtain a higher and more general classification response. Our model attained 95.57% and 77.93% classification accuracies by deploying 10-fold and leave one subject out (LOSO) cross-validations, respectively. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medical Signal Processing and Analysis)
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15 pages, 4390 KiB  
Article
Automatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach
by Atheer Bassel, Amjed Basil Abdulkareem, Zaid Abdi Alkareem Alyasseri, Nor Samsiah Sani and Husam Jasim Mohammed
Diagnostics 2022, 12(10), 2472; https://doi.org/10.3390/diagnostics12102472 - 12 Oct 2022
Cited by 32 | Viewed by 8014
Abstract
Skin cancer is one of the major types of cancer with an increasing incidence in recent decades. The source of skin cancer arises in various dermatologic disorders. Skin cancer is classified into various types based on texture, color, morphological features, and structure. The [...] Read more.
Skin cancer is one of the major types of cancer with an increasing incidence in recent decades. The source of skin cancer arises in various dermatologic disorders. Skin cancer is classified into various types based on texture, color, morphological features, and structure. The conventional approach for skin cancer identification needs time and money for the predicted results. Currently, medical science is utilizing various tools based on digital technology for the classification of skin cancer. The machine learning-based classification approach is the robust and dominant approach for automatic methods of classifying skin cancer. The various existing and proposed methods of deep neural network, support vector machine (SVM), neural network (NN), random forest (RF), and K-nearest neighbor are used for malignant and benign skin cancer identification. In this study, a method was proposed based on the stacking of classifiers with three folds towards the classification of melanoma and benign skin cancers. The system was trained with 1000 skin images with the categories of melanoma and benign. The training and testing were performed using 70 and 30 percent of the overall data set, respectively. The primary feature extraction was conducted using the Resnet50, Xception, and VGG16 methods. The accuracy, F1 scores, AUC, and sensitivity metrics were used for the overall performance evaluation. In the proposed Stacked CV method, the system was trained in three levels by deep learning, SVM, RF, NN, KNN, and logistic regression methods. The proposed method for Xception techniques of feature extraction achieved 90.9% accuracy and was stronger compared to ResNet50 and VGG 16 methods. The improvement and optimization of the proposed method with a large training dataset could provide a reliable and robust skin cancer classification system. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medical Signal Processing and Analysis)
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20 pages, 1245 KiB  
Article
The Complexity of the Arterial Blood Pressure Regulation during the Stress Test
by Naseha Wafa Qammar, Ugnė Orinaitė, Vaiva Šiaučiūnaitė, Alfonsas Vainoras, Gintarė Šakalytė and Minvydas Ragulskis
Diagnostics 2022, 12(5), 1256; https://doi.org/10.3390/diagnostics12051256 - 18 May 2022
Cited by 3 | Viewed by 1828
Abstract
In this study, two categories of persons with normal and high ABP are subjected to the bicycle stress test (9 persons with normal ABP and 10 persons with high ABP). All persons are physically active men but not professional sportsmen. The mean and [...] Read more.
In this study, two categories of persons with normal and high ABP are subjected to the bicycle stress test (9 persons with normal ABP and 10 persons with high ABP). All persons are physically active men but not professional sportsmen. The mean and the standard deviation of age is 41.11 ± 10.21 years; height 178.88 ± 0.071 m; weight 80.53 ± 10.01 kg; body mass index 25.10 ± 2.06 kg/m2. Machine learning algorithms are employed to build a set of rules for the classification of the performance during the stress test. The heart rate, the JT interval, and the blood pressure readings are observed during the load and the recovery phases of the exercise. Although it is obvious that the two groups of persons will behave differently throughout the bicycle stress test, with this novel study, we are able to detect subtle variations in the rate at which these changes occur. This paper proves that these differences are measurable and substantial to detect subtle differences in the self-organization of the human cardiovascular system. It is shown that the data collected during the load phase of the stress test plays a more significant role than the data collected during the recovery phase. The data collected from the two groups of persons are approximated by Gaussian distribution. The introduced classification algorithm based on the statistical analysis and the triangle coordinate system helps to determine whether the reaction of the cardiovascular system of a new candidate is more pronounced by an increased heart rate or an increased blood pressure during the stress test. The developed approach produces valuable information about the self-organization of human cardiovascular system during a physical exercise. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medical Signal Processing and Analysis)
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