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M-MSSEU: source-free domain adaptation for multi-modal stroke lesion segmentation using shadowed sets and evidential uncertainty
AbstractDue to the unavailability of source domain data encountered in unsupervised domain adaptation, there has been an increasing number of studies on source-free domain adaptation (SFDA) in recent years. To better solve the SFDA problem and effectively leverage the multi-modal information in medical images, this paper presents a novel SFDA method for multi-modal stroke lesion segmentation in which evidential deep learning instead of convolutional neural network. Specifically, for multi-modal stroke images, we design a multi-modal opinion fusion module which uses Dempster-Shafer evidence theory for decision fusion of dif...
Source: Health Information Science and Systems - September 28, 2023 Category: Information Technology Source Type: research

An error-bounded median filter for correcting ECG baseline wander
AbstractThe baseline wander (BLW) in electrocardiogram (ECG) is a common disturbance that has a significant influence on the ECG wave pattern recognition. Many methods, such as IIR filter, mean filter, etc., can be used to correct BLW; However, most of them work on the original ECG signals. Compressed ECG data are economic for data storage and transmission, and if the baseline correction can be processed on them, it will be more efficient than we decompress them first and then do such correction. In this paper, we propose a new type of median filterCM_Filter, which works on the synopses of straight lines achieved from ECG ...
Source: Health Information Science and Systems - September 26, 2023 Category: Information Technology Source Type: research

Medimatrix: innovative pre-training of grayscale images for rheumatoid arthritis diagnosis revolutionises medical image classification
AbstractEfficient and accurate medical image classification (MIC) methods face two major challenges: (1) high similarity between images of different disease classes; and (2) generating large medical image datasets for training deep neural networks is challenging due to privacy restrictions and the need for expert ground truth annotations. In this paper, we introduce a novel deep learning method called pre-training grayscale images with supervised learning for MIC (MediMatrix). Instead of pre-training on color ImageNet, our approach uses MediMatrix on grayscale ImageNet. To improve the performance of the network, we introdu...
Source: Health Information Science and Systems - September 26, 2023 Category: Information Technology Source Type: research

Differential diagnosis between dilated cardiomyopathy and ischemic cardiomyopathy based on variational mode decomposition and high order spectra analysis
This study proposes a classification algorithm based on variational mode decomposition (VMD) and high order spectra, which decomposes the preprocessed ECG signal and extracts its first five modes obtained through VMD. After that, these modes are estimated for their corresponding bispectrums, and the feature vector is composed of fifteen features including bispectral, frequency, and nonlinear features based on this. Finally, a dataset containing 75 subjects (38 DCM, 37 ICM) is classified and compared using random forest (RF), decision tree, support vector machine, and K-nearest neighbor. The results show that, in comparison...
Source: Health Information Science and Systems - September 20, 2023 Category: Information Technology Source Type: research

LDS-CNN: a deep learning framework for drug-target interactions prediction based on large-scale drug screening
ConclusionIn this study, we propose a DTI prediction method to solve the problems of unified encoding of large-scale data in multiple formats. It provides a feasible way to efficiently abstract the features among different types of drug-related data, thus reducing experimental costs and time consumption. The proposed method can be used to identify potential drug targets and candidates for the treatment of complex diseases. This work provides a reference for DTI to process large-scale data and different formats with deep learning methods and provides certain suggestions for future research. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - September 2, 2023 Category: Information Technology Source Type: research

Design and technical validation to generate a synthetic 12-lead electrocardiogram dataset to promote artificial intelligence research
ConclusionThe synthetic 12-lead ECG dataset was confirmed to perform similarly to the real-world 12-lead ECG in the classification model. This implies that a synthetic dataset can perform similarly to a real dataset in clinical research using AI. The synthetic dataset generation process in this study provides a way to overcome the medical data disclosure challenges constrained by privacy rights, a way to encourage open data policies, and contribute significantly to promoting cardiovascular disease research. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - August 30, 2023 Category: Information Technology Source Type: research

S-LSTM-ATT: a hybrid deep learning approach with optimized features for emotion recognition in electroencephalogram
ConclusionCombining an FFOA-based feature selection  (FS) and an S-LSTM-ATT-based classification model demonstrated promising results with high accuracy. Other metrics like precision, recall, F1 score and kappa score proved the suitability of the proposed model for ER in EEG signals. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - August 29, 2023 Category: Information Technology Source Type: research

Video-based evaluation system for tic action in Tourette syndrome: modeling, detection, and evaluation
In this study, we proposed a tic action detection method using face video feature recognition for tic and control groups. Through facial ROI extraction, a 3D convolutional neural network was used to learn video feature representations, and multi-instance learning anomaly detection strategy was integrated to construct the tic action analysis and discrimination framework. We applied this tic recognition framework in our video dataset. The model evaluation results achieved average tic detection accuracy of 91.02%, precision of 77.07% and recall of 78.78%. And the tic score curve with postprocessing provided information of how...
Source: Health Information Science and Systems - August 28, 2023 Category: Information Technology Source Type: research

Combining temporal and spatial attention for seizure prediction
Conclusion:The proposed model is comparable to the state of the arts. Experiments on different datasets show that it has good robustness and generalization performance. The high sensitivity and low FPR prove that this model has great potential to realize clinical assistance for diagnosis and treatment. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - August 23, 2023 Category: Information Technology Source Type: research

Gut microbiome biomarkers in adolescent obesity: a regional study
ConclusionsThis study reveals unique features of gut microbiome in terms of microbial composition and metabolic functions in obese adolescents, and provides a baseline for reference and comparison studies. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - August 17, 2023 Category: Information Technology Source Type: research

A new diagnostic autism spectrum disorder (DASD) strategy using ensemble diagnosis methodology based on blood tests
AbstractAutism Spectrum Disorder (ASD) is a complex neurodevelopmental disease that impacts a child ’s way of behavior and social communication. In early childhood, children with ASD typically exhibit symptoms such as difficulty in social interaction, limited interests, and repetitive behavior. Although there are symptoms of ASD disease, most people do not understand these symptoms and therefore do not have enough knowledge to determine whether or not a child has ASD. Thus, early detection of ASD children based on accurate diagnosis model based on Artificial Intelligence (AI) techniques is a critical process to reduce th...
Source: Health Information Science and Systems - August 14, 2023 Category: Information Technology Source Type: research

A new mixed reality tool for training in minimally invasive robotic-assisted surgery
This article presents a mixed-reality (MR) tool for the stereoscopic visualization, annotation and collaborative display of RAS surgical procedures. The tool is an MR application because it can display real stereoscopic content and augment it with virtual elements (annotations) properly registered in 3D and tracked over time. This new tool allows the registration of surgical procedures, teachers (experts) and students (trainees), so that the teacher can share a set of videos with their students, annotate them with virtual information and use a shared virtual pointer with the students. The students can visualize the videos ...
Source: Health Information Science and Systems - August 2, 2023 Category: Information Technology Source Type: research

Effectiveness assessment of repetitive transcranial alternating current stimulation with concurrent EEG and fNIRS measurement
In conclusion, our proposed closed-loop scheme offers a promising advance for evaluating the effectiveness of tACS during the stimulation session. Specifically, the assessment criteria use participant-specific brain-based signals along with a behavioral output. Mo reover, we propose a feedback efficacy score that can aid in determining the optimal stimulation duration based on a participant-specific brain state, thereby preventing the risk of overstimulation. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - August 2, 2023 Category: Information Technology Source Type: research

Autonomous detection of myocarditis based on the fusion of improved quantum genetic algorithm and adaptive differential evolution optimization back propagation neural network
This study examines analogies among five different ECG signal types: normal, abnormal, myocarditis, myocardial infarction (MI), and prior myocardial infarction (PMI). Additionally, the study uses binary and multiclass classification to group myocarditis with other cardiovascular disorders in order to assess how well the algorithm performs in categorization. The experimental results demonstrate that the combination of IQGA and ADE-BPNN can effectively increase the precision and accuracy of myocarditis autonomous diagnosis. In addition, HRV assesses the method ’s robustness, and the classification tool can detect viruses i...
Source: Health Information Science and Systems - August 1, 2023 Category: Information Technology Source Type: research

Early detection of paediatric and adolescent obsessive –compulsive, separation anxiety and attention deficit hyperactivity disorder using machine learning algorithms
ConclusionUsing Streamlit and Python a web application was developed based on the findings of the analysis. The application will assist parents/guardians and school officials in detecting mental illnesses early in their children and adolescents using signs and symptoms to start the treatment at the earliest convenience. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - July 22, 2023 Category: Information Technology Source Type: research