Neuro-critical multimodal Edge-AI monitoring algorithm and IoT system design and development

In recent years, with the continuous development of neurocritical medicine, the success rate of treatment of patients with traumatic brain injury (TBI) has continued to increase, and the prognosis has also improved. TBI patients' condition is usually very complicated, and after treatment, patients often need a more extended time to recover. The degree of recovery is also related to prognosis. However, as a young discipline, neurocritical medicine still has many shortcomings. Especially in most hospitals, the condition of Neuro-intensive Care Unit (NICU) is uneven, the equipment has limited functionality, and there is no unified data specification. Most of the instruments are cumbersome and expensive, and patients often need to pay high medical expenses. Recent years have seen a rapid development of big data and artificial intelligence (AI) technology, which are advancing the medical IoT field. However, further development and a wider range of applications of these technologies are needed to achieve widespread adoption. Based on the above premises, the main contributions of this thesis are the following. First, the design and development of a multi-modal brain monitoring system including 8-channel electroencephalography (EEG) signals, dual-channel NIRS signals, and intracranial pressure (ICP) signals acquisition. Furthermore, an integrated display platform for multi-modal physiological data to display and analysis signals in real-time was designed. This thesis also introduces the use of the Qt signal and slot event processing mechanism and multi-threaded to improve the real-time performance of data processing to a higher level. In addition, multi-modal electrophysiological data storage and processing was realized on cloud server. The system also includes a custom built Django cloud server which realizes real-time transmission between server and WeChat applet. Based on WebSocket protocol, the data transmission delay is less than 10ms. The analysis platform can be equipped with deep learning models to realize the monitoring of patients with epileptic seizures and assess the level of consciousness of Disorders of Consciousness (DOC) patients. This thesis combines the standard open-source data set CHB-MIT, a clinical data set provided by Huashan Hospital, and additional data collected by the system described in this thesis. These data sets are merged to build a deep learning network model and develop related applications for automatic disease diagnosis for smart medical IoT systems. It mainly includes the use of the clinical data to analyze the characteristics of the EEG signal of DOC patients and building a CNN model to evaluate the patient's level of consciousness automatically. Also, epilepsy is a common disease in neuro-intensive care. In this regard, this thesis also analyzes the differences of various deep learning model between the CHB-MIT data set and clinical data set for epilepsy monitoring, in order to select the most appropriate model for the system being designed and developed. Finally, this thesis also verifies the AI-assisted analysis model.. The results show that the accuracy of the CNN network model based on the evaluation of consciousness disorder on the clinical data set reaches 82%. The CNN+STFT network model based on epilepsy monitoring reaches 90% of the accuracy rate in clinical data. Also, the multi-modal brain monitoring system built is fully verified. The EEG signal collected by this system has a high signal-to-noise ratio, strong anti-interference ability, and is very stable. The built brain monitoring system performs well in real-time and stability. Keywords: TBI, Neurocritical care, Multi-modal, Consciousness Assessment, seizures detection, deep learning, CNN, IoT

Diabetes Prediction Using Artificial Neural Network

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks to predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3

Involving machine learning techniques in heart disease diagnosis: a performance analysis

Artificial intelligence is a science that is growing at a tremendous speed every day and has become an essential part of many domains, including the medical domain. Therefore, countless artificial intelligence applications can be seen in the medical domain at various levels, which are employed to enhance early diagnosis and prediction and reduce the risks associated with many diseases, including heart diseases. In this article, machine learning techniques (logistic regression, random forest, artificial neural network, support vector machines, and k-nearest neighbors) are utilized to diagnose heart disease from the Cleveland Clinic dataset got from the University of California Irvine machine learning (UCL) repository and Kaggle platform then create a comparison between the performance of these techniques. In addition, some literature related to machine learning and deep learning techniques that aim to provide reasonable solutions in monitoring, detecting, diagnosing, and predicting heart disease and how these technologies assist in making health decisions are reviewed. Ten studies are selected and summarized by the authors published between 2017 and 2022 are illustrated. After executing a series of tests, it is seen that the most profitable performance in diagnosing heart disease is the support vector machines, with a diagnostic accuracy of 96%. This article has concluded that these techniques play a significant and influential role in assisting physicians and health care workers in analyzing heart patients' data, making health decisions, and saving patients' lives

Integrating Symbolic and Neural Processing in a Self-Organizing Architechture for Pattern Recognition and Prediction

British Petroleum (89A-1204); Defense Advanced Research Projects Agency (N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (F49620-92-J-0225

PREDICTION OF SEPSIS DISEASE BY ARTIFICIAL NEURAL NETWORKS

Sepsis is a fatal condition, which affects at least 26 million people in the world every year that is resulted by an infection. For every 100,000 people, sepsis is seen in 149-240 of them and it has a mortality rate of 30%. The presence of infection in the patient is determined in order to diagnose the sepsis disease. Organ dysfunctions associated with an infection is diagnosed as sepsis. With the increased usage of artificial intelligence in the field of medicine, the early prediction and treatment of many diseases are provided with these methods. Considering the learning, reasoning and decision making abilities of artificial neural networks, which are the sub field of artificial intelligence are inferred to be used in predicting early stages of sepsis disease and determining the sepsis level is assessed. In this study, it is aimed to help sepsis diagnosis by using multi-layered artificial neural network.In construction of artificial neural network model, feed forward back propagation network structure and Levenberg-Marquardt training algorithm were used. The input and output variables of the model were the parameters which doctors use to diagnose the sepsis disease and determine the level of sepsis. The proposed method aims to provide an alternative prediction model for the early detection of sepsis disease

Advancements in Medical Imaging and Diagnostics with Deep Learning Technologies

Medical imaging has long been a cornerstone in diagnostic medicine, providing clinicians with a non-invasive method to visualize internal structures and processes. However, traditional imaging techniques have faced challenges in resolution, safety concerns related to radiation exposure, and the need for invasive procedures for clearer visualization. With the advent of deep learning technologies, significant advancements have been made in the field of medical imaging, addressing many of these challenges and introducing new capabilities. This research seeks into the integration of deep learning in enhancing image resolution, leading to clearer and more detailed visualizations. Furthermore, the ability to reconstruct three-dimensional images from traditional two-dimensional scans offers a more comprehensive view of the area under examination. Automated analysis powered by deep learning algorithms not only speeds up the diagnostic process but also detects anomalies that might be overlooked by the human eye. Predictive analysis, based on these enhanced images, can forecast the likelihood of diseases, and real-time analysis during surgeries ensures immediate feedback, enhancing the precision of medical procedures. Safety in medical imaging has also seen improvements. Techniques powered by deep learning require reduced radiation, minimizing risks to patients. Additionally, the enhanced clarity and detail in images reduce the need for invasive procedures, further ensuring patient safety. The integration of imaging data with Electronic Health Records (EHR) has paved the way for personalized care recommendations, tailoring treatments based on individual patient history and current diagnostics. Lastly, the role of deep learning extends to medical education, where it aids in creating realistic simulations and models, equipping medical professionals with better training tools

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients

Objective The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. Methods and materials The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC©. The variables included in the analysis comprise the neuropsychological initial evaluation of the patient (cognitive affectation profile), the results of the rehabilitation tasks performed by the patient in PREVIRNEC© and the outcome of the patient after a 3–5 months treatment. To achieve the treatment outcome prediction, we apply and compare three different data mining techniques: the AMMLP model, a backpropagation neural network (BPNN) and a C4.5 decision tree. Results The prediction performance of the models was measured by ten-fold cross validation and several architectures were tested. The results obtained by the AMMLP model are clearly superior, with an average predictive performance of 91.56%. BPNN and C4.5 models have a prediction average accuracy of 80.18% and 89.91% respectively. The best single AMMLP model provided a specificity of 92.38%, a sensitivity of 91.76% and a prediction accuracy of 92.07%. Conclusions The proposed prediction model presented in this study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. The ability to predict treatment outcomes may provide new insights toward improving effectiveness and creating personalized therapeutic interventions based on clinical evidence

An integrated decision analytic framework of machine learning with multi-criteria decision making for patient prioritization in elective surgeries

Objectif: De nombreux centres de santé à travers le monde utilisent des critères d'évaluation des préférences cliniques (CPAC) pour donner la priorité aux patients pour accéder aux chirurgies électives. Le processus de priorisation clinique du patient utilise à cette fin les caractéristiques du patient et se compose généralement de critères cliniques, d'expériences de patients précédemment hospitalisés et de commentaires sur les réseaux sociaux. Le but de la hiérarchisation des patients est de déterminer un ordre précis pour les patients et de déterminer combien chaque patient bénéficiera de la chirurgie. En d'autres termes, la hiérarchisation des patients est un type de problème de prise de décision qui détermine l'ordre de ceux qui ont le plus bénéficié de la chirurgie. Cette étude vise à développer une méthodologie hybride en intégrant des algorithmes d'apprentissage automatique et des techniques de prise de décision multicritères (MCDM) afin de développer un nouveau modèle de priorisation des patients. L'hypothèse principale est de valider le fait que l'intégration d'algorithmes d'apprentissage automatique et d'outils MCDM est capable de mieux prioriser les patients en chirurgie élective et pourrait conduire à une plus grande précision. Méthode: Cette étude vise à développer une méthodologie hybride en intégrant des algorithmes d'apprentissage automatique et des techniques de prise de décision multicritères (MCDM) afin de développer un modèle précis de priorisation des patients. Dans un premier temps, une revue de la littérature sera effectuée dans différentes bases de données pour identifier les méthodes récemment développées ainsi que les facteurs de risque / attributs les plus courants dans la hiérarchisation des patients. Ensuite, en utilisant différentes méthodes MCDM telles que la pondération additive simple (SAW), le processus de hiérarchie analytique (AHP) et VIKOR, l'étiquette appropriée pour chaque patient sera déterminée. Dans la troisième étape, plusieurs algorithmes d'apprentissage automatique seront appliqués pour deux raisons: d'abord la sélection des caractéristiques parmi les caractéristiques communes identifiées dans la littérature et ensuite pour prédire les classes de patients initialement déterminés. Enfin, les mesures détaillées des performances de prédiction des algorithmes pour chaque méthode seront déterminées. Résultats: Les résultats montrent que l'approche proposée a atteint une précision de priorisation assez élevée(~70 %). Cette précision a été obtenue sur la base des données de 300 patients et elle pourrait être considérablement améliorée si nous avions accès à plus de données réelles à l'avenir. À notre connaissance, cette étude présente la première et la plus importante du genre à combiner efficacement les méthodes MCDM avec des algorithmes d'apprentissage automatique dans le problème de priorisation des patients en chirurgie élective.Objective: Many healthcare centers worldwide use Clinical Preference Assessment criteria (CPAC) to prioritize patients for accessing elective surgeries [44]. The patient's clinical prioritization process uses patient characteristics for this purpose and usually consists of clinical criteria, experiences of patients who have been previously hospitalized, and comments on social media. The sense of patient prioritization is to determine an accurate ordering for patients and how much each patient will benefit from the surgery. This research intends to build a hybrid approach for creating a new patient prioritizing model by combining machine learning algorithms with multi-criteria decision-making (MCDM) methodologies. The central hypothesis is to validate that the integration of machine learning algorithms and MCDM tools can better prioritize elective surgery patients and lead to higher accuracy. Method: As a first step, a literature review was performed in different databases to identify the recently developed methods and the most common criteria in patient prioritization. Then, using various MCDM methods, including simple additive weighting (SAW), analytical hierarchy process (AHP), and VIKOR, the appropriate label for each patient was determined. As the third step, several machine learning algorithms were applied to predict each patient's classes. Finally, we established the algorithms' precise prediction performance metrics for each approach. Results: The results show that the proposed approach has achieved relatively high prioritization accuracy (~70%). This accuracy has been obtained based on the data from 300 patients, and it could be significantly improved if we have access to more accurate data in the future. To the best of our knowledge, this research is the first of its type to demonstrate the effectiveness of combining MCDM methodologies with machine learning algorithms in patient prioritization problems in elective surgery

Recognition and Evaluation of Heart Arrhythmias via a General Sparse Neural Network

In clinical use, an electrocardiogram (ECG) is an essential medical tool for assessing heart arrhythmias. Thousands of human beings worldwide are affected by different cardiac problems nowadays. As a consequence, studying the features of the ECG pattern is critical for detecting a wide range of cardiac diseases. The ECG is a test which assesses the intensity of the electrical impulses in the circulatory system. In the present investigation, detection and examination of arrhythmias in the heart on the  system using GSNNs (General sparsed neural network classifier) can be carried out[1]. In this paper, the methodologies of support vector regression(SVR), neural mode decomposition(NMD), Artificial Neural Network (ANN), Support Vector Machine(SVM) and are examined. To assess the suggested structure, three distinct ECG waveform situations are chosen from the MIT-BIH arrhythmia collection. The main objective of this assignment is to create a simple, accurate, and simply adaptable approach for classifying the three distinct heart diseases chosen. The wavelet transform Db4 is used in the present paper to obtain several features from an ECG signal. The suggested setup was created using the MATLAB programme. The algorithms suggested are 98% accurate for forecasting cardiac arrhythmias, which is greater than prior techniques