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IEEE Joint 22nd International Symposium on Computational Intelligence and Informatics / 8th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics (CINTI-MACRo)

作者： Danaei, Saba Bostani, Arsam Moravvej, Seyed Vahid Mohammadi, Fardin Alizadehsani, Roohallah Shoeibi, Afshin Alinejad-Rokny, Hamid Nahavandi, Saeid Adiban Inst Higher Educ Semnan Iran Urmia Univ Dept Mech Engn Biosyst Nazlu Iran Univ Tehran Dept Exercise Physiol & Hlth Sci Tehran Iran Internship UNSW BioMed Machine Learning Lab Sydney NSW 2052 Australia Deakin Univ IISRI Waurn Ponds Vic 3216 Australia Univ New South Wales UNSW Sydney UNSW Data Sci Hub Sydney NSW 2052 Australia UNSW Sydney Grad Sch Biomed Engn BioMed Machine Learning Lab Sydney NSW 2052 Australia

ISBN: (纸本)9798350398823

Myocarditis occurs when the heart muscle becomes inflamed and inflammation occurs when your body's immune system responds to infections. It can be diagnosed using cardiac magnetic resonance image (MRI), a non-invasive imaging technique with the possibility of operator bias. This paper proposes a hybrid method of deep reinforcement learning-based algorithms and meta-heuristics algorithms. A mutual learning-based artificial bee colony (ML-ABC) is employed for initial weight, which adjusts the candidate food source generated with the higher fitness between two individuals determined by a mutual learning factor. Moreover, a sequential decision-making process investigates the imbalanced classification issue, in which a convolutional neural network (CNN) is used as the foundation for policy architecture. At first, initial weights are produced using the ML-ABC algorithm. After that, the agent receives a sample at each phase and classifies it, obtaining environmental rewards. The minority class receives more rewards than the majority class. Eventually, the agent discovers an ideal strategy with the aid of a specific reward function and a beneficial learning environment. We evaluate our proposed approach on the Z-Alizadeh Sani myocarditis dataset based on standard criteria and demonstrate that the proposed method gives superior myocarditis diagnosis performance.

关键词： Myocarditis diagnosis MRI Deep reinforcement learning Classification ABC algorithm

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Early Diagnosis of Schizophrenia in EEG Signals Using One Dimensional Transformer Model 10th

Early Diagnosis of Schizophrenia in EEG Signals Using One Di...

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10th International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC)

作者： Shoeibi, Afshin Jafari, Mahboobeh Sadeghi, Delaram Alizadehsani, Roohallah Alinejad-Rokny, Hamid Beheshti, Amin Gorriz, Juan M. Univ Granada Data Sci & Computat Intelligence Inst Granada Spain Macquarie Univ AI Enabled Proc AIP Res Ctr Internship Hlth Data Analyt Program Sydney NSW Australia Deakin Univ Inst Intelligent Syst Res & Innovat IISRI Geelong Vic Australia UNSW Sydney UNSW BioMed Machine Learning Lab Grad Sch Biomed Engn Sydney NSW 2052 Australia Macquarie Univ Sch Comp Data Sci Lab Sydney NSW 2109 Australia

ISBN: (纸本)9783031611391;9783031611407

Schizophrenia (SZ) is a complex mental disorder, hallmarked by symptoms including delusions, hallucinations, disorganized speech, cognitive impairments, and diminished motivation. Electroencephalography (EEG) recordings have become a critical tool for clinicians and psychologists in diagnosing SZ. Nonetheless, interpreting EEG data to diagnose SZ presents significant challenges for specialists, leading to increased interest in leveraging artificial intelligence (AI) for early detection. This study introduces a novel approach for SZ detection from EEG signals utilizing a transformer-based architecture. The methodology encompasses dataset selection, preprocessing, feature extraction, and classification phases. The RepOD dataset was employed for all simulations. Preprocessing entails filtering, normalization, and segmenting into time windows. Following this, a one-dimensional (1D) transformer architecture, incorporating various activation functions, is applied to extract features from the preprocessed EEG signals. In the architecture's final layer, the Softmax activation function is utilized for classifying the data. The performance of the proposed model is assessed using a K-Fold cross-validation strategy, with K set to 10. The proposed method achieved a maximum accuracy of 97.62% in diagnosing schizophrenia (SZ), underscoring its potential efficacy in SZ diagnosis.

关键词： Schizophrenia Diagnosis EEG Signals Deep learning Transformer

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Myocarditis Diagnosis: A Method using Mutual learning-Based ABC and Reinforcement learning

Myocarditis Diagnosis: A Method using Mutual Learning-Based ...

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International Symposium on Computational Intelligence and Informatics

作者： Saba Danaei Arsam Bostani Seyed Vahid Moravvej Fardin Mohammadi Roohallah Alizadehsani Afshin Shoeibi Hamid Alinejad-Rokny Saeid Nahavandi Adiban Institute of Higher Education Semnan Iran Department of mechanical engineering of biosystems Urmia university Department of exercise physiology & health science University of tehran Internship in UNSW BioMedical Machine Learning Lab Sydney NSW Australia Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Waurn Ponds Victoria Australia UNSW Data Science Hub The University of New South Wales (UNSW Sydney) Sydney New South Wales Australia BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney Sydney NSW Australia

ISBN: (纸本)9798350398830

Myocarditis occurs when the heart muscle becomes inflamed and inflammation occurs when your body’s immune system responds to infections. It can be diagnosed using cardiac magnetic resonance image (MRI), a non-invasive imaging technique with the possibility of operator bias. This paper proposes a hybrid method of deep reinforcement learning-based algorithms and meta-heuristics algorithms. A mutual learning-based artificial bee colony (ML-ABC) is employed for initial weight, which adjusts the candidate food source generated with the higher fitness between two individuals determined by a mutual learning factor. Moreover, a sequential decision-making process investigates the imbalanced classification issue, in which a convolutional neural network (CNN) is used as the foundation for policy architecture. At first, initial weights are produced using the ML-ABC algorithm. After that, the agent receives a sample at each phase and classifies it, obtaining environmental rewards. The minority class receives more rewards than the majority class. Eventually, the agent discovers an ideal strategy with the aid of a specific reward function and a beneficial learning environment. We evaluate our proposed approach on the Z-Alizadeh Sani myocarditis dataset based on standard criteria and demonstrate that the proposed method gives superior myocarditis diagnosis performance.

关键词： Mechatronics Computational modeling Metaheuristics Reinforcement learning Myocardium Markov processes Classification algorithms

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Diagnosis of Schizophrenia in EEG Signals Using dDTF Effective Connectivity and New PreTrained CNN and Transformer Models 10th

Diagnosis of Schizophrenia in EEG Signals Using dDTF Effecti...

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10th International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC)

作者： Shoeibi, Afshin Khodatars, Marjane Alinejad-Rorky, Hamid Heras, Jonathan Bagherzadeh, Sara Beheshti, Amin Gorriz, Juan M. Univ Granada Data Sci & Computat Intelligence Inst Granada Spain Macquarie Univ AI Enabled Proc AIP Res Ctr Internship Hlth Data Analyt Program Sydney NSW Australia UNSW Sydney UNSW BioMed Machine Learning Lab Grad Sch Biomed Engn Sydney NSW 2052 Australia Univ La Rioja Dept Math & Comp Sci La Rioja Spain Islamic Azad Univ Dept Biomed Engn Sci & Res Branch Tehran Iran Macquarie Univ Sch Comp Data Sci Lab Sydney NSW 2109 Australia

ISBN: (纸本)9783031611391;9783031611407

Schizophrenia (SZ) is a multifaceted mental disorder that typically emerges in early adulthood, characterized by a spectrum of physiological and cognitive deficits. Electroencephalography (EEG) recordings are pivotal in SZ diagnosis, necessitating the expertise of specialist doctors and psychologists. However, the analysis of EEG signals is labor-intensive and susceptible to human error. This study introduces a deep learning (DL) pipeline for the early detection of SZ using EEG signals. The pipeline includes stages of dataset selection, preprocessing, feature extraction, and classification. For this study, the RepOD dataset, consisting of EEG recordings from 14 subjects with SZ and healthy controls (HC), was utilized. The preprocessing phase involves normalizing and segmenting the EEG data. Subsequently, the EEG signals are divided into various sub-bands via Discrete Wavelet Transform (DWT), and effective connectivity matrices are derived using the directed Directed Transfer Function (dDTF) technique. Following this, state-of-the-art pretrained DL models based on CNNs and transformers are applied to extract features and classify the 2D dDTF images obtained from different EEG sub-bands. Notably, the ConvNext-Tiny architecture demonstrated superior performance, achieving an accuracy of 96% in the beta sub-band. Furthermore, this model surpassed the performance of other DL models in terms of accuracy across additional EEG sub-bands.

关键词： Schizophrenia EEG Detection DWT dDTF Transformers

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Improving PPG Signal Classification with machine learning: The Power of a Second Opinion 24

Improving PPG Signal Classification with Machine Learning: T...

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24th International Conference on Digital Signal Processing, DSP 2023

作者： Asgharnezhad, Hamzeh Shamsi, Afshar Bakhshayeshi, Ivan Alizadehsani, Roohallah Chamaani, Somayyeh Alinejad-Rokny, Hamid University of New South Wales Internship in BML Sydney Australia Concordia University Concordia Ins. Inf. Sys. Eng Montreal Canada University of New South Wales BioMedical Machine Learning Lab Sydney Australia Geelong Australia K.N. Toosi University of Technology Faculty of Electrical Engineering Tehran Iran Sydney Australia

ISBN: (纸本)9798350339598

Photoplethysmography (PPG) is a non-invasive technique that uses light to measure blood volume changes in tissues. It is widely used in clinical settings for monitoring vital signs such as heart rate, blood oxygen saturation, and blood pressure. To extract relevant information from PPG signals, such as peak detection and signal quality assessment, careful processing is required. Although traditional machine learning-based methods are capable of extracting useful information from PPG signals, they do not provide a measure of their confidence. In contrast, probabilistic machine learning approaches, such as Bayesian networks, can quantify uncertainty and provide estimates of prediction confidence. This would be beneficial for clinical decision-making and lead to more transparent and interpretable results. This paper proposes a new confidence-aware framework for Cuff-Less prediction of blood pressure using Monte Carlo Dropout (MCD) and Bayesian optimization techniques. Our results demonstrate that our approach outperforms simple MCD, providing more reliable predictions. © 2023 IEEE.

关键词： Blood pressure

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Importance of Wearable Health Monitoring Systems Using IoMT;Requirements, Advantages, Disadvantages and Challenges 22

Importance of Wearable Health Monitoring Systems Using IoMT;...

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作者： Khozeimeh, Fahime Roshanzamir, Mohamad Shoeibi, Afshin Darbandy, Mohammad Tayarani Alizadehsani, Roohallah Alinejad-Rokny, Hamid Ahmadian, Davood Khosravi, Abbas Nahavandi, Saeid Deakin Univ IISRI Geelong Vic 3217 Australia Fasa Univ Dept Comp Engn Fasa *** Iran Macquarie Univ AI Enabled Proc AIP Res Ctr Internship Hlth Data Analyt Program Sydney NSW 2109 Australia Islamic Azad Univ Taft Sch Architecture Taft *** Iran UNSW Sydney Grad Sch Biomed Engn BioMed Machine Learning Lab Sydney NSW 2052 Australia Univ Tabriz Fac Math Stat & Comp Sci Tabriz Iran

ISBN: (纸本)9798350398823

Providing better quality of service is one of the most important goals in medical/healthcare systems. The services provided should have features such as low latency, appropriate geographic distribution, and real-time interaction. Internet of Medical Things (IoMT) technology is one of the solutions to achieve high quality of services with these features. The wearable health monitoring systems which have become popular in the last decade are one of the foundations of achievement to these features. In this paper, the importance of wearable devices under IoMT technology is investigated. There are several reasons for using wearable devices under IoMT technology, such as reducing healthcare costs and long waiting times. Therefore, anyone working on wearable devices needs to have a general understanding of how they are designed and function. In fact, the integration of new devices to monitor and alert patients about their physical health is a promising option for improving health care systems around the world. Overcrowding in medical centers will be prevented by upgrading the facilities for the virtual presence of patients. It can also ensure real-time monitoring and reduce operating costs. In addition, it can accelerate treatment of patients and reduce the cost of investment and increase its coverage.

关键词： Internet of Medical Things Wearable Devices Healthcare Systems Internet of Things

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Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023

arXiv

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arXiv 2023年

作者： Jafari, Mahboobeh Sadeghi, Delaram Shoeibi, Afshin Alinejad-Rokny, Hamid Beheshti, Amin García, David López Chen, Zhaolin Acharya, U. Rajendra Gorriz, Juan M. Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Health Data Analytics Program Centre for Applied Artificial Intelligence Macquarie University Sydney2109 Australia Data Science Lab School of Computing Macquarie University SydneyNSW2109 Australia Monash University Melbourne Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Department of Psychiatry University of Cambridge United Kingdom

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings. © 2023,

关键词： Electroencephalography

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Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence

arXiv

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arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Ghassemi, Navid Heras, Jonathan Ling, Sai Ho Beheshti, Amin Zhang, Yu-Dong Wang, Shui-Hua Alizadehsani, Roohallah Gorriz, Juan M. Acharya, U. Rajendra Rokny, Hamid Alinejad Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Mathematics and Computer Science University of La Rioja La Rioja Spain Australia Data Analytics Lab Department of Computing Macquarie University SydneyNSW2109 Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Deakin University VIC3217 Australia Department of Psychiatry University of Cambridge United Kingdom School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia

Myocarditis is a significant cardiovascular disease (CVD) that poses a threat to the health of many individuals by causing damage to the myocardium. The occurrence of microbes and viruses, including the likes of HIV, plays a crucial role in the development of myocarditis disease (MCD). The images produced during cardiac magnetic resonance imaging (CMRI) scans are low contrast, which can make it challenging to diagnose cardiovascular diseases. In other hand, checking numerous CMRI slices for each CVD patient can be a challenging task for medical doctors. To overcome the existing challenges, researchers have suggested the use of artificial intelligence (AI)-based computer-aided diagnosis systems (CADS). The presented paper outlines a CADS for the detection of MCD from CMR images, utilizing deep learning (DL) methods. The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing. First, the Z-Alizadeh dataset was selected for the experiments. Subsequently, the CMR images underwent various preprocessing steps, including denoising, resizing, as well as data augmentation (DA) via CutMix and MixUp techniques. In the following, the most current deep pre-trained and transformer models are used for feature extraction and classification on the CMR images. The findings of our study reveal that transformer models exhibit superior performance in detecting MCD as opposed to pre-trained architectures. In terms of DL architectures, the Turbulence Neural Transformer (TNT) model exhibited impressive accuracy, reaching 99.73% utilizing a 10-fold cross-validation approach. Additionally, to pinpoint areas of suspicion for MCD in CMRI images, the Explainable-based Grad Cam method was employed. © 2022, CC BY-NC-ND.

关键词： Diseases

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3DECG-Net: ECG fusion network for multi-label cardiac arrhythmia detection

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Computers in Biology and Medicine 2024年 182卷 109126-109126页

作者： Sadeghi, Alireza Hajati, Farshid Rezaee, Alireza Sadeghi, Mahdieh Argha, Ahmadreza Alinejad-Rokny, Hamid Department of Mechatronics School of Intelligent Systems College of Interdisciplinary Science and Technology University of Tehran Tehran Iran School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2350 Australia Student Research Committee Mazandaran University of Medical Sciences Sari Iran School of Biomedical Engineering UNSW Sydney RandwickNSW2052 Australia BioMedical Machine Learning Lab School of Biomedical Engineering UNSW Sydney RandwickNSW2052 Australia

Cardiovascular diseases represent the leading global cause of death, typically diagnosed and addressed through electrocardiograms (ECG), which record the heart's electrical activity. In recent years, there has been a notable surge in ECG recordings, driven by the widespread use of wearable devices. However, the limited availability of medical experts to analyze these recordings underscores the necessity for automated ECG analysis using computer-aided methods. In this study, we introduced 3DECG-Net, a deep learning model designed to detect and classify seven distinct heart states through the analysis of data fusion from 12-lead ECG in a multi-label framework. Our model leverages a residual architecture with a multi-head attention mechanism, undergoing training within a five-fold cross-validation scheme. By transforming 12-lead ECG signals into 3D data with the help of Recurrent Plot technique, 3DECG-Net achieves a noteworthy micro F1-score of 80.3 %, surpassing the performance of other state-of-the-art deep learning models developed for this specific task. Also, we present an ECG preprocessing framework to generate compact, high-quality ECG signals for potential application in future studies within this domain. We conduct an explainable AI experiment using Local Interpretable Model-agnostic Explanations (LIME) to elucidate the significance of each lead in accurately diagnosing specific arrhythmias, ensuring the logical processing of ECG data by 3DECG-Net. The findings of this study suggest that the proposed model is trustworthy and has the potential to be used as an effective diagnostic toolset for identifying heart arrhythmias. Its effectiveness can improve the diagnostic process, facilitate early treatment, and enhance overall efficiency in medical settings. © 2024

关键词： Data fusion

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Improving PPG Signal Classification with machine learning: The Power of a Second Opinion

Improving PPG Signal Classification with Machine Learning: T...

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International Conference on Digital Signal Processing (DSP)

作者： Hamzeh Asgharnezhad Afshar Shamsi Ivan Bakhshayeshi Roohallah Alizadehsani Somayyeh Chamaani Hamid Alinejad-Rokny Internship in BML University of New South Wales Sydney Australia Concordia Ins. Inf. Sys. Eng Concordia University Montreal Canada BioMedical Machine Learning Lab University of New South Wales Sydney Australia Int. Sys. Research and Innovation (IISRI) Deakin University Geelong Australia Faculty of Electrical Engineering K.N. Toosi University of Technology Tehran Iran BioMedical Machine Learning Lab (BML) University of New South Wales Sydney Australia

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