Deep learning for motor imagery EEG-based classification: A review

Biomedical Signal Processing and Control, Journal Year: 2020, Volume and Issue: 63, P. 102172 - 102172

Published: Oct. 8, 2020

Language: Английский

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Epileptic Seizures Detection Using Deep Learning Techniques: A Review

International Journal of Environmental Research and Public Health, Journal Year: 2021, Volume and Issue: 18(11), P. 5780 - 5780

Published: May 27, 2021

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a areas, one its branches is deep learning (DL). Before the rise DL, conventional machine algorithms involving feature extraction were performed. This limited their performance ability those handcrafting features. However, in features classification are entirely automated. The advent these techniques many areas medicine, such as diagnosis has made significant advances. In this study, comprehensive overview works focused on automated seizure detection DL neuroimaging modalities presented. Various methods seizures automatically EEG MRI described. addition, rehabilitation systems developed for analyzed, summary provided. tools include cloud computing hardware required implementation algorithms. important challenges accurate with discussed. advantages limitations employing DL-based Finally, most promising models possible future delineated.

Language: Английский

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A survey on deep learning in medicine: Why, how and when?

Information Fusion, Journal Year: 2020, Volume and Issue: 66, P. 111 - 137

Published: Sept. 15, 2020

Language: Английский

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The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century

Bioengineering, Journal Year: 2024, Volume and Issue: 11(4), P. 337 - 337

Published: March 29, 2024

As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging a key force transformation. This review motivated by urgent need to harness AI’s potential mitigate these issues aims critically assess integration in different domains. We explore how AI empowers clinical decision-making, optimizes hospital operation management, refines medical image analysis, revolutionizes patient care monitoring through AI-powered wearables. Through several case studies, we has transformed specific domains discuss remaining possible solutions. Additionally, will methodologies assessing solutions, ethical of deployment, importance data privacy bias mitigation responsible technology use. By presenting critical assessment transformative potential, this equips researchers with deeper understanding current future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, technologists navigate complexities implementation, fostering development AI-driven solutions that prioritize standards, equity, patient-centered approach.

Language: Английский

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Deep learning and the electrocardiogram: review of the current state-of-the-art

EP Europace, Journal Year: 2020, Volume and Issue: 23(8), P. 1179 - 1191

Published: Nov. 26, 2020

Abstract In the recent decade, deep learning, a subset of artificial intelligence and machine has been used to identify patterns in big healthcare datasets for disease phenotyping, event predictions, complex decision making. Public electrocardiograms (ECGs) have existed since 1980s very specific tasks cardiology, such as arrhythmia, ischemia, cardiomyopathy detection. Recently, private institutions begun curating large ECG databases that are orders magnitude larger than public ingestion by learning models. These efforts demonstrated not only improved performance generalizability these aforementioned but also application novel clinical scenarios. This review focuses on orienting clinician towards fundamental tenets state-of-the-art prior its use analysis, current applications ECGs, well their limitations future areas improvement.

Language: Английский

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Brain-Computer Interface: Advancement and Challenges

Sensors, Journal Year: 2021, Volume and Issue: 21(17), P. 5746 - 5746

Published: Aug. 26, 2021

Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking has been conducted in this domain. Still, no comprehensive review that covers BCI completely yet. Hence, a overview of presented study. This study applications upholds significance Then, each element systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing algorithms, classifiers, are explained concisely. In addition, brief technologies or mostly sensors used BCI, appended. Finally, paper investigates unsolved challenges explains them with possible solutions.

Language: Английский

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Application of data fusion for automated detection of children with developmental and mental disorders: A systematic review of the last decade

Information Fusion, Journal Year: 2023, Volume and Issue: 99, P. 101898 - 101898

Published: June 25, 2023

Mental health is a basic need for sustainable and developing society. The prevalence financial burden of mental illness have increased globally, especially in response to community worldwide pandemic events. Children suffering from such disorders find it difficult cope with educational, occupational, personal, societal developments, treatments are not accessible all. Advancements technology resulted much research examining the use artificial intelligence detect or identify characteristics illness. Therefore, this paper presents systematic review nine developmental (Autism spectrum disorder, Attention deficit hyperactivity Schizophrenia, Anxiety, Depression, Dyslexia, Post-traumatic stress Tourette syndrome, Obsessive-compulsive disorder) prominent children adolescents. Our focuses on automated detection these using physiological signals. This also detailed discussion signal analysis, feature engineering, decision-making their advantages, future directions challenges papers published children. We presented details dataset description, validation techniques, features extracted models. present open questions availability, uncertainty, explainability, hardware implementation resources analysis machine deep learning Finally, main findings study conclusion section.

Language: Английский

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Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-ray Images

Diagnostics, Journal Year: 2022, Volume and Issue: 12(4), P. 915 - 915

Published: April 6, 2022

Chest X-ray radiographic (CXR) imagery enables earlier and easier lung disease diagnosis. Therefore, in this paper, we propose a deep learning method using transfer technique to classify diseases on CXR images improve the efficiency accuracy of computer-aided diagnostic systems' (CADs') performance. Our proposed is one-step, end-to-end learning, which means that raw are directly inputted into model (EfficientNet v2-M) extract their meaningful features identifying categories. We experimented our three classes normal, pneumonia, pneumothorax U.S. National Institutes Health (NIH) data set, achieved validation performances loss = 0.6933, 82.15%, sensitivity 81.40%, specificity 91.65%. also Cheonan Soonchunhyang University Hospital (SCH) set four pneumothorax, tuberculosis, 0.7658, 82.20%, 94.48%; testing tuberculosis was 63.60%, 82.30%, 82.80%, 89.90%, respectively.

Language: Английский

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Clinical Validation of AI Disease Detection Models — An Overview of the Clinical Validation Process for AI Disease Detection Models, and How They Can Be Validated for Accuracy and Effectiveness

Published: Jan. 3, 2025

Artificial intelligence (AI) is being utilized to analyze and distinguish diseases within the rapidly evolving healthcare sector. With potential significantly improve patient outcomes in real-world clinical settings, this unique approach offers fresh perspectives innovative modeling techniques for disease diagnosis. Utilizing cutting-edge approaches strategies boost demonstrative productivity precision, we present groundbreaking improvements AI detection models as they are currently used. Our have gone through a thorough approval strategy, when assessed them across various groups circumstances, found that novel primary calculations performed especially well. examination highlights few key advancements greatly affected field of AI-driven health technologies. These include enhanced increasing synthesizing training data, well adaptive learning algorithms can adjust shifting therapeutic trends. Also, successfully applied ensemble combine qualities models. essential objective ponder make straightforward reasonable utilize settings. By providing real-time decision support, clinicians educated choices based on latest available eventually improving expanding productivity. Looking ahead, think future will depend collaboration interdisciplinary endeavors. This incorporates coordinating multimodal creating new algorithms, building up common conventions, giving preparing openings over distinctive areas. In general, our investigate demonstrates noteworthy revolutionize delivery improved outcomes. continued focus development, collaboration, learning, able pave way toward healthier more prosperous future.

Language: Английский

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Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals

Computers in Biology and Medicine, Journal Year: 2019, Volume and Issue: 113, P. 103387 - 103387

Published: Aug. 9, 2019

Language: Английский

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