Epileptic Seizures Detection Using Deep Learning Techniques: A Review

International Journal of Environmental Research and Public Health, Год журнала: 2021, Номер 18(11), С. 5780 - 5780

Опубликована: Май 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.

Язык: Английский

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Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review

Computers in Biology and Medicine, Год журнала: 2021, Номер 139, С. 104949 - 104949

Опубликована: Окт. 29, 2021

Язык: Английский

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Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review

Computers in Biology and Medicine, Год журнала: 2021, Номер 136, С. 104697 - 104697

Опубликована: Июль 31, 2021

Язык: Английский

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Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models

Frontiers in Neuroinformatics, Год журнала: 2021, Номер 15

Опубликована: Ноя. 25, 2021

Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in brain, function some brain regions out balance, leading lack coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based methods for automated SZ diagnosis via electroencephalography (EEG) signals. The obtained results are compared with those conventional methods. To implement proposed methods, dataset Institute Psychiatry Neurology Warsaw, Poland, has been used. First, EEG signals were divided into 25 s time frames then normalized by z -score or norm L2. In classification step, two different approaches considered this was first carried machine e.g., support vector machine, k -nearest neighbors, decision tree, naïve Bayes, random forest, extremely randomized trees, bagging. Various DL models, namely, long short-term memories (LSTMs), one-dimensional convolutional networks (1D-CNNs), 1D-CNN-LSTMs, used following. models implemented activation functions. Among CNN-LSTM architecture had best performance. architecture, ReLU L2-combined normalization model achieved an accuracy percentage 99.25%, better than most former studies field. It worth mentioning that perform all simulations, -fold cross-validation method = 5

Язык: Английский

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Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies

Biomedical Signal Processing and Control, Год журнала: 2021, Номер 73, С. 103417 - 103417

Опубликована: Дек. 7, 2021

Язык: Английский

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Emotion recognition in EEG signals using deep learning methods: A review

Computers in Biology and Medicine, Год журнала: 2023, Номер 165, С. 107450 - 107450

Опубликована: Сен. 9, 2023

Язык: Английский

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Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Frontiers in Molecular Neuroscience, Год журнала: 2022, Номер 15

Опубликована: Окт. 4, 2022

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD also associated with communication deficits repetitive behavior affected individuals. Various detection methods have been developed, including neuroimaging modalities psychological tests. Among these methods, magnetic resonance imaging (MRI) are of paramount importance to physicians. Clinicians rely on MRI diagnose accurately. The non-invasive include functional (fMRI) structural (sMRI) methods. However, diagnosing fMRI sMRI for specialists often laborious time-consuming; therefore, several computer-aided design systems (CADS) based artificial intelligence (AI) developed assist specialist Conventional machine learning (ML) deep (DL) the most popular schemes AI used ASD. This study aims review automated using AI. We CADS ML techniques diagnosis modalities. There has very limited work use DL develop diagnostic models A summary studies provided Supplementary Appendix. Then, challenges encountered during described detail. Additionally, graphical comparison automatically discussed. suggest future approaches detecting ASDs neuroimaging.

Язык: Английский

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Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review

Computers in Biology and Medicine, Год журнала: 2023, Номер 160, С. 106998 - 106998

Опубликована: Май 6, 2023

Язык: Английский

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Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning

Applied Soft Computing, Год журнала: 2023, Номер 144, С. 110511 - 110511

Опубликована: Июнь 15, 2023

Язык: Английский

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Assertiveness-based Agent Communication for a Personalized Medicine on Medical Imaging Diagnosis

Опубликована: Апрель 19, 2023

Intelligent agents are showing increasing promise for clinical decision-making in a variety of healthcare settings. While substantial body work has contributed to the best strategies convey these agents' decisions clinicians, few have considered impact personalizing and customizing communications on clinicians' performance receptiveness. This raises question how intelligent should adapt their tone accordance with target audience. We designed two approaches communicate an agent breast cancer diagnosis different tones: suggestive (non-assertive) imposing (assertive) one. used inform about: (1) number detected findings; (2) severity each per medical imaging modality; (3) visual scale representing estimates; (4) sensitivity specificity agent; (5) arguments patient, such as pathological co-variables. Our results demonstrate that assertiveness plays important role this communication is perceived its benefits. show according professional experience clinician can reduce errors increase satisfaction, bringing novel perspective design adaptive between clinicians.

Язык: Английский

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