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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Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images

Biomedical Signal Processing and Control, Journal Year: 2021, Volume and Issue: 68, P. 102622 - 102622

Published: April 8, 2021

Language: Английский

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Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

Annals of Operations Research, Journal Year: 2021, Volume and Issue: 339(3), P. 1077 - 1118

Published: March 21, 2021

Language: Английский

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Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Sensors, Journal Year: 2021, Volume and Issue: 21(22), P. 7710 - 7710

Published: Nov. 19, 2021

Epilepsy is a brain disorder disease that affects people's quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides computer-aided diagnosis system (CADS) for the automatic seizures in EEG signals. The proposed method consists three steps, including preprocessing, feature extraction, and classification. In order perform simulations, Bonn Freiburg datasets used. Firstly, we band-pass filter with 0.5-40 Hz cut-off frequency removal artifacts datasets. Tunable-Q Wavelet Transform (TQWT) signal decomposition. second step, various linear nonlinear features extracted from TQWT sub-bands. this statistical, frequency, based on fractal dimensions (FDs) entropy theories. classification different approaches conventional machine learning (ML) deep (DL) discussed. CNN-RNN-based DL number layers applied. have been fed input CNN-RNN model, satisfactory results reported. K-fold cross-validation k = 10 employed demonstrate effectiveness procedure. revealed achieved an accuracy 99.71% 99.13%, respectively.

Language: Английский

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Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression

Cognitive Neurodynamics, Journal Year: 2022, Volume and Issue: 17(6), P. 1501 - 1523

Published: Nov. 12, 2022

Language: Английский

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RLMD‐PA: A Reinforcement Learning‐Based Myocarditis Diagnosis Combined with a Population‐Based Algorithm for Pretraining Weights

Contrast Media & Molecular Imaging, Journal Year: 2022, Volume and Issue: 2022(1)

Published: Jan. 1, 2022

Myocarditis is heart muscle inflammation that becoming more prevalent these days, especially with the prevalence of COVID-19. Noninvasive imaging cardiac magnetic resonance (CMR) can be used to diagnose myocarditis, but interpretation time-consuming and requires expert physicians. Computer-aided diagnostic systems facilitate automatic screening CMR images for triage. This paper presents an model myocarditis classification based on a deep reinforcement learning approach called as learning-based diagnosis combined population-based algorithm (RLMD-PA) we evaluated using Z-Alizadeh Sani dataset prospectively acquired at Omid Hospital, Tehran. addresses imbalanced problem inherent formulates sequential decision-making process. The policy architecture convolutional neural network (CNN). To implement this model, first apply artificial bee colony (ABC) obtain initial values RLMD-PA weights. Next, agent receives sample each step classifies it. For act, gets reward from environment in which minority class greater than majority class. Eventually, finds optimal under guidance particular function helpful environment. Experimental results standard performance metrics show has achieved high accuracy classification, indicating proposed suitable diagnosis.

Language: Английский

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Global, regional, and national burdens of myocarditis, 1990–2019: systematic analysis from GBD 2019

BMC Public Health, Journal Year: 2023, Volume and Issue: 23(1)

Published: April 19, 2023

Myocarditis, a health-threatening heart disease, is attracting increasing attention. This systematic study was conducted to the prevalence of disease through trends incidence, mortality, disability-adjusted life years (DALYs) over last 30 years, which would be helpful for policymakers better choices reasonable decisions.The global, regional, and national burdens myocarditis from 1990-2019 were analyzed by using 2019 Global Burden Disease (GBD) database. on produced new findings according age, sex, Social-Demographic Index (SDI) investigating DALYs, age-standardized incidence rate (ASIR), death (ASDR), corresponding estimated annual percentage change (EAPC).The number increased 62.19%, 780,410 cases in 1990 1,265,770 2019. The ASIR decreased 4.42% (95%CI, -0.26% -0.21%) past years. deaths 65.40% 19,618 324,490 2019, but ASDR relatively stable investigated period. low-middle SDI regions (EAPC=0.48; 95%CI, 0.24 0.72) low (EAPC=-0.97; -1.05 -0.89). DALY 1.19% -1.33% -1.04%) per year.Globally, risk incidences with age. Measures should taken control high-burden regions. Medical supplies improved high-middle middle reduce these

Language: Английский

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Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence—review of evidence and proposition of a roadmap to clinical translation

Journal of Cardiovascular Magnetic Resonance, Journal Year: 2024, Volume and Issue: 26(2), P. 101051 - 101051

Published: Jan. 1, 2024

Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment of heart disease; however, limitations CMR include long exam times and high complexity compared to other cardiac modalities. Recently advancements in artificial intelligence (AI) technology have shown great potential address many limitations. While developments are remarkable, translation AI-based methods into real-world clinical practice remains at a nascent stage much work lies ahead realize full AI CMR.

Language: Английский

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

Published: Nov. 21, 2022

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

Language: Английский

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Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2022, Volume and Issue: 12(6)

Published: Oct. 11, 2022

Abstract Apnea is a sleep disorder that stops or reduces airflow for short time during sleep. Sleep apnea may last few seconds and happen many while sleeping. This reduction in breathing associated with loud snoring, which awaken the person feeling of suffocation. So far, variety methods have been introduced by researchers to diagnose apnea, among polysomnography (PSG) method known be best. Analysis PSG signals very complicated. Many studies conducted on automatic diagnosis from biological using artificial intelligence (AI), including machine learning (ML) deep (DL) methods. research reviews investigates AI First, computer aided system (CADS) ML DL techniques along its parts dataset, preprocessing, are introduced. also summarizes important specifications table. In following, comprehensive discussion made carried out this field. The challenges paramount importance researchers. Accordingly, these obstacles elaborately addressed. another section, most future works detection presented. Ultimately, essential findings study provided conclusion section. article categorized under: Technologies > Artificial Intelligence Application Areas Data Mining Software Tools Algorithmic Development Biological

Language: Английский

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