Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991–2020

Alizadehsani, Roohallah; Khosravi, Abbas; Roshanzamir, Mohamad; Abdar, Moloud; Sarrafzadegan, Nizal; Shafie, Davood; Khozeimeh, Fahime; Shoeibi, Afshin; Nahavandi, Saeid; Panahiazar, Maryam; Bishara, Andrew; Beygui, Ramin E.; Puri, Rishi; Kapadia, Samir; Tan, Ru San; Acharya, U. Rajendra

doi:10.1016/j.compbiomed.2020.104095

Cited by 81 publications

(37 citation statements)

References 110 publications

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“…The objective of these studies is to assist physicians in accurate epileptic seizures diagnosis. AI research involves conventional machine learning [ 151 ] and DL [ 152 , 153 , 154 , 155 , 156 ] scopes. Until recently, many machine learning methods that were adopted to automatically detect seizures could not be seriously used for a variety of real-time diagnostic aid tools for epileptic seizures due to their disadvantages.…”

Section: Discussionmentioning

confidence: 99%

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Shoeibi

Khodatars

Ghassemi

et al. 2021

IJERPH

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283

109

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

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Section: Discussionmentioning

confidence: 99%

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Shoeibi

Khodatars

Ghassemi

et al. 2021

IJERPH

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283

109

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“…For future work, the proposed method can be utilized to encrypt EEG signals of epileptic seizures [ 67 , 68 ], attention deficit hyperactivity disorder (ADHD) [ 69 , 70 ], schizophrenia [ 71 , 72 ], and multiple sclerosis [ 73 ]. Additionally, the technique presented in this study can be adopted to encrypt MRI [ 74 , 75 ], X-ray [ 76 ], mammography [ 77 , 78 ], and other medical data [ 79 , 80 , 81 , 82 , 83 , 84 ].…”

Section: Discussionmentioning

confidence: 99%

Medical Images Encryption Based on Adaptive-Robust Multi-Mode Synchronization of Chen Hyper-Chaotic Systems

Javan

Jafari

Shoeibi

et al. 2021

Sensors

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In this paper, a novel medical image encryption method based on multi-mode synchronization of hyper-chaotic systems is presented. The synchronization of hyper-chaotic systems is of great significance in secure communication tasks such as encryption of images. Multi-mode synchronization is a novel and highly complex issue, especially if there is uncertainty and disturbance. In this work, an adaptive-robust controller is designed for multimode synchronized chaotic systems with variable and unknown parameters, despite the bounded disturbance and uncertainty with a known function in two modes. In the first case, it is a main system with some response systems, and in the second case, it is a circular synchronization. Using theorems it is proved that the two synchronization methods are equivalent. Our results show that, we are able to obtain the convergence of synchronization error and parameter estimation error to zero using Lyapunov’s method. The new laws to update time-varying parameters, estimating disturbance and uncertainty bounds are proposed such that stability of system is guaranteed. To assess the performance of the proposed synchronization method, various statistical analyzes were carried out on the encrypted medical images and standard benchmark images. The results show effective performance of the proposed synchronization technique in the medical images encryption for telemedicine application.

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“…Although perception tasks such as object detection need human intelligence, a subsequent task that needs inference and reasoning needs more human intelligence. Nowadays, new algorithms like deep learning could extremely improve perception tasks (Alizadehsani et al, 2021;Asgharnezhad et al, 2020;Ghassemi et al, 2019Ghassemi et al, , 2020Khodatars et al, 2020;Mohammadpoor et al, 2016;Sharifrazi et al, 2020;Shoeibi et al, 2020a, b;Shoeibi, 2021;Shoushtarian et al, 2020). However, for higher-level inference, probabilistic graphical models are more powerful than other algorithms.…”

Section: Related Work Based On Bayesian Deep Learningmentioning

confidence: 99%

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

et al. 2021

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Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making.

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Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991–2020

Cited by 81 publications

References 110 publications

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Medical Images Encryption Based on Adaptive-Robust Multi-Mode Synchronization of Chen Hyper-Chaotic Systems

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

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