Classification and discrimination of focal and non-focal EEG signals based on deep neural network

Taqi, Arwa Mohammed; Al-Azzo, Fadwa; Mariofanna, Milanova; Al-Saadi, Jassim M.

doi:10.1109/crcsit.2017.7965539

Cited by 22 publications

(8 citation statements)

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“…AlexNet won the competition by achieving the top-5 test accuracy of 84.6%. Taqi et al [ 57 ] used the AlexNet network to diagnose focal epileptic seizures. This proposed network used the feature extraction approach and eventually applied the Softmax layer for classification purposes and achieved 100% accuracy.…”

Section: Epileptic Seizures Detection Based On DL Techniquesmentioning

confidence: 99%

“…In the field of EEG signal processing to diagnose epileptic seizures, this architecture has recently received the attention of researchers. Taqi et al [ 57 ] used this network in their preliminary studies to diagnose epileptic seizures. Their model was used to extract features from the Bern-Barcelona dataset and achieved excellent results.…”

Section: Epileptic Seizures Detection Based On DL Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Shoeibi

Khodatars

Ghassemi

et al. 2021

IJERPH

256

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: Epileptic Seizures Detection Based On DL Techniquesmentioning

confidence: 99%

Section: Epileptic Seizures Detection Based On DL Techniquesmentioning

confidence: 99%

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Shoeibi

Khodatars

Ghassemi

et al. 2021

IJERPH

256

109

View full text Add to dashboard Cite

show abstract

“…Year Features Performance (%) Johansen et al [33] Antoniades et al [34] Lin et al [35] Achilles et al [36] Thodoroff et al [37] Page et al [38] Vidyaratne et al [39] Yan et al [40] Lin et al [41] Hosseini et al [42] Wei et al [43] Golmohammadi et al [44] Taqi et al [45] O'Shea et al [46] Talathi et al [47] Yuan et al [48] Le et al [49] Hosseini et al [50] Gogna et al [51] Achilles et al [52] Accuracy= 100.0 Daoud et al [57] Zhang et al [58] Ullah et al [59] Acharya et al [60] Yıldırım et al [61] Chen et al [62] Yuvaraj et al [63] Hussein et al [64] Aristizabal et al [65] Hussein et al [66] Rajaguru et al Gleichgerrcht [82] Ullah et al [83] Acharya et al [84] Tjepkema et al [85] Maria Hugle et al [86] Thomas et al [87] Hussein et al [88] Emami et al [89] Jang and Cho [90] Nejedly et al [91] Iesmantas et al [92] Avcu et al [93] Hossain et al [94] Zuo et al…”

Section: Authormentioning

confidence: 99%

Applications of Artificial Intelligence in Automatic Detection of Epileptic Seizures Using EEG Signals: A Review

Saminu,

Xu,

Zhang

et al. 2022

AIA

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Correctly interpreting an Electroencephalography (EEG) signal with high accuracy is a tedious and time-consuming task that may take several years of manual training due to its complexity, noisy, non-stationarity, and nonlinear nature. To deal with the vast amount of data and recent challenges of meeting the requirements to develop low cost, high speed, low complexity smart internet of medical things (IoMT) computer-aided devices (CAD), artificial intelligence (AI) techniques which consist of machine learning and deep learning plays a vital role in achieving the stated goals. Over the years, machine learning techniques have been developed to detect and classify epileptic seizures. But until recently, deep learning techniques have been applied in various applications such as image processing and computer visions. However, several research studies have turned their attention to exploring the efficacy of deep learning to overcome some challenges associated with conventional automatic seizure detection techniques. This paper endeavors to review and investigate the fundamentals, applications, and progress of AI-based techniques applied in CAD system for epileptic seizure detection and characterisation. It would help in actualising and realising smart wireless wearable medical devices so that patients can monitor seizures before their occurrence and help doctors diagnose and treat them. The work reveals that the recent application of deep learning algorithms improves the realisation and implementation of mobile health in a clinical environment.

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“…In addition to detecting epilepsy waveforms to assist clinical needs, deep learning algorithms can also classify patients with focal epilepsy to achieve the purpose of serving clinical surgical decisions. Taji et al [72] applied three different CNN models, the classification of the EEG signals of patients with focal and non-focal epilepsy can not only use less training data to achieve the best classification performance, but also increase the calculation speed to reduce the time required for the classification process. Good classification performance provides help for the diagnosis of focal epilepsy disease.…”

Section: Disease Detectionmentioning

confidence: 99%

Deep Learning Algorithms in EEG Signal Decoding Application: A Review

Vallabhaneni¹,

Sharma²,

Kumar

et al. 2021

IEEE Access

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In recent years, deep learning algorithms have been developed rapidly, and they are becoming a powerful tool in biomedical engineering. Especially, there has been an increasing focus on the use of deep learning algorithms for decoding physiological or pathological status of the brain from electroencephalographic (EEG). This paper overviews current application of deep learning algorithms in various EEG decoding tasks, and introduces commonly used algorithms, typical application scenarios, important progresses and existing problems. Firstly, the basic principles of deep learning algorithms used in EEG decoding is briefly described, including convolutional neural network, deep belief network, autoencoder and recurrent neural network. In this paper, existing applications of deep learning on EEG is discussed, including brain-computer interfaces, cognitive neuroscience and diagnosis of brain disorders. Finally, this paper outlines some key problems that will be addressed in future applications of deep learning for EEG decoding, such as parameter selection, computational complexity, and the capability of generalization.

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Classification and discrimination of focal and non-focal EEG signals based on deep neural network

Cited by 22 publications

References 0 publications

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Applications of Artificial Intelligence in Automatic Detection of Epileptic Seizures Using EEG Signals: A Review

Deep Learning Algorithms in EEG Signal Decoding Application: A Review

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