Epileptic seizure detection using convolutional neural networks and recurrence plots of EEG signals

Ravi, Sriya; Shreenidhi, S; Shahina, A.; Ilakiyaselvan, N.; Khan, Abbas

doi:10.1007/s11042-021-11608-2

Cited by 14 publications

(4 citation statements)

References 22 publications

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“…RP exploits the states of a dynamic system by constructing a two-dimensional binary matrix that corresponds to multiple points in the phase space trajectory that are roughly in the same region [ 95 ], thereby discovering hidden recurring patterns in the provided signal. In [ 96 ], the EEG signal was transformed to RP before being fed to an ensemble architecture of CNNs paired with a voting classifier. It was claimed that the use of RP displayed high-performance results in exploiting the interclass variability.…”

Section: Discussionmentioning

confidence: 99%

Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals—A Systematic Literature Review

Nafea

Ismail

2022

Bioengineering

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Electroencephalography (EEG) is a complicated, non-stationary signal that requires extensive preprocessing and feature extraction approaches to be accurately analyzed. In recent times, Deep learning (DL) has shown great promise in exploiting the characteristics of EEG signals as it can learn relevant features from raw data autonomously. Although studies involving DL have become more common in the last two years, the topic of whether DL truly delivers advantages over conventional Machine learning (ML) methodologies remains unsettled. This study aims to present a detailed overview of the main challenges in the field of seizure detection, prediction, and classification utilizing EEG data, and the approaches taken to solve them using ML and DL methods. A systematic review was conducted surveying peer-reviewed publications published between 2017 and 16 July 2022 using two scientific databases (Web of Science and Scopus) totaling 6822 references after discarding duplicate publications. Whereas 2262 articles were screened based on the title, abstract, and keywords, only 214 were eligible for full-text assessment. A total of 91 papers have been included in this survey after meeting the eligible inclusion and exclusion criteria. The most significant findings from the review are summarized, and several important concepts involving ML and DL for seizure detection, prediction, and classification are discussed in further depth. This review aims to learn more about the different approaches for identifying different types and stages of epileptic seizures, which may then be employed to enhance the lives of epileptic patients in the future, as well as aid experts in the field.

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Section: Discussionmentioning

confidence: 99%

Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals—A Systematic Literature Review

Nafea

Ismail

2022

Bioengineering

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“…It achieves an accuracy of 99.67% for single-channel EEGH datasets. Recurrence plots have been used as a means to capture the non-linear dynamics in the EEG signal 40 , 41 . Riemannian geometry has been used to transform the covariance matrices estimated from the non-invasive scalp EEG (sEEG) signals into a feature vector 42 .…”

Section: Related Workmentioning

confidence: 99%

EEG-based epileptic seizure detection using binary dragonfly algorithm and deep neural network

Yogarajan,

Alsubaie,

Rajasekaran

et al. 2023

Sci Rep

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Electroencephalogram (EEG) is one of the most common methods used for seizure detection as it records the electrical activity of the brain. Symmetry and asymmetry of EEG signals can be used as indicators of epileptic seizures. Normally, EEG signals are symmetrical in nature, with similar patterns on both sides of the brain. However, during a seizure, there may be a sudden increase in the electrical activity in one hemisphere of the brain, causing asymmetry in the EEG signal. In patients with epilepsy, interictal EEG may show asymmetric spikes or sharp waves, indicating the presence of epileptic activity. Therefore, the detection of symmetry/asymmetry in EEG signals can be used as a useful tool in the diagnosis and management of epilepsy. However, it should be noted that EEG findings should always be interpreted in conjunction with the patient's clinical history and other diagnostic tests. In this paper, we propose an EEG-based improved automatic seizure detection system using a Deep neural network (DNN) and Binary dragonfly algorithm (BDFA). The DNN model learns the characteristics of the EEG signals through nine different statistical and Hjorth parameters extracted from various levels of decomposed signals obtained by using the Stationary Wavelet Transform. Next, the extracted features were reduced using the BDFA which helps to train DNN faster and improve its performance. The results show that the extracted features help to differentiate the normal, interictal, and ictal signals effectively with 100% accuracy, sensitivity, specificity, and F1 score with a 13% selected feature subset when compared to the existing approaches.

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“…This method's analysis uses a real-time database and yields 93.4 percent precision. Ravi, S. et al [105] used a nonlinear method of obtaining EEG frames to detect abnormalities in the EEG. A CNN approach was trained for the real-time identification of epileptic seizures.…”

Section: Feature Extraction and Feature Selectionmentioning

confidence: 99%

EEG Feature Engineering Methods-A Comprehensive Review

Martin¹

2022

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Today, the primary topic of discussion in the signal processing domain is the analysis of non-stationary and nonlinear signal data. The use of biomedical equipment generates enormous amounts of physiological data that can be analyzed and used for clinical diagnosis. Manual inference of specific decisions from such signals is laborious due to artifacts and the time-series nature of the data, particularly for electroencephalography (EEG) signals. As a result, it is critical to employ appropriate methods for signal analysis. The purpose of this survey is to gain an understanding of the various techniques used to process EEG signal data in epileptic seizure detection frameworks. A variety of classification and regression frameworks based on machine learning have been reviewed. This systematic review will adhere to the PRISMA guidelines. This survey uncovered several significant findings.

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Epileptic seizure detection using convolutional neural networks and recurrence plots of EEG signals

Cited by 14 publications

References 22 publications

Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals—A Systematic Literature Review

Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals—A Systematic Literature Review

EEG-based epileptic seizure detection using binary dragonfly algorithm and deep neural network

EEG Feature Engineering Methods-A Comprehensive Review

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