Seizure detection using EEG: A survey of different techniques

Ahmad, Maham; Saeed, Maryam; Saleem, Sajid; Kamboh, Awais Mehmood

doi:10.1109/icet.2016.7813209

Cited by 17 publications

(9 citation statements)

References 48 publications

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“…Observation of this model M. Ahmad, proposed model he categorized model into two parts one is spectral domain feature another part is temporal domain feature. The implementation of this handcrafted feature though it differs from various technology, continuously it involves expert to acquire lots of presentation from data [11].…”

Section: Literature Surveymentioning

confidence: 99%

Improved feature exctraction process to detect seizure using CHBMIT-dataset

Kumar¹,

Narayanappa

2021

IJECE

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One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.

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Section: Literature Surveymentioning

confidence: 99%

Improved feature exctraction process to detect seizure using CHBMIT-dataset

Kumar¹,

Narayanappa

2021

IJECE

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“…The authors of [27] provided a review on the applications of entropies with their advantages and disadvantages in epilepsy analysis. A brief description of the epileptic seizure detection and analysis process including preprocessing, feature extraction, feature ranking/selection and classification was conducted in [28]. Automated epileptic seizure detection techniques based on multi-domain approaches were reviewed and highlighted in [29].…”

Section: Introductionmentioning

confidence: 99%

A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

Saminu

Zhang

et al. 2021

Brain Sciences

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The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

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“…It measures the electrical potential detected by each electrode. EEG can be used in monitoring the brain during anesthesia [ 28 ], surgical procedures [ 29 ], and investigations of brain disorders (psychoses [ 30 ], meningoencephalitis [ 31 ], Parkinson [ 9 ], Alzheimer [ 32 , 33 , 34 , 35 , 36 , 37 ], dementia [ 38 ], epilepsy [ 39 , 40 , 41 , 42 ], central motor neuron syndrome [ 43 ], cerebral palsy [ 44 , 45 , 46 ], and muscular dystrophy [ 47 ]). Mainly, EEG systems are used to diagnose and monitor patients with neuropathology, especially in diagnosis of epilepsy and in studying the seizures, as well as the monitoring of treatment and evolution.…”

Section: Introductionmentioning

confidence: 99%

“…Manifestations consist of involuntary movements of different body segments and abnormal neuro-vegetative sensations in the body. EEG analysis can be used to diagnose and monitor the patient in various stages of the disease (focal or generalized seizures, sleep) [ 38 , 39 , 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

EEG-Brain Activity Monitoring and Predictive Analysis of Signals Using Artificial Neural Networks

Aileni

Pasca

Florescu

2020

Sensors

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Predictive observation and real-time analysis of the values of biomedical signals and automatic detection of epileptic seizures before onset are beneficial for the development of warning systems for patients because the patient, once informed that an epilepsy seizure is about to start, can take safety measures in useful time. In this article, Daubechies discrete wavelet transform (DWT) was used, coupled with analysis of the correlations between biomedical signals that measure the electrical activity in the brain by electroencephalogram (EEG), electrical currents generated in muscles by electromyogram (EMG), and heart rate monitoring by photoplethysmography (PPG). In addition, we used artificial neural networks (ANN) for automatic detection of epileptic seizures before onset. We analyzed 30 EEG recordings 10 min before a seizure and during the seizure for 30 patients with epilepsy. In this work, we investigated the ANN dimensions of 10, 50, 100, and 150 neurons, and we found that using an ANN with 150 neurons generates an excellent performance in comparison to a 10-neuron-based ANN. However, this analyzes requests in an increased amount of time in comparison with an ANN with a lower neuron number. For real-time monitoring, the neurons number should be correlated with the response time and power consumption used in wearable devices.

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Seizure detection using EEG: A survey of different techniques

Cited by 17 publications

References 48 publications

Improved feature exctraction process to detect seizure using CHBMIT-dataset

Improved feature exctraction process to detect seizure using CHBMIT-dataset

A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

EEG-Brain Activity Monitoring and Predictive Analysis of Signals Using Artificial Neural Networks

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