Design and Implementation of a Smart Headband for Epileptic Seizure Detection and Its Verification Using Clinical Database

Lin, Shih-Kai; Qomah, Isti; Lin, Yu‐Shan; Chiueh, Herming; Lin, Chin-Yew

doi:10.1109/iscas.2018.8351687

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Ictal and seizure-free EEG signal classification was implemented on the FPGA of Secure Object Delivery Protocol (SODP) in Singh and Prince (2015). The created hardware took a sampled IMF of an EEG signal as input and generated the SODP while also computing the SODP's 95% con- Lin et al (2018) proposed a system platform that involved the design of a smart headband for epileptic seizure detection and the creation of smart device applications for healthcare which can be integrated with a cloud computing platform. A Real-Time Wearable FPGA-based seizure detection processor was proposed in Marni et al (2018) using metropolis-hastings.…”

Section: Hardware Experimental Papersmentioning

confidence: 99%

“…Lin et al (2018) proposed a system platform that involved the design of a smart headband for epileptic seizure detection and the creation of smart device applications for healthcare which can be integrated with a cloud computing platform. A Real‐Time Wearable FPGA‐based seizure detection processor was proposed in Marni et al (2018) using metropolis‐hastings.…”

Section: Hardware Developmentsmentioning

confidence: 99%

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A review on software and hardware developments in automatic epilepsy diagnosis using EEG datasets

et al. 2023

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Epilepsy is a common non-communicable, group of neurological disorders affecting more than 50 million individuals worldwide. Different approaches of basic, clinical, and translational research of the human brain have been explored to diagnose, treat, and manage the growing no. of cases of epilepsy. Various hospital information from video, images, signals, forms, and so forth, are retrieved and analysed to develop a consensus for such patients. Electroencephalography (EEG) tests are routinely used to diagnose the type of epilepsy in a clinical setting. Artificial Intelligence algorithms are assisting in the early detection and prediction of epileptic patterns observed in EEG signals. This paper reviews recent and emerging state-of-the-art (SOTA) software and hardware approaches in data selection, signal processing, feature estimation, classification, detection methods, and evaluation metrics applied to open and private EEG datasets from 2014 to 2022. The work summarizes and compares reported works through subjective and objective parameters. Rise in hardware pipeline, start-ups, and companies, deep learning methods in software pipeline and release of free EEG datasets have been observed from 2014 to 2022. SOTA

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Section: Hardware Experimental Papersmentioning

confidence: 99%

Section: Hardware Developmentsmentioning

confidence: 99%

A review on software and hardware developments in automatic epilepsy diagnosis using EEG datasets

et al. 2023

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“…Spectral features were extracted, and SSM was adapted to detect seizures [73]. Epileptic seizure detection was performed with the help of the LDA classifier [74]. Time-frequency domain features were extracted, and an ANFIS classifier was employed to differentiate seizure and non-seizure events [76].…”

Section: Classificationmentioning

confidence: 99%

“…The circuitry consisted of a flexible print circuit and fabric electrodes, which were integrated with a cloud computing platform. The 16 entropy features were extracted and given to the linear classifier, which effectively discriminated ictal and nonictal activity [74]. A multivariate method was applied to extract spectral graph-theoretic features to compute temporal synchronization patterns, which gave 98% sensitivity and a low latency of 6 s [75].…”

Section: Introductionmentioning

confidence: 99%

Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey

Prasanna

Subathra

Mohammed

et al. 2021

JPM

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Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures. Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic seizures caused by an unexpected flow of electrical activities in the brain. Automated detection of an epileptic seizure is a crucial task in diagnosing epilepsy which overcomes the drawback of a visual diagnosis. The dataset analyzed in this article, collected from Children’s Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), contains long-term EEG records from 24 pediatric patients. This review paper focuses on various patient-dependent and patient-independent personalized medicine approaches involved in the computer-aided diagnosis of epileptic seizures in pediatric subjects by analyzing EEG signals, thus summarizing the existing body of knowledge and opening up an enormous research area for biomedical engineers. This review paper focuses on the features of four domains, such as time, frequency, time-frequency, and nonlinear features, extracted from the EEG records, which were fed into several classifiers to classify between seizure and non-seizure EEG signals. Performance metrics such as classification accuracy, sensitivity, and specificity were examined, and challenges in automatic seizure detection using the CHB-MIT database were addressed.

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A Wearable System Design for Epileptic Seizure Detection

Sangeetha¹,

Shanthini²,

Prasad³

et al. 2021

Sustainable Communication Networks and Application

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Design and Implementation of a Smart Headband for Epileptic Seizure Detection and Its Verification Using Clinical Database

Cited by 5 publications

References 9 publications

A review on software and hardware developments in automatic epilepsy diagnosis using EEG datasets

A review on software and hardware developments in automatic epilepsy diagnosis using EEG datasets

Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey

A Wearable System Design for Epileptic Seizure Detection

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