Amplitude-integrated EEG processing and its performance for automatic seizure detection

Satirasethawong, Chavin; Lek-uthai, Apiwat; Chomtho, Krisnachai

doi:10.1109/icsipa.2015.7412252

Cited by 10 publications

(11 citation statements)

References 27 publications

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“…Thus, aEEG is widely used as a bedside available screening tool for neonatal seizure detection. For automating the seizure detection process, only two studies were searched and reviewed [40], [41].…”

Section: Automatic Seizure Detectionmentioning

confidence: 99%

“…With the development of the digital aEEG system, it provides the opportunity towards a quantitative approach of aEEG interpretation. In recent years, different algorithms have been proposed to digitalize the aEEG transformation process or to automate the interpretation process [32]- [41]. With the development and utilization of the novel digitalized aEEG transformation procedure, a quantity of automatic aEEG interpretation algorithms like the machine learning methods for the aEEG tracing classification [34]- [39], the algorithms for the automatic seizure detection [40], [41] have been proposed.…”

Section: Introductionmentioning

confidence: 99%

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Amplitude-Integrated Electroencephalography Applications and Algorithms in Neonates: A Systematic Review

et al. 2019

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Amplitude-integrated electroencephalography (aEEG) is a simplified method for long-term, continuous, and bedside monitoring of brain activity. While conventional Electroencephalography (EEG) is the gold standard of assessing brain function, aEEG is easy to operate and allows bedside interpretation of brain activity by health care providers without extensive knowledge of neurophysiology. aEEG is increasingly applied in neurological monitoring in neonates, especially in the neonatal intensive care unit (NICU). To a growing extent, researchers and clinicians are convinced that aEEG provides valuable clinical information and can be used to assess the severity of neonatal encephalopathy. Meanwhile, to digitalize the aEEG transformation process and automate the interpretation process, different algorithms have been proposed in the last decades. This paper provides a comprehensive review of aEEG for neonatal monitoring from both clinical and technological perspectives. The paper first reviews the clinical applications of aEEG and discusses the merits and demerits of neonatal aEEG monitoring in terms of the assistance of the treatment and prognosis of cerebral diseases like hypoxic-ischemic encephalopathy (HIE), seizure and so on. And then furthermore, the algorithms to transform EEG into aEEG and the algorithms for aEEG interpretation like the automatic classification of aEEG tracing, automatic seizure detection of aEEG, etc. are reviewed.INDEX TERMS Amplitude-integrated electroencephalography, cerebral function monitoring, automatic seizure detection.

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Section: Automatic Seizure Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Amplitude-Integrated Electroencephalography Applications and Algorithms in Neonates: A Systematic Review

et al. 2019

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“…Currently, many studies have been developed based on AI and machine learning to automatically detect epileptic seizures in EEG records [ASS + 13, AFS + 15, AESAA15, BLuCS20, HSZ20]. Some studies used features relevant to amplitude of EEGs such as aEEG [SLuC15] and energy [SG10,ATE10] However, inclusion and exclusion criteria were not given with exact details; it is not clear how well their methods perform on the excluded data set. It is evident that determining the seizure onset and offset as the first and last epochs of seizures predicted by existing epoch-based seizure classifiers is clinically inappropriate.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have developed methods to automatically detect epileptic seizures in EEG epochs (Acharya et al, 2015;Alotaiby et al, 2015;Boonyakitanont et al, 2020a;Hassan et al, 2020). Some studies focused on extracting single features relevant to EEG characteristics, e.g., amplitude (Satirasethawong et al, 2015;Shoeb and Guttag, 2010;Altunay et al, 2010), statistics (Samiee et al, 2015;Li et al, 2017), entropy (Tawfik et al, 2016;Li et al, 2018;Gupta and Pachori, 2019) and predictability (Kumar et al, 2015;Jaiswal and Banka, 2017;Li et al, 2019). Others have examined combinations of features to jointly distinguish between ictal patterns and normal activities (Mursalin et al, 2017;Alickovic et al, 2018;Vidyaratne and Iftekharuddin, 2017;Fergus et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

ScoreNet: A Neural network-based post-processing model for identifying epileptic seizure onset and offset in EEGs

Boonyakitanont

Lek-uthai

Songsiri

2020

Preprint

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This article aims to design an automatic detection algorithm of epileptic seizure onsets and offsets in scalp EEGs. A proposed scheme consists of two sequential steps: the detection of seizure episodes, and the determination of seizure onsets and offsets in long EEG recordings. We introduce a neural network-based model called ScoreNet as a post-processing technique to determine the seizure onsets and offsets in EEGs. A cost function called a log-dice loss that has an analogous meaning to F1 is proposed to handle an imbalanced data problem. In combination with several classifiers including random forest, CNN, and logistic regression, the ScoreNet is then verified on the CHB-MIT Scalp EEG database. As a result, in seizure detection, the ScoreNet can significantly improve F1 to 70.15% and can considerably reduce false positive rate per hour to 0.05 on average. In addition, we propose detection delay metric, an effective latency index as a summation of the exponential of delays, that includes undetected events into account. The index can provide a better insight into onset and offset detection than conventional time-based metrics.

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“…Nevertheless, this task is still a time-consuming process for the neurologists to review the continuous EEG. Therefore, an automated epileptic seizure detection using EEG signals is developed to facilitate the interpretation of long-term monitoring [7].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Deep Neural Networks for Seizure Detection in EEG Signals

Boonyakitanont

Lek-uthai

Chomtho

et al. 2019

Preprint

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This paper aims to apply machine learning techniques to an automated epileptic seizure detection using EEG signals to help neurologists in a time-consuming diagnostic process. We employ two approaches based on convolution neural networks (CNNs) and artificial neural networks (ANNs) to provide a probability of seizure occurrence in a windowed EEG recording of 18 channels. In order to extract relevant features based on time, frequency, and time-frequency domains for these networks, we consider an improvement of the Bayesian error rate from a baseline. Features of which the improvement rates are higher than the significant level are considered. These dominant features extracted from all EEG channels are concatenated as the input for ANN with 7 hidden layers, while the input of CNN is taken as raw multi-channel EEG signals. Using multi-concept of deep CNN in image processing, we exploit 2D-filter decomposition to handle the signal in spatial and temporal domains. Our experiments based on CHB-MIT Scalp EEG Database showed that both ANN and CNN were able to perform with the overall accuracy of up to 99.07% and F1-score of up to 77.04%. ANN with dominant features is more capable of detecting seizure events than CNN whereas CNN requiring no feature extraction is slightly better than ANN in classification accuracy.

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Amplitude-integrated EEG processing and its performance for automatic seizure detection

Cited by 10 publications

References 27 publications

Amplitude-Integrated Electroencephalography Applications and Algorithms in Neonates: A Systematic Review

Amplitude-Integrated Electroencephalography Applications and Algorithms in Neonates: A Systematic Review

ScoreNet: A Neural network-based post-processing model for identifying epileptic seizure onset and offset in EEGs

A Comparison of Deep Neural Networks for Seizure Detection in EEG Signals

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