EEG based epileptiform pattern recognition inside and outside the seizure states

Göksu, Hüseyin

doi:10.1016/j.bspc.2018.03.004

Cited by 28 publications

(20 citation statements)

References 47 publications

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“…The accuracy obtained by our method is also higher than that by Zhang and Hassan's methods [55], [61] in the three-class classification. Besides, the result in our work is comparable with that in Göksu's work [19]. In Gö ksu's method, the combination of traditional energy and entropy in sub-bands leads to a promising result, which encourages us to enhance the classification performance by introducing the nonlinear dynamics to our method in the following work.…”

Section: Discussionsupporting

confidence: 73%

“…Baldominos et al [18] used energy ratios resulting from the division of the energy in a small window and the energy in a much larger window. Göksu [19] extracted energy and entropies as feature vectors in the time-frequency domain.…”

Section: Introductionmentioning

confidence: 99%

“…The second category uses modern classifiers and compares favorably with the first category in terms of accuracy. In the third category, the scheme augments extra types of features besides energy, such as entropy and standard deviation, for the classifier [12], [13], [16], [17], [19]. However, all three categories speak about the 'energy' as the usual tendency in the traditional signal processing literature.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identification of Epileptic Seizures by Characterizing Instantaneous Energy Behavior of EEG

et al. 2019

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Automatic seizure detection has been often treated as a classification problem that aims at determining the label of electroencephalogram (EEG) signals by computer science, as the EEG monitoring is a helpful adjunct to the diagnosis of epilepsy. In most existing work, the traditional signal energy of the EEG has been applied for classification, since the energy pattern of epileptic seizures differs from that of non-seizures. Although they are effective, the accuracy either heavily depends on additional information besides energy or is limited by the shortcoming of energy-based features. To address this issue, the proposed approach achieves the classification based on the instantaneous energy of the EEG signals instead. The proposed approach first measures the instantaneous energy related to changes in the EEG signals. Then, energy behavior over time is characterized by instantaneous energy-based features from different aspects. Finally, the classification is carried out on the features to produce output labels. By processing instantaneous energy, the information of energy evolution is involved. As such, the accuracy is improved without bringing in extra information besides energy, or complicated transformation. In multi-class problems, the proposed approach has obtained promising results for identifying the ictal EEG, which indicates the tremendous potential of the proposed approach for epileptic seizure detection. INDEX TERMSSeizure identification, electroencephalogram (EEG) signal, instantaneous energy, energy behavior.

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Section: Discussionsupporting

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of Epileptic Seizures by Characterizing Instantaneous Energy Behavior of EEG

et al. 2019

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“…Apart from the considerable amount of time it takes even for an expert neurologist to identify and categorise fast oscillations, the process is obviously prone to subjective perception and bias [ 38 ]. Automated diagnosis of epilepsy [ 59 ], automated detection of epileptic spikes [ 60 ], automated seizure detection [ 61 ], and even seizure prediction [ 62 ] were supported by advanced algorithms from digital signal processing, often alongside with artificial intelligence. These technical advances have also been introduced into HFO research and proposed concepts and algorithms for automated detection of HFOs [ 38 , 63 – 80 ].…”

Section: Automated Hfo Detectionmentioning

confidence: 99%

High-Frequency Oscillations in the Scalp Electroencephalogram: Mission Impossible without Computational Intelligence

Höller

Trinka

Höller

2018

Computational Intelligence and Neuroscience

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High-frequency oscillations (HFOs) in the electroencephalogram (EEG) are thought to be a promising marker for epileptogenicity. A number of automated detection algorithms have been developed for reliable analysis of invasively recorded HFOs. However, invasive recordings are not widely applicable since they bear risks and costs, and the harm of the surgical intervention of implantation needs to be weighted against the informational benefits of the invasive examination. In contrast, scalp EEG is widely available at low costs and does not bear any risks. However, the detection of HFOs on the scalp represents a challenge that was taken on so far mostly via visual detection. Visual detection of HFOs is, in turn, highly time-consuming and subjective. In this review, we discuss that automated detection algorithms for detection of HFOs on the scalp are highly warranted because the available algorithms were all developed for invasively recorded EEG and do not perform satisfactorily in scalp EEG because of the low signal-to-noise ratio and numerous artefacts as well as physiological activity that obscures the tiny phenomena in the high-frequency range.

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“…Meanwhile, epilepsy diagnosis has been also attracted much attention in the area of pattern recognition [15]- [17]. It normally relies on feature extraction and pattern classification techniques.…”

Section: Introductionmentioning

confidence: 99%

A Unified Framework and Method for EEG-Based Early Epileptic Seizure Detection and Epilepsy Diagnosis

Chen

Xie

et al. 2020

IEEE Access

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Electroencephalogram (EEG) contains important physiological information that can reflect the activity of human brain, making it useful for epileptic seizure detection and epilepsy diagnosis. However visual inspection of large amounts of EEG by human expert is time-consuming, and meanwhile there are often inconsistences in judgement between physicians. In this paper, we develop a unified framework for early epileptic seizure detection and epilepsy diagnosis, which includes two phases. In the first phase, the signal intensity is first calculated for each data point of the given EEG, enabling the well-known autoregressive moving average (ARMA) model to characterize the dynamic behavior of the EEG time series. The residual error between the predicted value of learned ARMA model and the actually observed value is used as the anomaly score to support a null hypothesis testing for making epileptic seizure decision. The epileptic seizure detection phase can provide a quick detection for anomaly EEG patterns, but the resulting suspicious segment may include epilepsy or other disordering EEG activities thus required to be identified. Therefore, in the second phase, we use pattern recognition technique to classify the suspicious EEG segments. In particular, we propose a new and practical classifier based on a pairwise of one-class SVMs for epilepsy diagnosis. The proposed classifier requires normal and epilepsy data for training, but can recognize normal, epilepsy and even other disorders that would not be trained in the training samples. This point is practical and meaningful in real clinic scenarios as the input EEG may include other brain disordering diseases besides of epilepsy. We conducted experiments on the publicly-available Bern-Barcelona and CHB-MIT EEG database, respectively, to validate the effectiveness of the proposed framework, and our method achieved classification accuracy of 93% and 94% on them. Comprehensive experimental results, outperforming the state-of-the-arts, suggest its great potentials in real applications.

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EEG based epileptiform pattern recognition inside and outside the seizure states

Cited by 28 publications

References 47 publications

Identification of Epileptic Seizures by Characterizing Instantaneous Energy Behavior of EEG

Identification of Epileptic Seizures by Characterizing Instantaneous Energy Behavior of EEG

High-Frequency Oscillations in the Scalp Electroencephalogram: Mission Impossible without Computational Intelligence

A Unified Framework and Method for EEG-Based Early Epileptic Seizure Detection and Epilepsy Diagnosis

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