Comparative sensitivity of quantitative EEG (QEEG) spectrograms for detecting seizure subtypes

Goenka, Ajay; Boro, Alexis; Yozawitz, Elissa

doi:10.1016/j.seizure.2018.01.008

Cited by 27 publications

(47 citation statements)

References 13 publications

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“…It may be best to use AS in tandem with another spectrogram type, such as CDSA or RS, so as to be able to capture both focal as well as other seizure types. 18 Of note, AS is also useful for ischemia detection as it can give information about background activity in one hemisphere compared with the other. 4,6,16 In the study by Goenka et al, 18 two trained epileptologists reviewed raw cEEG to identify gold standard seizures.…”

Section: Rhythmicity Spectrogrammentioning

confidence: 99%

“…6 Asymmetry spectrogram is most effective for focal seizures where the asymmetry between hemispheres would be most apparent. 18 Asymmetry spectrogram would not be very useful for a generalized seizure, as the power in each hemisphere would be comparable. It may be best to use AS in tandem with another spectrogram type, such as CDSA or RS, so as to be able to capture both focal as well as other seizure types.…”

Section: Qeeg Trend Classificationsmentioning

confidence: 99%

“…Sensitivity of nonexperts was found to range from 73.8% to 96%, and false alarm rate ranged from 0.1/hour to 3.2/hour. 9,18,20,31 The studies used CDSA, RS, aEEG, AS, and ET, often in combination, but sometimes with CDSA alone. 9,10,20,22,31 It is important to note that all of the studies recommend pairing qEEG with a full or focused cEEG read.…”

Section: Nonexpert Reviewmentioning

confidence: 99%

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Utility of Quantitative EEG for Seizure Detection in Adults

Kaleem

Swisher

2021

Journal of Clinical Neurophysiology

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Summary:Traditional review of EEG for seizure detection requires time and the expertise of a trained neurophysiologist; therefore, it is time- and resource-intensive. Quantitative EEG (qEEG) encompasses a variety of methods to make EEG review more efficient and allows for nonexpert review. Literature supports that qEEG is commonly used by neurophysiologists and nonexperts in clinical practice. In this review, the different types of qEEG trends and spectrograms used for seizure detection in adults, from basic concepts to clinical applications, are discussed. The merits and drawbacks of the most common qEEG trends are detailed. The authors detail the retrospective literature on qEEG sensitivity, specificity, and false alarm rate as interpreted by experts and nonexperts alike. Finally, the authors discuss the future of qEEG as a useful screening tool and speculate on the trajectory of future investigations in the field.

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Section: Rhythmicity Spectrogrammentioning

confidence: 99%

Section: Qeeg Trend Classificationsmentioning

confidence: 99%

Section: Nonexpert Reviewmentioning

confidence: 99%

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Utility of Quantitative EEG for Seizure Detection in Adults

Kaleem

Swisher

2021

Journal of Clinical Neurophysiology

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“…The phase-space reconstruction (PSR) technique was used to analyze the visual aspect of EEG signals in order to detect epileptic seizures [9,10]. In addition, the electroencephalogram (EEG) signals were decomposed into frequency domain using a Fourier transform [11]. The WT decomposed the EEG signals into time-frequency domain [12].…”

Section: Introductionmentioning

confidence: 99%

Detection of Epileptic Seizures Using Wavelet Transform, Peak Extraction and PSR from EEG Signals

Jang

Lee

2020

Symmetry

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In this study, techniques were proposed for the detection of epileptic seizures from electroencephalogram (EEG) signals using the wavelet transform (WT), peak extraction and phase–space reconstruction (PSR) based Euclidean distances. In the first step, the wavelet coefficients were extracted after eliminating the noise from the EEG signals using a WT, which is a widely used signal processing technique. In the second step, the peaks were extracted from the wavelet coefficients. In the third step, the continuous peaks that were extracted were mapped to 3D coordinates using PSR. In the fourth step, the Euclidean distances between the mapped 3D coordinates and the origin were obtained. The features of the Euclidean distances obtained were extracted using statistical techniques. The final features extracted were used as inputs to the neural network with weighted fuzzy membership (NEWFM). NEWFM contains the bounded sum of weighted fuzzy memberships (BSWFMs) that can reveal the differences in the graphic characteristics between normal EEG signals and epileptic-seizure EEG signals. The BSWFMs can easily be embedded in a portable device to detect epileptic seizures from EEG signals in real life.

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“…Bu nedenlerden dolayı EEG sinyallerinden maksimum bilgiyi edinebilmek için bilgisayar ortamında EEG verilerini analiz eden çalışmalar yapılmakta ve başta Fourier dönüşümü, dalgacık dönüşümü ve yapay sinir ağları olmak üzere farklı nicelleştirme yöntemleri kullanılmaktadır. Bu yöntemler aracılığıyla nicelleştirilen EEG verilerinden deliryum [2], depresyon [3], epilepsi [4], [5], Alzheimer [6], [7], travmaya bağlı beyin hasarı [8], şizofreni [9], çocuklarda dikkat eksikliği ve hiperaktivite [10], [11] gibi sinir sistemi ile ilişkili hastalıklarda beyin dinamiğinde meydana gelen değişikliklere ilişkin kapsamlı bilgi edinilebilmektedir.…”

Section: Introductionunclassified

Determination and mapping of energy changes related to brain oscillations with a new approach based on wavelet transform: An epilepsy example

Değirmenci¹,

Yalçin²,

Çekirge³

et al. 2020

Pamukkale J Eng Sci

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Beyin osilasyonlarının nicel analizi, başta epilepsi olmak üzere çeşitli beyin patolojilerinde beyin dinamiklerinin anlaşılmasına katkıda bulunur. Bu çalışmada dalgacık dönüşümü temelli yeni bir nicel yaklaşım kullanılarak beyin osilasyonlarının enerji değişimlerinin saptanması ve haritalanması amaçlanmıştır. Oluşturulan sinyallerin analizinde EEG'yi dört temel alt frekans bandına ayrıştıran ve ardından bu bantların enerji değerlerini nicelleştiren bir yaklaşım önerilmiştir. Ayrıca, bu enerji değerleri kullanılarak kafa yüzeyi enerji haritaları oluşturulmuştur. Önerilen yöntemi test etmek için CHB-MIT Scalp EEG Database veri tabanından alınan epilepsili hastalara ait EEG kayıtları kullanılmıştır. Analiz edilecek sinyaller nöbet anı ortada olmak üzere nöbet süresi uzunluğu kadar öncesi ve sonrasını kapsayacak şekilde tüm kayıttan ayrılarak hazırlanmıştır. Elde edilen sayısal veriler kullanılarak her bir alt bant enerji yüzdesi için nöbet öncesi, nöbet anı ve nöbet sonrası enerji farklılıkları istatistiksel olarak da karşılaştırılmıştır. Bu çalışmada sunulan yöntemin epilepsi ve diğer beyin patolojileri için kayıtlanan tüm EEG'ler için uygulanarak beyin dinamikleri hakkında bilgi edinilebileceği ve aynı anda çok sayıda montajdan kayıt alınması durumunda beyin patolojilerine yol açan odakların belirlenmesinde diğer beyin görüntüleme yöntemleriyle birlikte kullanılabileceği düşünülmüştür.The quantitative analysis of brain oscillations has an importance in understanding brain dynamics of several brain pathologies. In this study, it is purposed to determine and map the change in the energy of brain oscillations using a new quantitative approach, based on wavelet transform. An approach, seperating the EEG records to four main subband and then quantizing the energy values of them, is suggested. Moreover, energy maps of scalp surface are formed by using these energy values. To test the suggested method, EEG records of epileptic patients which had been obtained from CHB-MIT Scalp EEG database were utilized. The signals which would be analyzed were prepared such that the seizure was in the middle and the signal consists of pre-seizure and post-seizure as long as the seizure time. Then, by using the data obtained, energy differences during pre-seizure, seizure and postseizure were compared for each sub-band energy percentage. It is thought that the suggested method can be applied to EEGs recorded for epilepsy and other pathologies to be informed about brain dynamics. In addition, in case of getting records simultaneously from several montages, the method can be used for localization of sources causing brain pathologies with other brain imaging techniques.Anahtar kelimeler: Epilepsi, EEG, Nicel analiz, Beyin enerji haritası, Dalgacık dönüşümü.

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Comparative sensitivity of quantitative EEG (QEEG) spectrograms for detecting seizure subtypes

Abstract: Our study suggests that different seizure types have specific patterns in the Persyst QEEG spectrograms. Identifying these patterns in the EEG can significantly increase the sensitivity for seizure identification.

Cited by 27 publications

References 13 publications

Utility of Quantitative EEG for Seizure Detection in Adults

Utility of Quantitative EEG for Seizure Detection in Adults

Detection of Epileptic Seizures Using Wavelet Transform, Peak Extraction and PSR from EEG Signals

Determination and mapping of energy changes related to brain oscillations with a new approach based on wavelet transform: An epilepsy example

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