American Clinical Neurophysiology Society Guideline 1: Minimum Technical Requirements for Performing Clinical Electroencephalography

Sinha, Saurabh; Sullivan, Lucy; Sabau, Dragos; San-Juan, Daniel; Dombrowski, Keith; Halford, Jonathan J.; Hani, Abeer J.; Drislane, Frank W.; Stecker, Mark M.

doi:10.1097/wnp.0000000000000308

Cited by 151 publications

(50 citation statements)

References 7 publications

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“…Epileptik anormallikleri yakalamak için tekrarlayan rutin EEG çekimleri yapılması önerilmektedir. [12,13] Nöbet sonrası çekilen ilk rutin EEG'de epileptiform anormalliği yakalama oranı %29-55 iken, 3. EEG'de %39-72'ye kadar yükselebilmektedir.…”

Section: Discussionunclassified

Outcome of Long-term Video-EEG Monitoring

Arslan¹

2017

Epilepsi

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Objectives: Long-term video-electroencephalogram (EEG) monitoring (VEM) is a diagnostic system used for many purposes, including the precise categorization of epileptic seizures, excluding non-epileptic seizures, and finding the seizure onset zone. The aim of this study was to demonstrate the importance of the use of VEM in the diagnosis and differential diagnosis of epilepsy.Methods: Data of patients who were hospitalized in the video-EEG unit of Dicle University Neurology Department between 2012 and 2016 were retrospectively evaluated. The records of 245 patients that were of at least 24-hours duration were included in the study. Results:The mean duration of recording was 3.3±1.3 days. Clinically observed seizures were detected in 37.5% (n=92) of the patients. Of those, 21.2% (n=52) were evaluated as epileptic seizures and 16.3% (n=40) were defined as non-epileptic seizures. The proportion of psychogenic non-epileptic seizures was 14% (n=36). The mean length of the recording of the first seizure attack was 1.6 days. Interictal EEG abnormalities were found in 13.4% (n=33) of the patients. The mean duration of the disorder was 7.3 years. Conclusion:Medical history, physical examination, and routine EEG procedures can be misleading factors in the diagnosis of epilepsy. VEM is a crucial technique to differentiate diagnoses in patients with treatment-resistant epilepsy and to precisely diagnose the seizure type and the epileptic syndrome.Amaç: Uzun süreli video-EEG monitörizasyonu (VEM); epilepsi nöbetlerini sınıflamak, non-epileptik nöbetleri ayırt etmek ve nöbet başlangıç alanını saptamak gibi çok çeşitli durumlarda kullanılan faydalı bir tanı yöntemidir. Bu çalışmada epilepsi tanı ve ayırıcı tanısında VEM uygulamasının önemini vurgulamayı amaçladık. Gereç veYöntem: Dicle Üniversitesi Nöroloji Kliniği Video EEG Ünitesi'nde 2012-2016 yılları arasında yatmış olan hastaların VEM raporları geriye dönük olarak incelendi. En az 24 saatlik kayıtlar dikkate alınarak toplam 245 hasta çalışmaya dahil edildi. Bulgular: Ortalama kayıt süresi 3.3±1.3 gün idi. Doksan iki hastada (%37.5) klinik olarak nöbet gözlendi. Bunların 52'si (%21.2) epileptik, 40'ı (%16.3) non-epileptik nöbetler olarak değerlendirildi. Psikojen non-epileptik nöbet (PNEN) oranı %14 (36 hasta) olarak saptandı. İlk nöbetin kaydedilme zamanı ortalama olarak 1.6 gün idi. Otuz üç hastada (%13.4) interiktal EEG'de anormallik saptandı. Ortalama hastalık süresi 7.3 yıl idi. Sonuç: Epilepsi hastalarının tanısında öykü, muayene, rutin EEG ile sınırlı kalındığında yanılma ihtimalinin olabileceğini, tedaviye dirençli olgularda PNEN ayırıcı tanısı için, nöbet tipi ve epileptik sendromun kesin tanısı için VEM yapılmasının önemli olduğunu düşünmekteyiz. Anahtar sözcükler: Elektroensefalografi; epilepsi; video-EEG monitörizasyonu. Demet ARSLAN Dicle Üniversitesi Tıp Fakültesi, Nöroloji Anabilim Dalı, Diyarbakır

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Section: Discussionunclassified

Outcome of Long-term Video-EEG Monitoring

Arslan¹

2017

Epilepsi

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“…Routine-EEG recording followed the requirements for performing clinical electroencephalography from the American Clinical Neurophysiology Society (ACNS) guideline [ 21 ]. Accordingly, all 21 electrodes were placed in the 10–20 System standard, and the instrument settings (sensitivities, filters, paper speed, and montage) agree with the guideline recommendations [ 21 ]. Additionally, simultaneous video recordings were performed to allow interpretation of clinical events and artifacts.…”

Section: Methodsmentioning

confidence: 99%

Outcomes of patients with altered level of consciousness and abnormal electroencephalogram: A retrospective cohort study

et al. 2017

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IntroductionNonconvulsive seizures (NCS) are frequent in hospitalized patients and may further aggravate injury in the already damaged brain, potentially worsening outcomes in encephalopathic patients. Therefore, both early seizure recognition and treatment have been advocated to prevent further neurological damage.ObjectiveEvaluate the main EEG patterns seen in patients with impaired consciousness and address the effect of treatment with antiepileptic drugs (AEDs), continuous intravenous anesthetic drugs (IVADs), or the combination of both, on outcomes.MethodsThis was a single center retrospective cohort study conducted in a private, tertiary care hospital. Consecutive adult patients with altered consciousness submitted to a routine EEG between January 2008 and February 2011 were included in this study. Based on EEG pattern, patients were assigned to one of three groups: Group Interictal Patterns (IP; EEG showing only interictal epileptiform discharges or triphasic waves), Group Rhythmic and Periodic Patterns (RPP; at least one EEG with rhythmic or periodic patterns), and Group Ictal (Ictal; at least one EEG showing ictal pattern). Groups were compared in terms of administered antiepileptic treatment and frequency of unfavorable outcomes (modified Rankin scale ≥3 and in-hospital mortality).ResultsTwo hundred and six patients (475 EEGs) were included in this analysis. Interictal pattern was observed in 35.4% (73/206) of patients, RPP in 53.4% (110/206) and ictal in 11.2% (23/206) of patients. Treatment with AEDs, IVADs or a combination of both was administered in half of the patients. While all Ictal group patients received treatment (AEDs or IVADs), only 24/73 (32.9%) IP group patients and 55/108 (50.9%) RPP group patients were treated (p<0.001). Hospital length of stay (LOS) and frequency of unfavorable outcomes did not differ among the groups. In-hospital mortality was higher in IVADs treated RPP patients compared to AEDs treated RPP patients [11/19 (57.9%) vs. 11/36 (30.6%) patients, respectively, p = 0.049]. Hospital LOS, in-hospital mortality and frequency of unfavorable outcomes did not differ between Ictal patients treated exclusively with AEDs or IVADs.ConclusionIn patients with acute altered consciousness and abnormal routine EEG, antiepileptic treatment did not improve outcomes regardless of the presence of periodic, rhythmic or ictal EEG patterns.

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“…The data were sampled at 128 Hz and converted using a 12 bit converter. [19] We used the program Wave-Finder (WF) [20] to segment a signal, compute the features for individual segments, and for the visualization of results. The Wave-Finder program uses adaptive segmentation (for more information about the method, see this study [21] activity).…”

Section: Preprocessingmentioning

confidence: 99%

Automatic Eeg Classification Using Density Based Algorithms Dbscan and Denclue

2019

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Electroencephalograph (EEG) is a commonly used method in neurological practice. Automatic classifiers (algorithms) highlight signal sections with interesting activity and assist an expert with record scoring. Algorithm K-means is one of the most commonly used methods for EEG inspection. In this paper, we propose/apply a method based on density-oriented algorithms DBSCAN and DENCLUE. DBSCAN and DENCLUE separate the nested clusters against K-means. All three algorithms were validated on a testing dataset and after that adapted for a real EEG records classification. 24 dimensions EEG feature space were classified into 5 classes (physiological, epileptic, EOG, electrode, and EMG artefact). Modified DBSCAN and DENCLUE create more than two homogeneous classes of the epileptic EEG data. The results offer an opportunity for the EEG scoring in clinical practice. The big advantage of the proposed algorithms is the high homogeneity of the epileptic class.

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American Clinical Neurophysiology Society Guideline 1: Minimum Technical Requirements for Performing Clinical Electroencephalography

Cited by 151 publications

References 7 publications

Outcome of Long-term Video-EEG Monitoring

Outcome of Long-term Video-EEG Monitoring

Outcomes of patients with altered level of consciousness and abnormal electroencephalogram: A retrospective cohort study

Automatic Eeg Classification Using Density Based Algorithms Dbscan and Denclue

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