Diagnosis of seizures and encephalopathy using conventional EEG and amplitude integrated EEG

Boylan, Geraldine B.; Kharoshankaya, Liudmila; Mathieson, Sean

doi:10.1016/b978-0-444-64029-1.00018-7

Cited by 22 publications

(21 citation statements)

References 186 publications

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“…3 Imminently harmful differential diagnoses of status epilepticus, acute transverse myelitis, infection-mediated Guillain-Barré's syndrome, and acute demyelinating encephalomyelitis were all considered and ruled out during the patient's work-up. [6][7][8][9] With the patient's gastrointestinal symptoms, rotavirusassociated CNS manifestations were also considered. 10 Possible sequelae, including both encephalopathy and flaccid paralysis, present in our case have been reported.…”

Section: Discussionmentioning

confidence: 99%

Acetazolamide-Triggered Acute Flaccid Paralysis and Toxic-Metabolic Encephalopathy in a Toddler with Gastroenteritis: A Case Report

Green

Nawathe

2020

Journal of Child Science

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Acetazolamide is an infrequently prescribed medication in the outpatient pediatric setting, as one of its major indications is for the treatment of glaucoma, which is a largely adult disease. Though incredibly rare, serious neurologic side effects such as stroke, flaccid paralysis, and coma can occur after its administration. This case presentation of acute flaccid paralysis with metabolic derangement underscores the unusual, yet possible, toxic-metabolic and neurologic sequelae that can occur in an already acidotic host (in this case, caused by gastroenteritis) with acetazolamide ingestion. Life-threatening conditions must always be ruled out in patients who present with encephalopathy, but a medication history was crucial in clinching this case's unifying diagnosis. While there are case reports highlighting the rare central nervous system toxicities associated with acetazolamide ingestion, to our knowledge, none exist in the pediatric literature.

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Section: Discussionmentioning

confidence: 99%

Acetazolamide-Triggered Acute Flaccid Paralysis and Toxic-Metabolic Encephalopathy in a Toddler with Gastroenteritis: A Case Report

Green

Nawathe

2020

Journal of Child Science

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“…Therefore, the goal of SVM is to maximize the classification interval under the condition of satisfying the constraint formula ( 2 ), that is, to solve the problem of the following formula [ 24 ]:

…”

Section: Experimental Designmentioning

confidence: 99%

Artificial Intelligence Analysis of EEG Amplitude in Intensive Heart Care

Chen

Wang

2021

Journal of Healthcare Engineering

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This article first studied the morphological characteristics of the EEG for intensive cardiac care; that is, based on the analysis of the mechanism of disease diagnosis and treatment, a signal processing and machine learning model was constructed. Then, the methods of signal preprocessing, signal feature extraction, new neural network model structure, training mechanism, optimization algorithm, and efficiency are studied, and experimental verification is carried out for public data sets and clinical big data. Then, the principle of intensive cardiac monitoring, the mechanism of disease diagnosis, the types of arrhythmia, and the characteristics of the typical signal are studied, and the rhythm performance, individual variability, and neurophysiological basis of electrical signals in intensive cardiac monitoring are researched. Finally, the automatic signal recognition technology is studied. In order to improve the training speed and generalization ability, a multiclassification model based on Least Squares Twin Support Vector Machine (LS-TWIN-SVM) is proposed. The computational complexity of the classification model algorithm is compared, and intelligence is adopted. The optimization algorithm selects the parameters of the classifier and uses the EEG signal to simulate the model. Support Vector Machines and their improved algorithms have achieved the ultimum in shallow neural networks and have achieved good results in the classification and recognition of bioelectric signals. The LS-TWIN-SVM algorithm proposed in this paper has achieved good results in the classification and recognition of bioelectric signals. It can perform bioinformatics processing on intensive cardiac care EEG signals, systematically biometric information, diagnose diseases, the real-time detection, auxiliary diagnosis, and rehabilitation of patients.

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“…Several authors have reported algorithms for automatic seizure detection, 44,45 which will help to detect neonatal seizures in clinical NICU settings, although the accuracy of seizure detection is not always satisfactory at present. Moreover, automatic interpretation of background EEG activities may be available along with the advance of deep learning of EEG data 46,47 . Objective EEG classification derived from deep learning may help to clarify EEG characteristics in some specific neonatal epilepsy.…”

Section: Future Prospectsmentioning

confidence: 99%

“…Moreover, automatic interpretation of background EEG activities may be available along with the advance of deep learning of EEG data. 46,47 Objective EEG classification derived from deep learning may help to clarify EEG characteristics in some specific neonatal epilepsy. The importance of EEG is increasing in order to understand neonatal epilepsy more precisely.…”

Section: Future Prospectsmentioning

confidence: 99%

Electroencephalography in neonatal epilepsies

Okumura

2020

Pediatrics International

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Neonatal epilepsiesneonatal seizures caused by remote symptomatic etiologiesare infrequent compared with those caused by acute symptomatic etiologies. The etiologies of neonatal epilepsies are classified into structural, genetic, and metabolic. Electroencephalography (EEG) and amplitude-integrated EEG (aEEG) are essential for the diagnosis and monitoring of neonatal epilepsies. Electroencephalography / aEEG findings may differ substantially among infants, even within infants with variants in a single gene. Unusual EEG/aEEG findings, such as downward seizure patterns on aEEG, can be found. Neonatal seizures are exclusively of focal onset. An International League Against Epilepsy task force proposed that the seizure type is typically determined by the predominant clinical feature and is classified into motor or non-motor presentations. Ictal EEG usually demonstrates a sudden, repetitive, evolving, and stereotyped activities with a minimum duration of 10 s. In epileptic spasms and myoclonic seizures, the cutoff point of 10 s cannot be applied. One must always be aware of electro-clinical dissociation in neonates suspected to have seizures. Amplitude-integrated EEG is also useful for the diagnosis and monitoring of neonatal epilepsies but aEEG cannot be recommended as the mainstay because of its relatively low sensitivity and specificity. At present, EEG findings are not pathognomonic, although some characteristic ictal or interictal EEG findings have been reported in several neonatal epilepsies. Deep learning will be expected to be introduced into EEG interpretation in near future. Objective EEG classification derived from deep learning may help to clarify EEG characteristics in some specific cases of neonatal epilepsy.

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Diagnosis of seizures and encephalopathy using conventional EEG and amplitude integrated EEG

Cited by 22 publications

References 186 publications

Acetazolamide-Triggered Acute Flaccid Paralysis and Toxic-Metabolic Encephalopathy in a Toddler with Gastroenteritis: A Case Report

Acetazolamide-Triggered Acute Flaccid Paralysis and Toxic-Metabolic Encephalopathy in a Toddler with Gastroenteritis: A Case Report

Artificial Intelligence Analysis of EEG Amplitude in Intensive Heart Care

Electroencephalography in neonatal epilepsies

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