Ambulatory EEG Usefulness in Epilepsy Management

Hasan, Tasneem F.; Tatum, William O.

doi:10.1097/wnp.0000000000000601

Cited by 29 publications

(21 citation statements)

References 91 publications

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“…At present, scalp electroencephalogram is commonly used in clinical diagnosis, which is characterized by low cost, simple, noninvasive, and convenient diagnosis. However, the predictive value of different waveforms for aSAH-induced epilepsy is not clear, and whether it can guide the treatment of patients is still unclear [ 4 ]. In order to more accurately analyze the predictive value of EEG wave patterns based on deep learning for epilepsy secondary to inferior cavity hemorrhage and improve the feasibility of research results [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction Value of Epilepsy Secondary to Inferior Cavity Hemorrhage Based on Scalp EEG Wave Pattern in Deep Learning

Jiang

2022

Journal of Healthcare Engineering

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Objective. To search the predictive value of epilepsy secondary to acute subarachnoid hemorrhage (aSAH) based on EEG wave pattern in deep learning. Methods. A total of 156 cases of secondary epilepsy with lower cavity hemorrhage in our hospital were selected and divided into the late epilepsy group and the early epilepsy group according to seizure time, and the nonseizure group and the seizure group according to seizure condition. General data of patients were collected, the EEG types of each group were analyzed, and the disease recurrence rate, treatment effect, and symptom onset time were compared. Results. Rapid and slow and rapid blood flow velocity were the main abnormal manifestations of epilepsy secondary to inferior cavity hemorrhage, accounting for 33.3% and 18.6%, respectively. Compared with the seizure group, the proportion of type ii and type iii in the nonseizure group was higher, and the proportion of type ii and type iii in the early epilepsy group was higher than in the late epilepsy group ( P < 0.05). The diagnostic accuracy, missed diagnosis rate, misdiagnosis rate, specificity, and sensitivity of the EEG wave pattern were 94.9%, 3.2%, 1.9%, 91.7%, and 96.2%, respectively. Compared with the early epilepsy group, the recurrence rate of type iii and type iv in the late epilepsy group was higher ( P < 0.05). The effective rates of the attack group and the nonattack group were 72.7% and 97.0%, respectively. Compared with the attack group, the effective rate of the nonattack group was higher ( P < 0.05). The effective rates of the early epilepsy group and the late epilepsy group were 91.7% and 85.0%, respectively. Compared with the late epilepsy group, the effective rate of the early epilepsy group was higher ( P < 0.05). Compared with the early epilepsy group, the late epilepsy group had longer tonic-clonic seizures, atonic seizures, and absent seizures, and the difference between the groups was statistically significant ( P < 0.05). Conclusion. In aSAH secondary epilepsy disease prediction, based on indepth study of the scalp EEG wave type prediction, they play an important role, including aSAH high-risk secondary epilepsy wave types for V, III, and IV types, as well as early and late epilepsy associated with disease stage. Through the diagnosis method to predict the severity of disease, this builds a good foundation for clinical treatment. It is beneficial to improve the effective rate of treatment.

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Section: Introductionmentioning

confidence: 99%

Prediction Value of Epilepsy Secondary to Inferior Cavity Hemorrhage Based on Scalp EEG Wave Pattern in Deep Learning

Jiang

2022

Journal of Healthcare Engineering

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“… 91 Ambulatory EEGs have emerged as less expensive and less burdensome methods for the detection and quantification of seizures that may improve diagnosis and management of patients with epilepsy. 92 …”

Section: Discussionmentioning

confidence: 99%

Remote Patient Monitoring for Neuropsychiatric Disorders: A Scoping Review of Current Trends and Future Perspectives from Recent Publications and Upcoming Clinical Trials

Sakamaki

Furusawa

Hayashi

et al. 2022

Telemedicine and e-Health

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Introduction: Telemedicine and remote patient monitoring are rapidly growing fields. This scoping review provides an update on remote patient monitoring for neuropsychiatric disorders from recent publications and upcoming clinical trials. Methods: Publications (PubMed and ICHUSHI; published January 2010 to February 2021) and trials ( and Japanese registries; active or recruiting by March 2021) that assessed wearable devices for remote management and/or monitoring of patients with neuropsychiatric disorders were searched. The review focuses on disorders with ≥3 publications. Results: We identified 44 publications and 51 active or recruiting trials, mostly from 2019 or 2020. Research on digital devices was most common for Parkinson's disease (11 publications and 19 trials), primarily for monitoring motor symptoms and/or preventing falls. Other disorders (3–5 publications each) included epilepsy (electroencephalogram [EEG] and seizure prediction), sleep disorder (sleep outcomes and behavioral therapies), multiple sclerosis (physical activity and symptoms), depression (physical activity, symptoms, and behavioral therapies), and amyotrophic lateral sclerosis (symptoms). Very few studies focused on newly emerging technologies (e.g., in-ear EEG and portable oximeters), and few studies integrated remote symptom monitoring with telemedicine. Discussion: Currently, development of digital devices for daily symptom monitoring is focused on Parkinson's disease. For the diseases reviewed, studies mostly focused on physical activity rather than psychiatric or nonmotor symptoms. Although the validity and usefulness of many devices are established, models for implementing remote patient monitoring in telehealth settings have not been established. Conclusions: Verification of the clinical effectiveness of digital devices combined with telemedicine is needed to further advance remote patient care for neuropsychiatric disorders.

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“…Usefulness of ambulatory EEG devices in the management of epilepsy has been reported in several studies. These devices enable the detection of not only epileptiform activity but also helps to classify and quantify seizures and epilepsy syndromes [26][27][28]. In our cohort, nine patients were referred for VEM because the events recorded with Nelli highlighted the need for presurgical evaluation or further diagnostic evaluation.…”

Section: Discussionmentioning

confidence: 99%