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Automated differentiation of acute encephalopathy with biphasic seizures and late reduced diffusion and prolonged febrile seizures in acute phase
by
Okanishi, Tohru
, Kuki, Ichiro
, Togawa, Masami
, Nakamura, Yuko
, Lee, Sooyoung
, Oguri, Masayoshi
, Hamano, Shin-Ichiro
, Ueta, Ikuya
, Maegaki, Yoshihiro
, Inaba, Yuji
, Takanashi, Jun-Ichi
, Onishi, Akinari
in
631/378
/ 692/617
/ Acute Disease
/ Acute encephalopathy
/ Brain
/ Brain Diseases - diagnosis
/ Brain Diseases - physiopathology
/ Child
/ Child, Preschool
/ Computed electroencephalography analysis
/ Diagnosis, Differential
/ Differential diagnosis
/ Drugs
/ EEG
/ Electrodes
/ Electroencephalography
/ Electroencephalography - methods
/ Encephalopathy
/ Febrile seizure
/ Female
/ Humanities and Social Sciences
/ Humans
/ Infant
/ Learning algorithms
/ Machine Learning
/ Magnetic resonance imaging
/ Male
/ Medical prognosis
/ multidisciplinary
/ Patients
/ Science
/ Science (multidisciplinary)
/ Seizures
/ Seizures - diagnosis
/ Seizures - physiopathology
/ Seizures, Febrile - diagnosis
/ Seizures, Febrile - physiopathology
/ Status epilepticus
2025
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Automated differentiation of acute encephalopathy with biphasic seizures and late reduced diffusion and prolonged febrile seizures in acute phase
by
Okanishi, Tohru
, Kuki, Ichiro
, Togawa, Masami
, Nakamura, Yuko
, Lee, Sooyoung
, Oguri, Masayoshi
, Hamano, Shin-Ichiro
, Ueta, Ikuya
, Maegaki, Yoshihiro
, Inaba, Yuji
, Takanashi, Jun-Ichi
, Onishi, Akinari
in
631/378
/ 692/617
/ Acute Disease
/ Acute encephalopathy
/ Brain
/ Brain Diseases - diagnosis
/ Brain Diseases - physiopathology
/ Child
/ Child, Preschool
/ Computed electroencephalography analysis
/ Diagnosis, Differential
/ Differential diagnosis
/ Drugs
/ EEG
/ Electrodes
/ Electroencephalography
/ Electroencephalography - methods
/ Encephalopathy
/ Febrile seizure
/ Female
/ Humanities and Social Sciences
/ Humans
/ Infant
/ Learning algorithms
/ Machine Learning
/ Magnetic resonance imaging
/ Male
/ Medical prognosis
/ multidisciplinary
/ Patients
/ Science
/ Science (multidisciplinary)
/ Seizures
/ Seizures - diagnosis
/ Seizures - physiopathology
/ Seizures, Febrile - diagnosis
/ Seizures, Febrile - physiopathology
/ Status epilepticus
2025
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Automated differentiation of acute encephalopathy with biphasic seizures and late reduced diffusion and prolonged febrile seizures in acute phase
by
Okanishi, Tohru
, Kuki, Ichiro
, Togawa, Masami
, Nakamura, Yuko
, Lee, Sooyoung
, Oguri, Masayoshi
, Hamano, Shin-Ichiro
, Ueta, Ikuya
, Maegaki, Yoshihiro
, Inaba, Yuji
, Takanashi, Jun-Ichi
, Onishi, Akinari
in
631/378
/ 692/617
/ Acute Disease
/ Acute encephalopathy
/ Brain
/ Brain Diseases - diagnosis
/ Brain Diseases - physiopathology
/ Child
/ Child, Preschool
/ Computed electroencephalography analysis
/ Diagnosis, Differential
/ Differential diagnosis
/ Drugs
/ EEG
/ Electrodes
/ Electroencephalography
/ Electroencephalography - methods
/ Encephalopathy
/ Febrile seizure
/ Female
/ Humanities and Social Sciences
/ Humans
/ Infant
/ Learning algorithms
/ Machine Learning
/ Magnetic resonance imaging
/ Male
/ Medical prognosis
/ multidisciplinary
/ Patients
/ Science
/ Science (multidisciplinary)
/ Seizures
/ Seizures - diagnosis
/ Seizures - physiopathology
/ Seizures, Febrile - diagnosis
/ Seizures, Febrile - physiopathology
/ Status epilepticus
2025
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Automated differentiation of acute encephalopathy with biphasic seizures and late reduced diffusion and prolonged febrile seizures in acute phase
Journal Article
Automated differentiation of acute encephalopathy with biphasic seizures and late reduced diffusion and prolonged febrile seizures in acute phase
2025
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Overview
Acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) is the most common subtype of acute encephalopathy in Japan and is difficult to differentiate from prolonged febrile seizures (PFSs). This study aimed to explore the capability of machine learning to differentiate AESD from PFSs on the basis of early electroencephalogram (EEG) analyses. Sixty one children with AESD (
n
= 20) or PFS (
n
= 41) were included. Digital EEG data with bipolar montage collected within 48 h (1–48 h) after seizure onset were analyzed using absolute power spectrum (APS) and phase lag index (PLI) values in each EEG frequency band. The APS values in the theta, alpha, beta, and gamma bands were lower for AESD than those for PFS. By contrast, the mean PLI values for all frequency bands were higher for AESD than for PFS. Machine learning analysis revealed that the APS value in the beta bands provided the highest differentiation accuracy and positive predictive value for AESD (68.8%). The mean APS values across all electrodes in the beta band may be a useful tool for differentiating between early-phase AESD and PFS. This study demonstrates the potential for early automated diagnosis of AESD and PFS using EEG analysis.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
/ 692/617
/ Brain
/ Brain Diseases - physiopathology
/ Child
/ Computed electroencephalography analysis
/ Drugs
/ EEG
/ Electroencephalography - methods
/ Female
/ Humanities and Social Sciences
/ Humans
/ Infant
/ Male
/ Patients
/ Science
/ Seizures
/ Seizures, Febrile - diagnosis
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