PMID- 37522045
OWN - NLM
STAT- Publisher
LR  - 20230928
IS  - 1871-4080 (Print)
IS  - 1871-4099 (Electronic)
IS  - 1871-4080 (Linking)
VI  - 17
IP  - 4
DP  - 2023 Aug
TI  - Differences in functional network between focal onset nonconvulsive status 
      epilepticus and toxic metabolic encephalopathy: application to machine learning 
      models for differential diagnosis.
PG  - 845-853
LID - 10.1007/s11571-022-09877-0 [doi]
AB  - We aimed to compare network properties between focal-onset nonconvulsive status 
      epilepticus (NCSE) and toxic/metabolic encephalopathy (TME) during periods of 
      periodic discharge using graph theoretical analysis, and to evaluate the 
      applicability of graph measures as markers for the differential diagnosis between 
      focal-onset NCSE and TME, using machine learning algorithms. 
      Electroencephalography (EEG) data from 50 focal-onset NCSE and 44 TMEs were 
      analyzed. Epochs with nonictal periodic discharges were selected, and the 
      coherence in each frequency band was analyzed. Graph theoretical analysis was 
      performed to compare brain network properties between the groups. Eight different 
      traditional machine learning methods were implemented to evaluate the utility of 
      graph theoretical measures as input features to discriminate between the two 
      conditions. The average degree (in delta, alpha, beta, and gamma bands), strength 
      (in delta band), global efficiency (in delta and alpha bands), local efficiency 
      (in delta band), clustering coefficient (in delta band), and transitivity (in 
      delta band) were higher in TME than in NCSE. TME showed lower modularity (in 
      delta band) and assortativity (in alpha, beta, and gamma bands) than NCSE. 
      Machine learning algorithms based on EEG global graph measures classified NCSE 
      and TME with high accuracy, and gradient boosting was the most accurate 
      classification model with an area under the receiver operating characteristics 
      curve of 0.904. Our findings on differences in network properties may provide 
      novel insights that graph measures reflecting the network properties could be 
      quantitative markers for the differential diagnosis between focal-onset NCSE and 
      TME.
CI  - (c) The Author(s) 2022.
FAU - Kim, Seong Hwan
AU  - Kim SH
AUID- ORCID: 0000-0002-0778-1570
AD  - Department of Neurology, Korea University Anam Hospital, Korea University College 
      of Medicine, Seoul, Republic of Korea. GRID: grid.222754.4. ISNI: 0000 0001 0840 
      2678
FAU - Kim, Hayom
AU  - Kim H
AUID- ORCID: 0000-0002-9991-3664
AD  - Department of Neurology, Korea University Anam Hospital, Korea University College 
      of Medicine, Seoul, Republic of Korea. GRID: grid.222754.4. ISNI: 0000 0001 0840 
      2678
FAU - Kim, Jung Bin
AU  - Kim JB
AUID- ORCID: 0000-0002-8013-9349
AD  - Department of Neurology, Korea University Anam Hospital, Korea University College 
      of Medicine, Seoul, Republic of Korea. GRID: grid.222754.4. ISNI: 0000 0001 0840 
      2678
LA  - eng
PT  - Journal Article
DEP - 20220903
PL  - Netherlands
TA  - Cogn Neurodyn
JT  - Cognitive neurodynamics
JID - 101306907
PMC - PMC10374505
OTO - NOTNLM
OT  - Differential diagnosis
OT  - Machine learning
OT  - Network
OT  - Nonconvulsive status epilepticus
OT  - Toxic metabolic encephalopathy
COIS- Conflict of interestThe authors declare no conflict of interest.
EDAT- 2023/07/31 06:42
MHDA- 2023/07/31 06:42
PMCR- 2022/09/03
CRDT- 2023/07/31 05:16
PHST- 2022/01/07 00:00 [received]
PHST- 2022/05/19 00:00 [revised]
PHST- 2022/08/19 00:00 [accepted]
PHST- 2023/07/31 06:42 [pubmed]
PHST- 2023/07/31 06:42 [medline]
PHST- 2023/07/31 05:16 [entrez]
PHST- 2022/09/03 00:00 [pmc-release]
AID - 9877 [pii]
AID - 10.1007/s11571-022-09877-0 [doi]
PST - ppublish
SO  - Cogn Neurodyn. 2023 Aug;17(4):845-853. doi: 10.1007/s11571-022-09877-0. Epub 2022 
      Sep 3.