PMID- 36525893
OWN - NLM
STAT- MEDLINE
DCOM- 20230124
LR  - 20230202
IS  - 1872-8952 (Electronic)
IS  - 1388-2457 (Linking)
VI  - 146
DP  - 2023 Feb
TI  - Discriminating between bipolar and major depressive disorder using a machine 
      learning approach and resting-state EEG data.
PG  - 30-39
LID - S1388-2457(22)00951-8 [pii]
LID - 10.1016/j.clinph.2022.11.014 [doi]
AB  - OBJECTIVE: Distinguishing major depressive disorder (MDD) from bipolar disorder 
      (BD) is a crucial clinical challenge as effective treatment is quite different 
      for each condition. In this study electroencephalography (EEG) was explored as an 
      objective biomarker for distinguishing MDD from BD using an efficient machine 
      learning algorithm (MLA) trained by a relatively large and balanced dataset. 
      METHODS: A 3 step MLA was applied: (1) a multi-step preprocessing method was used 
      to improve the quality of the EEG signal, (2) symbolic transfer entropy (STE), an 
      effective connectivity measure, was applied to the resultant EEG and (3) the MLA 
      used the extracted STE features to distinguish MDD (N = 71) from BD (N = 71) 
      subjects. RESULTS: 14 connectivity features were selected by the proposed 
      algorithm. Most of the selected features were related to the frontal, parietal, 
      and temporal lobe electrodes. The major involved regions were the Broca region in 
      the frontal lobe and the somatosensory association cortex in the parietal lobe. 
      These regions are near electrodes FC5 and CPz and are involved in processing 
      language and sensory information, respectively. The resulting classifier 
      delivered an evaluation accuracy of 88.5% and a test accuracy of 89.3%, using 80% 
      of the data for training and evaluation and the remaining 20% for testing, 
      respectively. CONCLUSIONS: The high evaluation and test accuracies of our 
      algorithm, derived from a large balanced training sample suggests that this 
      method may hold significant promise as a clinical tool. SIGNIFICANCE: The 
      proposed MLA may provide an inexpensive and readily available tool that 
      clinicians may use to enhance diagnostic accuracy and shorten time to effective 
      treatment.
CI  - Copyright (c) 2022 International Federation of Clinical Neurophysiology. Published 
      by Elsevier B.V. All rights reserved.
FAU - Ravan, M
AU  - Ravan M
AD  - Department of Electrical and Computer Engineering, New York Institute of 
      Technology, New York, NY, USA. Electronic address: mravan@nyit.edu.
FAU - Noroozi, A
AU  - Noroozi A
AD  - Department of Digital, Technologies, and Arts, Staffordshire University, 
      Staffordshire, England, UK.
FAU - Sanchez, M Margarette
AU  - Sanchez MM
AD  - Department of Biomedical Engineering, New York Institute of Technology, New York, 
      NY, USA.
FAU - Borden, L
AU  - Borden L
AD  - Department of Electrical and Computer Engineering, New York Institute of 
      Technology, New York, NY, USA.
FAU - Alam, N
AU  - Alam N
AD  - Department of Electrical and Computer Engineering, New York Institute of 
      Technology, New York, NY, USA.
FAU - Flor-Henry, P
AU  - Flor-Henry P
AD  - Alberta Hospital, Edmonton, AB, Canada(1).
FAU - Hasey, G
AU  - Hasey G
AD  - Department of Psychiatry and Behavioural Neurosciences, McMaster University, 
      Hamilton, ON, Canada.
LA  - eng
PT  - Journal Article
DEP - 20221207
PL  - Netherlands
TA  - Clin Neurophysiol
JT  - Clinical neurophysiology : official journal of the International Federation of 
      Clinical Neurophysiology
JID - 100883319
SB  - IM
MH  - Humans
MH  - *Depressive Disorder, Major/diagnosis/therapy
MH  - *Bipolar Disorder/diagnosis
MH  - Machine Learning
MH  - Frontal Lobe
MH  - Electroencephalography/methods
OTO - NOTNLM
OT  - Bipolar disorder
OT  - Diagnosis
OT  - Effective connectivity
OT  - Machine learning
OT  - Major depressive disorder
OT  - Resting state Electroencephalography (EEG)
OT  - Symbolic transfer entropy
EDAT- 2022/12/17 06:00
MHDA- 2023/01/25 06:00
CRDT- 2022/12/16 18:27
PHST- 2022/06/16 00:00 [received]
PHST- 2022/09/28 00:00 [revised]
PHST- 2022/11/27 00:00 [accepted]
PHST- 2022/12/17 06:00 [pubmed]
PHST- 2023/01/25 06:00 [medline]
PHST- 2022/12/16 18:27 [entrez]
AID - S1388-2457(22)00951-8 [pii]
AID - 10.1016/j.clinph.2022.11.014 [doi]
PST - ppublish
SO  - Clin Neurophysiol. 2023 Feb;146:30-39. doi: 10.1016/j.clinph.2022.11.014. Epub 
      2022 Dec 7.