PMID- 36085796
OWN - NLM
STAT- MEDLINE
DCOM- 20220913
LR  - 20221019
IS  - 2694-0604 (Electronic)
IS  - 2375-7477 (Linking)
VI  - 2022
DP  - 2022 Jul
TI  - A Machine Learning Algorithm to Discriminating Between Bipolar and Major 
      Depressive Disorders Based on Resting EEG Data.
PG  - 2635-2638
LID - 10.1109/EMBC48229.2022.9871453 [doi]
AB  - Distinguishing major depressive disorder (MDD) from bipolar disorder (BD) is a 
      crucial clinical challenge due to the lack of known biomarkers. Conventional 
      methods of diagnosis rest exclusively on symptomatic presentation, and personal 
      and family history. As a result, BD-depressed episode (BD-DE) is often 
      misdiagnosed as MDD, and inappropriate therapy is given. Electroencephalography 
      (EEG) has been widely studied as a potential source of biomarkers to 
      differentiate these disorders. Previous attempts using machine learning (ML) 
      methods have delivered insufficient sensitivity and specificity for clinical use, 
      likely as a consequence of the small training set size, and inadequate ML 
      methodology. We hope to overcome these limitations by employing a training 
      dataset of resting-state EEG from 71 MDD and 71 BD patients. We introduce a 
      robust 3 steps ML technique: 1) a multi-step preprocessing method is used to 
      improve the quality of the EEG signal 2) symbolic transfer entropy (STE), which 
      is an effective connectivity measure, is applied to the resultant EEG signals 3) 
      the ML algorithm uses the extracted STE features to distinguish MDD from BD 
      patients. Clinical Relevance--- The accuracy of our algorithm, derived from a 
      large sample of patients, suggests that this method may hold significant promise 
      as a clinical tool. The proposed method delivered total accuracy, sensitivity, 
      and specificity of 84.9%, 83.4%, and 87.1%, respectively.
FAU - Margarette Sanchez, M
AU  - Margarette Sanchez M
FAU - Borden, L
AU  - Borden L
FAU - Alam, N
AU  - Alam N
FAU - Noroozi, A
AU  - Noroozi A
FAU - Ravan, M
AU  - Ravan M
FAU - Flor-Henry, P
AU  - Flor-Henry P
FAU - Hasey, G
AU  - Hasey G
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - Annu Int Conf IEEE Eng Med Biol Soc
JT  - Annual International Conference of the IEEE Engineering in Medicine and Biology 
      Society. IEEE Engineering in Medicine and Biology Society. Annual International 
      Conference
JID - 101763872
SB  - IM
MH  - Algorithms
MH  - *Bipolar Disorder/diagnosis
MH  - *Depressive Disorder, Major/diagnosis
MH  - Electroencephalography
MH  - Humans
MH  - Machine Learning
EDAT- 2022/09/11 06:00
MHDA- 2022/09/14 06:00
CRDT- 2022/09/10 01:03
PHST- 2022/09/10 01:03 [entrez]
PHST- 2022/09/11 06:00 [pubmed]
PHST- 2022/09/14 06:00 [medline]
AID - 10.1109/EMBC48229.2022.9871453 [doi]
PST - ppublish
SO  - Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:2635-2638. doi: 
      10.1109/EMBC48229.2022.9871453.