PMID- 33656984
OWN - NLM
STAT- MEDLINE
DCOM- 20210625
LR  - 20210625
IS  - 1558-2531 (Electronic)
IS  - 0018-9294 (Linking)
VI  - 68
IP  - 4
DP  - 2021 Apr
TI  - Advanced Signal Processing Methods for Characterization of Schizophrenia.
PG  - 1123-1130
LID - 10.1109/TBME.2020.3011842 [doi]
AB  - OBJECTIVE: Schizophrenia is a severe mental disorder associated with 
      nerobiological deficits. Auditory oddball P300 have been found to be one of the 
      most consistent markers of schizophrenia. The goal of this study is to find 
      quantitative features that can objectively distinguish patients with 
      schizophrenia (SCZs) from healthy controls (HCs) based on their recorded auditory 
      odd-ball P300 electroencephalogram (EEG) data. METHODS: Using EEG dataset, we 
      develop a machine learning (ML) algorithm to distinguish 57 SCZs from 66 HCs. The 
      proposed ML algorithm has three steps. In the first step, a brain source 
      localization (BSL) procedure using the linearly constrained minimum variance 
      (LCMV) beamforming approach is employed on EEG signals to extract source 
      waveforms from 30 specified brain regions. In the second step, a method for 
      estimating effective connectivity, referred to as symbolic transfer entropy 
      (STE), is applied to the source waveforms. In the third step the ML algorithm is 
      applied to the STE connectivity matrix to determine whether a set of features can 
      be found that successfully discriminate SCZ from HC. RESULTS: The findings 
      revealed that the SCZs have significantly higher effective connectivity compared 
      to HCs and the selected STE features could achieve an accuracy of 92.68%, with a 
      sensitivity of 92.98% and specificity of 92.42%. CONCLUSION: The findings imply 
      that the extracted features are from the regions that are mainly affected by SCZ 
      and can be used to distinguish SCZs from HCs. SIGNIFICANCE: The proposed ML 
      algorithm may prove to be a promising tool for the clinical diagnosis of 
      schizophrenia.
FAU - Masychev, Kirill
AU  - Masychev K
FAU - Ciprian, Claudio
AU  - Ciprian C
FAU - Ravan, Maryam
AU  - Ravan M
FAU - Reilly, James P
AU  - Reilly JP
FAU - MacCrimmon, Duncan
AU  - MacCrimmon D
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20210318
PL  - United States
TA  - IEEE Trans Biomed Eng
JT  - IEEE transactions on bio-medical engineering
JID - 0012737
SB  - IM
MH  - Brain
MH  - Electroencephalography
MH  - Humans
MH  - Machine Learning
MH  - *Schizophrenia/diagnosis
MH  - Signal Processing, Computer-Assisted
EDAT- 2021/03/04 06:00
MHDA- 2021/06/29 06:00
CRDT- 2021/03/03 17:09
PHST- 2021/03/04 06:00 [pubmed]
PHST- 2021/06/29 06:00 [medline]
PHST- 2021/03/03 17:09 [entrez]
AID - 10.1109/TBME.2020.3011842 [doi]
PST - ppublish
SO  - IEEE Trans Biomed Eng. 2021 Apr;68(4):1123-1130. doi: 10.1109/TBME.2020.3011842. 
      Epub 2021 Mar 18.