PMID- 35687927
OWN - NLM
STAT- MEDLINE
DCOM- 20220713
LR  - 20220916
IS  - 1879-0534 (Electronic)
IS  - 0010-4825 (Linking)
VI  - 147
DP  - 2022 Aug
TI  - Minimal EEG channel selection for depression detection with connectivity features 
      during sleep.
PG  - 105690
LID - S0010-4825(22)00475-9 [pii]
LID - 10.1016/j.compbiomed.2022.105690 [doi]
AB  - BACKGROUND AND OBJECTIVES: Sleeping cortical electroencephalogram (EEG) has the 
      potential for depression detection, for different sleep structure and cortical 
      connection have been proved in depressed patients. However, the operation of 
      multi-channel sleep EEG recording is cumbersome and requires laboratory equipment 
      and professional sleep technician. Here, we focus on the depression detection 
      using minimal sleep EEG channels. METHODS: Sixteen channels of EEG data of 30 
      patients with depression and 30 age-matched normal controls were recorded during 
      sleep. Power spectral density of each single EEG channel was calculated, followed 
      by measuring the symbolic transfer entropy (STE) and weighed phase lag index 
      (WPLI) between EEG channel pairs in various frequency bands. Thereafter, these 
      features were evaluated by F-score in the two-way classification (depression vs. 
      control) of 30-s sleep EEG segments. Based on the F-score, entropy method was 
      introduced to calculate the weight which could further assess the classification 
      ability of various EEG channels or channel pairs. Finally, machine learning was 
      implemented to verify the important EEG channels or channel pairs in depression 
      diagnosis. RESULTS: The features characterizing the inter-hemispheric 
      connectivity in the posterior lobe, especially in the temporal lobe, showed high 
      classification capacity. The classification accuracy of using two and four EEG 
      channels in the temporal lobe were 97.96% and 99.61%, respectively. CONCLUSIONS: 
      This study showed the possibility of using only a few sleep EEG channels for 
      depression screening, which may greatly facilitate the diagnosis of depression 
      outside the hospital.
CI  - Copyright (c) 2022 Elsevier Ltd. All rights reserved.
FAU - Zhang, Yangting
AU  - Zhang Y
AD  - School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China.
FAU - Wang, Kejie
AU  - Wang K
AD  - School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China.
FAU - Wei, Yu
AU  - Wei Y
AD  - School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China.
FAU - Guo, Xinwen
AU  - Guo X
AD  - Psychology Department, Guangdong 999 Brain Hospital, Guangzhou, China.
FAU - Wen, Jinfeng
AU  - Wen J
AD  - Psychology Department, Guangdong 999 Brain Hospital, Guangzhou, China. Electronic 
      address: jfwen2000@126.com.
FAU - Luo, Yuxi
AU  - Luo Y
AD  - School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China; Key 
      Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, 
      Sun Yat-sen University, China. Electronic address: luoyuc@163.com.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20220606
PL  - United States
TA  - Comput Biol Med
JT  - Computers in biology and medicine
JID - 1250250
SB  - IM
MH  - *Depression/diagnosis
MH  - *Electroencephalography/methods
MH  - Entropy
MH  - Humans
MH  - Machine Learning
MH  - Sleep
OTO - NOTNLM
OT  - Depression detection
OT  - EEG channel Selection
OT  - Network connectivity
OT  - Sleep EEG
OT  - Temporal region
EDAT- 2022/06/11 06:00
MHDA- 2022/07/14 06:00
CRDT- 2022/06/10 18:13
PHST- 2022/04/22 00:00 [received]
PHST- 2022/05/29 00:00 [revised]
PHST- 2022/05/31 00:00 [accepted]
PHST- 2022/06/11 06:00 [pubmed]
PHST- 2022/07/14 06:00 [medline]
PHST- 2022/06/10 18:13 [entrez]
AID - S0010-4825(22)00475-9 [pii]
AID - 10.1016/j.compbiomed.2022.105690 [doi]
PST - ppublish
SO  - Comput Biol Med. 2022 Aug;147:105690. doi: 10.1016/j.compbiomed.2022.105690. Epub 
      2022 Jun 6.