Entropy: A Promising EEG Biomarker Dichotomizing Subjects With Opioid Use Disorder and Healthy Controls

Abstract

Electroencephalography (EEG) signals are known to be nonstationary and often multicomponential signals containing information about the condition of the brain. Since the EEG signal has complex, nonlinear, nonstationary, and highly random behaviour, numerous linear feature extraction methods related to the short-time windowing technique do not satisfy higher classification accuracy. Since biosignals are highly subjective, the symptoms may appear at random in the time scale and very small variations in EEG signals may depict a definite type of brain abnormality it is valuable and vital to extract and analyze the EEG signal parameters using computers. The challenge is to design and develop signal processing algorithms that extract this subtle information and use it for diagnosis, monitoring, and treatment of subjects suffering from psychiatric disorders. For this purpose, finite impulse response–based filtering process was employed rather than traditional time and frequency domain methods. Finite impulse response subbands were analyzed further to obtain feature vectors of different entropy markers and these features were fed into a classifier namely multilayer perceptron. The performances of the classifiers were finally compared considering overall classification accuracies, area under receiver operating characteristic curve scores. Our results underline the potential benefit of the introduced methodology is promising and is to be treated as a clinical interface in dichotomizing substance use disorders subjects and for other medical data analysis studies. The results also indicate that entropy estimators can distinguish normal and opioid use disorder subjects. EEG data and theta frequency band have distinctive capability for almost all types of entropies while nonextensive Tsallis entropy outperforms compared with other types of entropies.

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review

“…No distinction about gender is mentioned in this study. To classify the unipolar, bipolar and healthy subjects [57,115,116,175] have performed EEG experiments for 30 ,60, 89 and 134 subjects in which half of the participants are depressed and the others are healthy. Participants age varies from 20 to 50 years with no discrimination of gender.…”

“…For all subjects, the inclusion criteria are history of epilepsy, head injury, psychiatric disorders and effect of illegal drugs as shown in Table 14. The Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) and Back Depression Inventory (BDI-II) are the two major self-reported psychometric tests that are used in bipolar studies [115,116,175,174] for primary selection of the participants. Based on inclusion criteria and psychometric test, only those participants are considered for EEG study that have no psychotic disorder and their self-reported psychometric test score is above 14.…”

“…After the selection of the participants, a ten minutes EEG experiment is performed with open and close eye conditions for each subject. Placement and No psychotic DSM-IV,BDI -(BDI-II score>14 [172] Brain hospital patient DSM-5 -DSM-5 score [57] No mental disorder Watch Pics BDI(II)score 14-28 [115] No mental disorder BDI-II -BDI(II)score 14-28 [116] No neurological history -- [175] Heroin addiction aatients DSM-IV Stay relaxed and awake DSM-IV score [174] Age no more than 65 DSM-IV --…”

“…In this article, artificial neural network and deep learning-based approaches are considered for automated bipolar disorders diagnosis. Bandpass, notch filter and visual inspection [172] Bandpass filter at 0.15 to 70 Hz [57] Bandpass filter 40HZ [115] Total variation Filtering(TVP) [116] Notch filter at 50HZ [175] Independent component analysis [174] -…”

“…The researchers used different techniques and tools to manually or automatically remove the artifacts from the EEG signals as shown in Table : 16. The band pass, notch and ICA (Independent Component Analysis) are the important filters that are used in most of the bipolar disorder studies [171,172,57,116,175] to remove the noise, interference and physiological and non-physiological artifacts with 40 Hz to 70Hz frequency range. These filters remove the artifacts from EEG signals based on the frequency and amplitude level of the EEG signals (upper and lower level of the signals).…”