Evaluating Domain Knowledge and Time Series Features for Automated Detection of Schizophrenia from EEG Signals

Hussain, Saqib; Pirzada, Nasrullah; Saba, Erum; Panhwar, Muhammad Aamir; Ahmed, Tanveer

doi:10.14569/ijacsa.2021.0121160

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…Previous EEG mapping studies have found increased beta power and decreased alpha and delta/theta power in deaddictive alcoholic patients, compared to normal controls. Since slow activity is considered inhibitory, fast beta activity excitatory and alpha activity an expression of normal brain function, the desynchronised low-voltage fast patterns can be translated as CNS hyperexcitation [30], [31].…”

Section: A Eeg and Substance Addiction 1) Alcoholmentioning

confidence: 99%

“…Opinions as to the cause of these EEG changes in alcoholics show that incoherencies in brain function, particularly in frontal parts of the brain, may be participating in the development of alcoholism [5], [30], [31]. This is documented by a variety of studies: beta activity has been linked to the combination of the two pre-morbid factors of childhood behaviour disorders and paternal alcoholism.…”

Section: A Eeg and Substance Addiction 1) Alcoholmentioning

confidence: 99%

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The Contribution of Numerical EEG Analysis for the Study and Understanding of Addictions with Substances

Mengad,

Dirkaoui,

Ertel

et al. 2023

IJACSA

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Computerised electroencephalography (EEG) is one of a wide variety of brain imaging techniques used in addiction medicine. It is a sensitive measure of the effects of addiction on the brain and has been shown to show changes in brain electrical activity during addiction. But, the clinical value of computerised EEG recording in addictions is not yet clearly established. However, several studies argue that this non-invasive technique has an undeniable contribution to the understanding, prediction, diagnosis and monitoring of addictions. The aim of this article is to assess, through a systematic review, the contribution and interest of computerised EEG in the study and understanding of substance abuse by describing the different electrical activities that underlie it across the main frequency ranges: delta, theta, alpha, beta and gamma. We have been conducting a systematic review according to the recommendations of Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) and the Cochrane Group. We included 25 studies with a total of 1897 cases of addiction and 1504 controls. The studies dealt with addictions related to 05 licit and illicit psychoactive substances (alcohol, nicotine, cannabis, heroin and cocaine). The group of addicted patients showed significantly different brain electrical characteristics from the group of controls in the different EEG rhythms, whether during acute substance intoxication, abuse, withdrawal, abstinence, relapse, progression or response to treatment. The majority of studies have used EEG for diagnostic, predictive, monitoring purposes and also to discover electro-physiological markers of certain addictions.

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Section: A Eeg and Substance Addiction 1) Alcoholmentioning

confidence: 99%

Section: A Eeg and Substance Addiction 1) Alcoholmentioning

confidence: 99%

The Contribution of Numerical EEG Analysis for the Study and Understanding of Addictions with Substances

Mengad,

Dirkaoui,

Ertel

et al. 2023

IJACSA

View full text Add to dashboard Cite

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Feature engineering of EEG applied to mental disorders: a systematic mapping study

García-Ponsoda,

García-Carrasco,

Teruel

et al. 2023

Appl Intell

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Around a third of the total population of Europe suffers from mental disorders. The use of electroencephalography (EEG) together with Machine Learning (ML) algorithms to diagnose mental disorders has recently been shown to be a prominent research area, as exposed by several reviews focused on the field. Nevertheless, previous to the application of ML algorithms, EEG data should be correctly preprocessed and prepared via Feature Engineering (FE). In fact, the choice of FE techniques can make the difference between an unusable ML model and a simple, effective model. In other words, it can be said that FE is crucial, especially when using complex, non-stationary data such as EEG. To this aim, in this paper we present a Systematic Mapping Study (SMS) focused on FE from EEG data used to identify mental disorders. Our SMS covers more than 900 papers, making it one of the most comprehensive to date, to the best of our knowledge. We gathered the mental disorder addressed, all the FE techniques used, and the Artificial Intelligence (AI) algorithm applied for classification from each paper. Our main contributions are: (i) we offer a starting point for new researchers on these topics, (ii) we extract the most used FE techniques to classify mental disorders, (iii) we show several graphical distributions of all used techniques, and (iv) we provide critical conclusions for detecting mental disorders. To provide a better overview of existing techniques, the FE process is divided into three parts: (i) signal transformation, (ii) feature extraction, and (iii) feature selection. Moreover, we classify and analyze the distribution of existing papers according to the mental disorder they treat, the FE processes used, and the ML techniques applied. As a result, we provide a valuable reference for the scientific community to identify which techniques have been proven and tested and where the gaps are located in the current state of the art. Graphical Abstract

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Evaluating Domain Knowledge and Time Series Features for Automated Detection of Schizophrenia from EEG Signals

Cited by 2 publications

References 25 publications

The Contribution of Numerical EEG Analysis for the Study and Understanding of Addictions with Substances

The Contribution of Numerical EEG Analysis for the Study and Understanding of Addictions with Substances

Feature engineering of EEG applied to mental disorders: a systematic mapping study

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