Naslov
EEG characteristics in patients with affective disorder

Autori
Mulc, Damir ; Vukojević, Jakša ; Kinder, Ivana ; Friganović, Krešimir ; Vidović, Domagoj ; Brečić, Petrana ; Cifrek, Mario

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
European Neuropsychopharmacology, Vol. 40, Suppl. 1 (2020) / - Amsterdam : Elsevier, 2020, S226-S226

Skup
33rd European College of Neuropsychopharmacology Congress ( ECNP)

Mjesto i datum
Beč, Austrija; online, 12.09.2020. - 15.09.2020

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
affective disorder ; EEG ; depressive disorder ; bipolar disorder ; machine learning ; wave entropy

Sažetak
The aim of the study was to identify specific characteristics of EEG recordings that could be used as potential biomarkers for major depressive disorder or bipolar disorder. For that purpose we have included 30 healthy participants whose EEG recordings were compared with 30 patients diagnosed with major depressive disorder and 10 patients diagnosed with bipolar disorder, both according to the ICD-10 criteria and all of which did a native EEG recording on the day of the admission in the hospital. Our findings confirm some of the previous studies, validating the importance of some characteristics of theta and gamma waves in depression detection, but also disputed some earlier studies regarding relative wavelet energy. Furthermore, our research suggests that wave entropy in beta band wave on the Cz position is a good independent predictor of depression as well as wave entropy of delta band wave on position F7, which also had high predictive accuracy. We did not find any reliable predictors for bipolar disorder. Alongside these findings, we employed machine learning methodology in correlating clinical characteristics and EEG findings.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Kliničke medicinske znanosti, Kognitivna znanost (prirodne, tehničke, biomedicina i zdravstvo, društvene i humanističke znanosti)

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