A benchmark for prediction of psychiatric multimorbidity from resting EEG data in a large pediatric sample

Langer, Nicolas; Płomecka, Martyna Beata; Tröndle, Marius; Negi, Anuja; Popov, Tzvetan; Milham, Michael P.; Haufe, Stefan

doi:10.1016/j.neuroimage.2022.119348

Cited by 7 publications

(1 citation statement)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reduced feature was then combined with the optimal HF of value

to get the concatenated feature shown in Equation (16). These features were then considered to train and validate the considered disease detection scheme [ 39 , 40 , 41 , 42 , 43 ].

…”

Section: Methodsmentioning

confidence: 99%

Framework to Detect Schizophrenia in Brain MRI Slices with Mayfly Algorithm-Selected Deep and Handcrafted Features

Manic

Rajinikanth

Al-Bimani

et al. 2022

Sensors

View full text Add to dashboard Cite

Brain abnormality causes severe human problems, and thorough screening is necessary to identify the disease. In clinics, bio-image-supported brain abnormality screening is employed mainly because of its investigative accuracy compared with bio-signal (EEG)-based practice. This research aims to develop a reliable disease screening framework for the automatic identification of schizophrenia (SCZ) conditions from brain MRI slices. This scheme consists following phases: (i) MRI slices collection and pre-processing, (ii) implementation of VGG16 to extract deep features (DF), (iii) collection of handcrafted features (HF), (iv) mayfly algorithm-supported optimal feature selection, (v) serial feature concatenation, and (vi) binary classifier execution and validation. The performance of the proposed scheme was independently tested with DF, HF, and concatenated features (DF+HF), and the achieved outcome of this study verifies that the schizophrenia screening accuracy with DF+HF is superior compared with other methods. During this work, 40 patients’ brain MRI images (20 controlled and 20 SCZ class) were considered for the investigation, and the following accuracies were achieved: DF provided >91%, HF obtained >85%, and DF+HF achieved >95%. Therefore, this framework is clinically significant, and in the future, it can be used to inspect actual patients’ brain MRI slices.

show abstract

“…The reduced feature was then combined with the optimal HF of value

to get the concatenated feature shown in Equation (16). These features were then considered to train and validate the considered disease detection scheme [ 39 , 40 , 41 , 42 , 43 ].

…”

Section: Methodsmentioning

confidence: 99%