Key result
Electroencephalography (EEG) signals combined with machine learning models (LDA and fine KNN) successfully identified adolescent schizophrenia, though specific accuracy metrics were not reported.
Why the study?
Schizophrenia diagnosis methods are criticized for subjectivity and poor replicability, and diagnosis is particularly challenging in adolescents due to delusions being mistaken for childhood fantasies and adult methods being misapplied.
Can EEG signals combined with machine learning accurately diagnose schizophrenia in adolescents aged 10-14?
Observational
Can EEG signals combined with machine learning accurately diagnose schizophrenia in adolescents aged 10-14?
EEG signals combined with machine learning models like LDA and KNN show promise for objectively diagnosing schizophrenia in adolescents.
Answers are grounded in this paper and cite their sources.
Requires prospective validation before clinical use in adolescent schizophrenia; leaves open accuracy, generalizability, and diagnostic utility.
Schizophrenia is a psychiatric disorder which is prevalent in individuals around the world, where diagnosis methods for this disorder are done via a combination of interview style questioning of the patient alongside a review of their medical record; but these methods have been largely criticised for being subjective between psychiatrists and largely unreplicable. Schizophrenia also occurs in adolescent individuals who have been said to be even more challenging to diagnose largely due to delusions being mistaken for childhood fantasies, and established methods for adult patients being applied to diagnose adolescents. This work investigates the use of electroencephalography (EEG) signals acquired from adolescent patients in the age range of 10–14 years, alongside signal processing methods and machine learning modelling towards the diagnosis of adolescent schizophrenia. The results from the machine learning modelling showed that the linear discriminant analysis (LDA) and fine K‐nearest neighbour (KNN) produced the best recognition results for models with easy and hard interpretability, respectively. Additionally, a computational method was applied towards contrasting the neuroanatomical activation patterns in the brain of the schizophrenic and normal adolescents, where it was seen that the neural activation patterns of the normal adolescents showed a greater consistency when compared with the schizophrenics.
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Ejay Nsugbe (2023) conducted an observational in Schizophrenia. Electroencephalography (EEG) signals and machine learning modelling vs. Healthy adolescents was evaluated on Diagnosis of adolescent schizophrenia. Electroencephalography (EEG) signals combined with machine learning models (LDA and fine KNN) successfully identified adolescent schizophrenia, though specific accuracy metrics were not reported.
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