Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals

2022 ◽  
Vol 140 ◽  
pp. 105120
Joel.E.W. Koh ◽  
Chui Ping Ooi ◽  
Nikki SJ. Lim-Ashworth ◽  
Jahmunah Vicnesh ◽  
Hui Tian Tor ◽  
2014 ◽  
Vol 55 (4) ◽  
pp. 328-336 ◽  
Holly E. Erskine ◽  
Alize J. Ferrari ◽  
Guilherme V. Polanczyk ◽  
Terrie E. Moffitt ◽  
Christopher J. L. Murray ◽  

2004 ◽  
Vol 34 (6) ◽  
pp. 1113-1127 ◽  
S. L. BUKA ◽  

Background. To examine the familial associations of overt and covert antisocial behavior within the diagnosis of conduct disorder (CD) in families ascertained by referred children with attention-deficit hyperactivity disorder (ADHD), and to test if these familial associations differed between male and female probands.Method. Subjects were clinically-referred male and female ADHD children (n=273) and their first-degree biological relatives (n=807). Scores for overt and covert conduct problems were calculated by summing the DSM-III-R conduct disorder symptoms, as derived from structured diagnostic interviews. Familial aggregation analyses were conducted with multivariate regression modeling methodology.Results. Proband overt scores significantly predicted the overt scores of their relatives, and proband covert scores significantly predicted the covert scores of their relatives. There was no evidence of covert symptom scores predicting overt scores or vice versa. There was some evidence that the aggregation of covert symptoms was stronger in the families of female probands.Conclusions. These results provide preliminary evidence that overt and covert conduct disorder symptoms are independently transmitted through families and may represent distinct familial syndromes.

2021 ◽  
Esra Demirci ◽  
Mustafa Yasin Esas ◽  
Çiğdem Gülüzar Altıntop ◽  
Neslihan Taştepe ◽  
Fatma Latifoğlu

Abstract Although Attention Deficit Hyperactivity Disorder (ADHD) is a common childhood disease, objective diagnostic methods are insufficient still. Current diagnostic methods include the subjective influence of the evaluator. In this context, in our study, we aimed to minimize the subjective effect of the evaluator with the objective diagnosis support system for ADHD.In our study, a visual stimulus follow-up test developed by us was applied to the patient with ADHD and healthy individuals, and electrooculogram (EOG) signals were recorded simultaneously. With the features extracted from EOG signals, Artificial Neural Networks (ANN) were used for the classification study of patients and healthy individuals, and it was determined that the classification of ADHD and healthy group could be distinguished by 81.76% performance. Thus, the outcomes that will contribute to the objective diagnosis of ADHD have been presented. The results are remarkable and important findings have been obtained that will contribute to the literature.

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