Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals.
Comput Biol Med
; 140: 105120, 2021 Dec 04.
Article
em En
| MEDLINE
| ID: mdl-34896884
ABSTRACT
BACKGROUND:
The most prevalent neuropsychiatric disorder among children is attention deficit hyperactivity disorder (ADHD). ADHD presents with a high prevalence of comorbid disorders such as conduct disorder (CD). The lack of definitive confirmatory diagnostic tests for ADHD and CD make diagnosis challenging. The distinction between ADHD, ADHD + CD and CD is important as the course and treatment are different. Electrocardiography (ECG) signals may become altered in behavioral disorders due to brain-heart autonomic interactions. We have developed a software tool to categorize ADHD, ADHD + CD and CD automatically on ECG signals.METHOD:
ECG signals from participants were decomposed using empirical wavelet transform into various modes, from which entropy features were extracted. Robust ten-fold cross-validation with adaptive synthetic sampling (ADASYN) and z-score normalization were performed at each fold. Analysis of variance (ANOVA) technique was employed to determine the variability within the three classes, and obtained the most discriminatory features. Highly significant entropy features were then fed to classifiers.RESULTS:
Our model yielded the best classification results with the bagged tree classifier 87.19%, 87.71% and 86.29% for accuracy, sensitivity and specificity, respectively.CONCLUSION:
The proposed expert system can potentially assist mental health professionals in the stratification of the three classes, for appropriate intervention using accessible ECG signals.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Comput Biol Med
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Singapura