Key result
Logistic Regression achieved 100% accuracy in differentiating between autistic and normal children during a colour stimulus test, while Linear Discriminant Analysis achieved 90% accuracy in the baseline test.
Why the study?
The study was conducted to apply a machine learning approach to differentiate between autistic and normal children and evaluate classifier performance in detecting autism spectrum disorder using heart rate variability features.
Can machine learning classifiers using HRV features accurately differentiate between children with ASD and normal children?
Cross-Sectional (n=10)
Can machine learning classifiers using HRV features accurately differentiate between children with ASD and normal children?
Answers are grounded in this paper and cite their sources.
Machine learning analysis of heart rate variability may serve as a potential alternative method for early diagnosis of Autism Spectrum Disorder.
Autism spectrum disorder (ASD) is a developmental disability that involves persistent challenges in social interaction, communication and behaviour. The purpose of this study is to apply a machine learning approach to differentiate between autistic and normal children and to evaluate the performance of different classifiers in the detection of autism disorder. Heart Rate Variability (HRV) analysis is one of the strategies used for ASD detection by assessing the autonomic nervous system (ANS), which serves as a biomarker for the autism phenotype. HRV can be derived from the photoplethysmogram (PPG). Logistic Regression, Linear Discriminant Analysis and a Cubic Support Vector Machine (SVM) were chosen to evaluate the performance of HRV features in differentiating between normal and autistic children. Three different combinations of features were selected out of 19 features in total. From the results, Logistic Regression was the best classifier to differentiate between autistic and normal children in a colour stimulus test with 100% accuracy, while Linear Discriminant Analysis was best suited in the baseline test with 90% accuracy. In conclusion, the machine learning approach could be an alternative method of making an early diagnosis of ASD in the near future.
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Aimie-Salleh et al. (2022) conducted a cross-sectional in Autism Spectrum Disorder (n=10). Machine learning classification using HRV features derived from PPG vs. Typical Development (TD) children was evaluated on Classification accuracy of ASD vs TD. Logistic Regression achieved 100% accuracy in differentiating between autistic and normal children during a colour stimulus test, while Linear Discriminant Analysis achieved 90% accuracy in the baseline test.
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