Key result
Automated multiple linear regression identifies abnormal EEGs in preterm infants with ~86% sensitivity.
Why the study?
Does an automatic machine learning algorithm accurately detect abnormal EEGs in preterm infants?
Observational (n=100)
No
Does an automatic machine learning algorithm accurately detect abnormal EEGs in preterm infants?
Effect estimate: AUC 0.82
An automated machine learning approach using multiple linear regression can accurately detect abnormal EEGs in preterm infants, potentially reducing physician workload and aiding early diagnosis.
May aid EEG screening in preterm infants; leaves open prospective validation before clinical adoption.
Many preterm infants suffer from neural disorders caused by early birth complications. The detection of children with neurological risk is an important challenge. The electroencephalogram is an important technique for establishing long-term neurological prognosis. Within this scope, the goal of this study is to propose an automatic detection of abnormal preterm babies’ electroencephalograms (EEG). A corpus of 316 neonatal EEG recordings of 100 infants born after less than 35 weeks of gestation were preprocessed and a time series of standard deviation was computed. This time series was thresholded to detect Inter Burst Intervals (IBI). Temporal features were extracted from bursts and IBI. Feature selection was carried out with classification in one step so as to select the best combination of features in terms of classification performance. Two classifiers were tested: Multiple Linear Regressions and Support Vector Machines (SVM). Performance was computed using cross validations. Methods were validated on a corpus of 100 infants with no serious brain damage. The Multiple Linear Regression method shows the best results with a sensitivity of 86.11% ± 10.01%, a specificity of 77.44% ± 7.62% and an AUC (Area under the ROC curves) of 0.82 ± 0.04. An accurate detection of abnormal EEG for preterm infants is feasible. This study is a first step towards an automatic analysis of the premature brain, making it possible to lighten the physician’s workload in the future.
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Schang et al. (2018) conducted an observational in Preterm infants (n=100). Automatic EEG analysis using Multiple Linear Regression vs. Visual analysis by neuropediatrician was evaluated on Detection of abnormal EEG (AUC) (AUC 0.82). An automatic detection method using Multiple Linear Regression on 11 EEG features identified abnormal EEGs in preterm infants with a sensitivity of 86.11% and an AUC of 0.82.
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