V J Geraedts1, M Koch2, M F Contarino3, H A M Middelkoop4, H Wang2, J J van Hilten4, T H W Bäck2, M R Tannemaat4. 1. Leiden University Medical Centre, Department of Neurology, the Netherlands; Leiden University Medical Centre, Department of Epidemiology, the Netherlands. Electronic address: v.j.geraedts@lumc.nl. 2. Leiden Institute of Advanced Computer Science, the Netherlands. 3. Leiden University Medical Centre, Department of Neurology, the Netherlands; Haga Teaching Hospital, Department of Neurology, the Netherlands. 4. Leiden University Medical Centre, Department of Neurology, the Netherlands.
Abstract
OBJECTIVE: A downside of Deep Brain Stimulation (DBS) for Parkinson's Disease (PD) is that cognitive function may deteriorate postoperatively. Electroencephalography (EEG) was explored as biomarker of cognition using a Machine Learning (ML) pipeline. METHODS: A fully automated ML pipeline was applied to 112 PD patients, taking EEG time-series as input and predicted class-labels as output. The most extreme cognitive scores were selected for class differentiation, i.e. best vs. worst cognitive performance (n = 20 per group). 16,674 features were extracted per patient; feature-selection was performed using a Boruta algorithm. A random forest classifier was modelled; 10-fold cross-validation with Bayesian optimization was performed to ensure generalizability. The predicted class-probabilities of the entire cohort were compared to actual cognitive performance. RESULTS: Both groups were differentiated with a mean accuracy of 0.92; using only occipital peak frequency yielded an accuracy of 0.67. Class-probabilities and actual cognitive performance were negatively linearly correlated (β = -0.23 (95% confidence interval (-0.29, -0.18))). CONCLUSIONS: Particularly high accuracies were achieved using a compound of automatically extracted EEG biomarkers to classify PD patients according to cognition, rather than a single spectral EEG feature. SIGNIFICANCE: Automated EEG assessment may have utility for cognitive profiling of PD patients during the DBS screening.
OBJECTIVE: A downside of Deep Brain Stimulation (DBS) for Parkinson's Disease (PD) is that cognitive function may deteriorate postoperatively. Electroencephalography (EEG) was explored as biomarker of cognition using a Machine Learning (ML) pipeline. METHODS: A fully automated ML pipeline was applied to 112 PDpatients, taking EEG time-series as input and predicted class-labels as output. The most extreme cognitive scores were selected for class differentiation, i.e. best vs. worst cognitive performance (n = 20 per group). 16,674 features were extracted per patient; feature-selection was performed using a Boruta algorithm. A random forest classifier was modelled; 10-fold cross-validation with Bayesian optimization was performed to ensure generalizability. The predicted class-probabilities of the entire cohort were compared to actual cognitive performance. RESULTS: Both groups were differentiated with a mean accuracy of 0.92; using only occipital peak frequency yielded an accuracy of 0.67. Class-probabilities and actual cognitive performance were negatively linearly correlated (β = -0.23 (95% confidence interval (-0.29, -0.18))). CONCLUSIONS: Particularly high accuracies were achieved using a compound of automatically extracted EEG biomarkers to classify PDpatients according to cognition, rather than a single spectral EEG feature. SIGNIFICANCE: Automated EEG assessment may have utility for cognitive profiling of PDpatients during the DBS screening.
Authors: Dawoon Jung; Junggu Choi; Jeongjae Kim; Seoyoung Cho; Sanghoon Han Journal: Int J Environ Res Public Health Date: 2022-02-14 Impact factor: 3.390