Abstract
Autonomic nervous system involvement precedes the motor features of Parkinson’s disease (PD). Our goal was to develop a proof-of-concept model for identifying subjects at high risk of developing PD by analysis of cardiac electrical activity. We used standard 10-s electrocardiogram (ECG) recordings of 60 subjects from the Honolulu Asia Aging Study including 10 with prevalent PD, 25 with prodromal PD, and 25 controls who never developed PD. Various methods were implemented to extract features from ECGs including simple heart rate variability (HRV) metrics, commonly used signal processing methods, and a Probabilistic Symbolic Pattern Recognition (PSPR) method. Extracted features were analyzed via stepwise logistic regression to distinguish between prodromal cases and controls. Stepwise logistic regression selected four features from PSPR as predictors of PD. The final regression model built on the entire dataset provided an area under receiver operating characteristics curve (AUC) with 95% confidence interval of 0.90 [0.80, 0.99]. The five-fold cross-validation process produced an average AUC of 0.835 [0.831, 0.839]. We conclude that cardiac electrical activity provides important information about the likelihood of future PD not captured by classical HRV metrics. Machine learning applied to ECGs may help identify subjects at high risk of having prodromal PD.
Highlights
Autonomic nervous system involvement precedes the motor features of Parkinson’s disease (PD)
We hypothesized that early autonomic features of PD are detectable using machine learning, and tested this hypothesis using standard 10-s ECGs collected from participants in the prospective HonoluluAsia Aging Study (HAAS)
Identification of prodromal PD is an essential step as we progress toward implementing disease modifying therapeutic interventions
Summary
Autonomic nervous system involvement precedes the motor features of Parkinson’s disease (PD). A prospective study showed that low HRV determined from a 2-min ECG is associated with 2–threefold higher risk for PD5, the value of the ECG in predicting prodromal disease has not been established This may be because heart rate is a function of distance between two R peaks and it does not fully capture all the information reflected within electrocardiograms. A more sophisticated way of modeling electrical activity of the heart may help in identifying prodromal disease In this manuscript, we hypothesized that early autonomic features of PD are detectable using machine learning, and tested this hypothesis using standard 10-s ECGs collected from participants in the prospective HonoluluAsia Aging Study (HAAS)
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