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Electrocardiographic changes predate Parkinson's disease onset.

Publication ,  Journal Article
Akbilgic, O; Kamaleswaran, R; Mohammed, A; Ross, GW; Masaki, K; Petrovitch, H; Tanner, CM; Davis, RL; Goldman, SM
Published in: Scientific reports
July 2020

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.

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Published In

Scientific reports

DOI

EISSN

2045-2322

ISSN

2045-2322

Publication Date

July 2020

Volume

10

Issue

1

Start / End Page

11319

Related Subject Headings

  • Proof of Concept Study
  • Prodromal Symptoms
  • Pattern Recognition, Automated
  • Parkinson Disease
  • Middle Aged
  • Male
  • Machine Learning
  • Logistic Models
  • Humans
  • Heart Rate
 

Citation

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Akbilgic, O., Kamaleswaran, R., Mohammed, A., Ross, G. W., Masaki, K., Petrovitch, H., … Goldman, S. M. (2020). Electrocardiographic changes predate Parkinson's disease onset. Scientific Reports, 10(1), 11319. https://doi.org/10.1038/s41598-020-68241-6
Akbilgic, Oguz, Rishikesan Kamaleswaran, Akram Mohammed, G Webster Ross, Kamal Masaki, Helen Petrovitch, Caroline M. Tanner, Robert L. Davis, and Samuel M. Goldman. “Electrocardiographic changes predate Parkinson's disease onset.Scientific Reports 10, no. 1 (July 2020): 11319. https://doi.org/10.1038/s41598-020-68241-6.
Akbilgic O, Kamaleswaran R, Mohammed A, Ross GW, Masaki K, Petrovitch H, et al. Electrocardiographic changes predate Parkinson's disease onset. Scientific reports. 2020 Jul;10(1):11319.
Akbilgic, Oguz, et al. “Electrocardiographic changes predate Parkinson's disease onset.Scientific Reports, vol. 10, no. 1, July 2020, p. 11319. Epmc, doi:10.1038/s41598-020-68241-6.
Akbilgic O, Kamaleswaran R, Mohammed A, Ross GW, Masaki K, Petrovitch H, Tanner CM, Davis RL, Goldman SM. Electrocardiographic changes predate Parkinson's disease onset. Scientific reports. 2020 Jul;10(1):11319.

Published In

Scientific reports

DOI

EISSN

2045-2322

ISSN

2045-2322

Publication Date

July 2020

Volume

10

Issue

1

Start / End Page

11319

Related Subject Headings

  • Proof of Concept Study
  • Prodromal Symptoms
  • Pattern Recognition, Automated
  • Parkinson Disease
  • Middle Aged
  • Male
  • Machine Learning
  • Logistic Models
  • Humans
  • Heart Rate