Bradycardia Prediction in Preterm Infants Using Nonparametric Kernel Density Estimation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a statistical method to predict the onset of bradycardia in preterm infants without any prior knowledge. To model information on the QRS complex R wave, we exploit nonparametric methods to estimate the density. Our proposed method takes advantage of the kernel density estimator in order to provide a statistical guarantee of 95% accuracy. We also demonstrate our results through simulations to support our proposed method using preterm infant electrocardiogram (ECG) signals from a database. We show that the method achieves a 5% false alarm rate in predicting the onset of upcoming bradycardia events.

Original languageEnglish (US)
Title of host publicationConference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1309-1313
Number of pages5
ISBN (Electronic)9781728143002
DOIs
StatePublished - Nov 2019
Event53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States
Duration: Nov 3 2019Nov 6 2019

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2019-November
ISSN (Print)1058-6393

Conference

Conference53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
CountryUnited States
CityPacific Grove
Period11/3/1911/6/19

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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