Bradycardia Prediction in Preterm Infants Using Nonparametric Kernel Density Estimation

Subhasish Das; Bahman Moraffah; Ayan Banerjee; Sandeep K.S. Gupta; Antonia Papandreou-Suppappola

doi:10.1109/IEEECONF44664.2019.9049007

Bradycardia Prediction in Preterm Infants Using Nonparametric Kernel Density Estimation

Subhasish Das, Bahman Moraffah, Ayan Banerjee, Sandeep K.S. Gupta, Antonia Papandreou-Suppappola

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

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 language	English (US)
Title of host publication	Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	1309-1313
Number of pages	5
ISBN (Electronic)	9781728143002
DOIs	https://doi.org/10.1109/IEEECONF44664.2019.9049007
State	Published - Nov 2019
Event	53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States Duration: Nov 3 2019 → Nov 6 2019

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2019-November
ISSN (Print)	1058-6393

Conference

Conference	53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
Country/Territory	United States
City	Pacific Grove
Period	11/3/19 → 11/6/19

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/IEEECONF44664.2019.9049007

Cite this

Das, S., Moraffah, B., Banerjee, A., Gupta, S. K. S., & Papandreou-Suppappola, A. (2019). Bradycardia Prediction in Preterm Infants Using Nonparametric Kernel Density Estimation. In M. B. Matthews (Ed.), Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 (pp. 1309-1313). Article 9049007 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2019-November). IEEE Computer Society. https://doi.org/10.1109/IEEECONF44664.2019.9049007

Bradycardia Prediction in Preterm Infants Using Nonparametric Kernel Density Estimation. / Das, Subhasish; Moraffah, Bahman; Banerjee, Ayan et al.
Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019. ed. / Michael B. Matthews. IEEE Computer Society, 2019. p. 1309-1313 9049007 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2019-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Das, S, Moraffah, B, Banerjee, A , Gupta, SKS & Papandreou-Suppappola, A 2019, Bradycardia Prediction in Preterm Infants Using Nonparametric Kernel Density Estimation. in MB Matthews (ed.), Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019., 9049007, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2019-November, IEEE Computer Society, pp. 1309-1313, 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019, Pacific Grove, United States, 11/3/19. https://doi.org/10.1109/IEEECONF44664.2019.9049007

Das S, Moraffah B, Banerjee A , Gupta SKS , Papandreou-Suppappola A. Bradycardia Prediction in Preterm Infants Using Nonparametric Kernel Density Estimation. In Matthews MB, editor, Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019. IEEE Computer Society. 2019. p. 1309-1313. 9049007. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/IEEECONF44664.2019.9049007

Das, Subhasish ; Moraffah, Bahman ; Banerjee, Ayan et al. / Bradycardia Prediction in Preterm Infants Using Nonparametric Kernel Density Estimation. Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019. editor / Michael B. Matthews. IEEE Computer Society, 2019. pp. 1309-1313 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

@inproceedings{75d43f6e149f4227ae0dbf6ab4fb2c27,

title = "Bradycardia Prediction in Preterm Infants Using Nonparametric Kernel Density Estimation",

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.",

author = "Subhasish Das and Bahman Moraffah and Ayan Banerjee and Gupta, {Sandeep K.S.} and Antonia Papandreou-Suppappola",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 ; Conference date: 03-11-2019 Through 06-11-2019",

year = "2019",

month = nov,

doi = "10.1109/IEEECONF44664.2019.9049007",

language = "English (US)",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

publisher = "IEEE Computer Society",

pages = "1309--1313",

editor = "Matthews, {Michael B.}",

booktitle = "Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019",

}

TY - GEN

T1 - Bradycardia Prediction in Preterm Infants Using Nonparametric Kernel Density Estimation

AU - Das, Subhasish

AU - Moraffah, Bahman

AU - Banerjee, Ayan

AU - Gupta, Sandeep K.S.

AU - Papandreou-Suppappola, Antonia

PY - 2019/11

Y1 - 2019/11

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85083326156&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85083326156&partnerID=8YFLogxK

U2 - 10.1109/IEEECONF44664.2019.9049007

DO - 10.1109/IEEECONF44664.2019.9049007

M3 - Conference contribution

AN - SCOPUS:85083326156

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 1309

EP - 1313

BT - Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019

A2 - Matthews, Michael B.

PB - IEEE Computer Society

T2 - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019

Y2 - 3 November 2019 through 6 November 2019

ER -

Bradycardia Prediction in Preterm Infants Using Nonparametric Kernel Density Estimation

Abstract

Publication series

Conference

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this