Clinical accuracy QRS detector with automatic parameter adjustment in an autonomous, real-time physiologic monitor

Samuel C. Pinto; Christopher L. Felton; Lukas Smital; Barry Kent Gilbert; David R. Holmes III; Clifton R Haider

doi:10.1109/GlobalSIP.2017.8309112

Clinical accuracy QRS detector with automatic parameter adjustment in an autonomous, real-time physiologic monitor

Samuel C. Pinto, Christopher L. Felton, Lukas Smital, Barry Kent Gilbert, David R. Holmes III, Clifton R Haider

Physiology & Biomedical Engineering

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

Abstract

This paper presents a computationally and temporal data-compact QRS complex detection algorithm useful in embedded real-time electrocardiogram (ECG) waveform analysis. The aim of the compact algorithms is to provide high sensitivity and specificity, i.e. diagnostically useful QRS waveform detection, in a continuous ambulatory monitor setting. The proposed detector uses a multi-level approach: QRS highlighting by means of a Truncated Discrete Time Stockwell Transform (TDTST), peak discrimination, and a trained Neural Network to reduce the number of false positive QRS detections. An optimization method is presented that automatically adjust the detector's parameters to minimize the computational cost. Results demonstrate that the compact TDTST algorithm exhibits high QRS detection accuracy, an error rate of 0.31%, and remains applicable to real-time embedded physiologic ambulatory monitors.

Original language	English (US)
Title of host publication	2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1005-1009
Number of pages	5
Volume	2018-January
ISBN (Electronic)	9781509059904
DOIs	https://doi.org/10.1109/GlobalSIP.2017.8309112
State	Published - Mar 7 2018
Event	5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Montreal, Canada Duration: Nov 14 2017 → Nov 16 2017

Other

Other	5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017
Country	Canada
City	Montreal
Period	11/14/17 → 11/16/17

Keywords

Compact algorithm
Embedded
Physiologic monitoring
QRS detection
Realtime
Stockwell transform

ASJC Scopus subject areas

Information Systems
Signal Processing

Access to Document

10.1109/GlobalSIP.2017.8309112

Fingerprint Dive into the research topics of 'Clinical accuracy QRS detector with automatic parameter adjustment in an autonomous, real-time physiologic monitor'. Together they form a unique fingerprint.

Cite this

Pinto, S. C., Felton, C. L., Smital, L., Gilbert, B. K., Holmes III, D. R., & Haider, C. R. (2018). Clinical accuracy QRS detector with automatic parameter adjustment in an autonomous, real-time physiologic monitor. In 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings (Vol. 2018-January, pp. 1005-1009). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2017.8309112

Clinical accuracy QRS detector with automatic parameter adjustment in an autonomous, real-time physiologic monitor. / Pinto, Samuel C.; Felton, Christopher L.; Smital, Lukas; Gilbert, Barry Kent ; Holmes III, David R.; Haider, Clifton R.

2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1005-1009.

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

Pinto, SC, Felton, CL, Smital, L, Gilbert, BK , Holmes III, DR & Haider, CR 2018, Clinical accuracy QRS detector with automatic parameter adjustment in an autonomous, real-time physiologic monitor. in 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1005-1009, 5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017, Montreal, Canada, 11/14/17. https://doi.org/10.1109/GlobalSIP.2017.8309112

Pinto SC, Felton CL, Smital L, Gilbert BK , Holmes III DR , Haider CR. Clinical accuracy QRS detector with automatic parameter adjustment in an autonomous, real-time physiologic monitor. In 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1005-1009 https://doi.org/10.1109/GlobalSIP.2017.8309112

Pinto, Samuel C. ; Felton, Christopher L. ; Smital, Lukas ; Gilbert, Barry Kent ; Holmes III, David R. ; Haider, Clifton R. / Clinical accuracy QRS detector with automatic parameter adjustment in an autonomous, real-time physiologic monitor. 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1005-1009

@inproceedings{2e5fad1e271a4a08ae9afa62ad959307,

title = "Clinical accuracy QRS detector with automatic parameter adjustment in an autonomous, real-time physiologic monitor",

abstract = "This paper presents a computationally and temporal data-compact QRS complex detection algorithm useful in embedded real-time electrocardiogram (ECG) waveform analysis. The aim of the compact algorithms is to provide high sensitivity and specificity, i.e. diagnostically useful QRS waveform detection, in a continuous ambulatory monitor setting. The proposed detector uses a multi-level approach: QRS highlighting by means of a Truncated Discrete Time Stockwell Transform (TDTST), peak discrimination, and a trained Neural Network to reduce the number of false positive QRS detections. An optimization method is presented that automatically adjust the detector's parameters to minimize the computational cost. Results demonstrate that the compact TDTST algorithm exhibits high QRS detection accuracy, an error rate of 0.31%, and remains applicable to real-time embedded physiologic ambulatory monitors.",

keywords = "Compact algorithm, Embedded, Physiologic monitoring, QRS detection, Realtime, Stockwell transform",

author = "Pinto, {Samuel C.} and Felton, {Christopher L.} and Lukas Smital and Gilbert, {Barry Kent} and {Holmes III}, {David R.} and Haider, {Clifton R}",

year = "2018",

month = mar,

day = "7",

doi = "10.1109/GlobalSIP.2017.8309112",

language = "English (US)",

volume = "2018-January",

pages = "1005--1009",

booktitle = "2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 ; Conference date: 14-11-2017 Through 16-11-2017",

}

TY - GEN

T1 - Clinical accuracy QRS detector with automatic parameter adjustment in an autonomous, real-time physiologic monitor

AU - Pinto, Samuel C.

AU - Felton, Christopher L.

AU - Smital, Lukas

AU - Gilbert, Barry Kent

AU - Holmes III, David R.

AU - Haider, Clifton R

PY - 2018/3/7

Y1 - 2018/3/7

N2 - This paper presents a computationally and temporal data-compact QRS complex detection algorithm useful in embedded real-time electrocardiogram (ECG) waveform analysis. The aim of the compact algorithms is to provide high sensitivity and specificity, i.e. diagnostically useful QRS waveform detection, in a continuous ambulatory monitor setting. The proposed detector uses a multi-level approach: QRS highlighting by means of a Truncated Discrete Time Stockwell Transform (TDTST), peak discrimination, and a trained Neural Network to reduce the number of false positive QRS detections. An optimization method is presented that automatically adjust the detector's parameters to minimize the computational cost. Results demonstrate that the compact TDTST algorithm exhibits high QRS detection accuracy, an error rate of 0.31%, and remains applicable to real-time embedded physiologic ambulatory monitors.

AB - This paper presents a computationally and temporal data-compact QRS complex detection algorithm useful in embedded real-time electrocardiogram (ECG) waveform analysis. The aim of the compact algorithms is to provide high sensitivity and specificity, i.e. diagnostically useful QRS waveform detection, in a continuous ambulatory monitor setting. The proposed detector uses a multi-level approach: QRS highlighting by means of a Truncated Discrete Time Stockwell Transform (TDTST), peak discrimination, and a trained Neural Network to reduce the number of false positive QRS detections. An optimization method is presented that automatically adjust the detector's parameters to minimize the computational cost. Results demonstrate that the compact TDTST algorithm exhibits high QRS detection accuracy, an error rate of 0.31%, and remains applicable to real-time embedded physiologic ambulatory monitors.

KW - Compact algorithm

KW - Embedded

KW - Physiologic monitoring

KW - QRS detection

KW - Realtime

KW - Stockwell transform

UR - http://www.scopus.com/inward/record.url?scp=85047991176&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85047991176&partnerID=8YFLogxK

U2 - 10.1109/GlobalSIP.2017.8309112

DO - 10.1109/GlobalSIP.2017.8309112

M3 - Conference contribution

AN - SCOPUS:85047991176

VL - 2018-January

SP - 1005

EP - 1009

BT - 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017

Y2 - 14 November 2017 through 16 November 2017

ER -