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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publication2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1005-1009
Number of pages5
Volume2018-January
ISBN (Electronic)9781509059904
DOIs
StatePublished - Mar 7 2018
Event5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Montreal, Canada
Duration: Nov 14 2017Nov 16 2017

Other

Other5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017
CountryCanada
CityMontreal
Period11/14/1711/16/17

Fingerprint

Detectors
Mathematical transformations
Waveform analysis
Electrocardiography
Neural networks
Costs

Keywords

  • Compact algorithm
  • Embedded
  • Physiologic monitoring
  • QRS detection
  • Realtime
  • Stockwell transform

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

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 proceedingConference 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
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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.",
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