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 | |
| 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 |
Fingerprint
Keywords
- Compact algorithm
- Embedded
- Physiologic monitoring
- QRS detection
- Realtime
- Stockwell transform
ASJC Scopus subject areas
- Information Systems
- Signal Processing
Cite this
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
}
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.
ER -