An algorithm for microprocessor-based QRS detection

OJ Escalona, G Passariello, F Mora

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

QRS detection is fundamental for intensive care cardiac instrumentation, such as in arrhythmia recognition or ambulatory recording of cardiac events. Reliable detection of the QRS is necessary in order to obtain accurate measurements of the R-R intervals and QRS width. This paper describes a real-time algorithm for QRS detection based upon the theory of maxima and minima to locate peaks in the digitized ECG. A three-point sliding window is used to determine the presence of peaks and valleys on the signal. Analysis of the difference in amplitude and the time interval between consecutive peaks and valleys allows QRS detection. Thresholds used for detection are adapted according to changes in the amplitude and duration of the previous QRS complexes. Testing was performed with a normal ECG recording contaminated with different levels of added Gaussian noise, giving an average detection rate from 99.7 percent True Positive for a signal-to-noise ratio of 19 dB to 86.5 percent for a low SNR of 7 dB.QRS detection is fundamental for intensive care cardiac instrumentation, such as in arrhythmia recognition of ambulatory recording of cardiac events. This paper describes a real-time algorithm for QRS detection based upon the theory of maxima and minima to locate peaks in the digitized ECG. A three-point sliding window is used to determine the presence of peaks and valleys on the signal. Analysis of the difference in amplitude and the time interval between consecutive peaks and valleys allows QRS detection. Thresholds used for detection are adapted according to changes in the amplitude and duration of the previous QRS complexes. Testing was performed with a normal ECG recording contaminated with different levels of added Gaussian noise, giving an average detection rate from 99. 7 percent True Positives for a signal-to-noise ratio of 19 db to 86. 5 percent for a low snr of 7 db.
LanguageEnglish
Pages213-219
JournalJournal of Clinical Engineering
Volume11
Issue number3
Publication statusPublished - 1 May 1986

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Microcomputers
Electrocardiography
Microprocessor chips
Signal-To-Noise Ratio
Critical Care
Cardiac Arrhythmias
Signal to noise ratio
Testing

Keywords

  • ECG
  • ECG Processing
  • QRS Detection
  • Microcomputer Applications
  • Arrhythmia Analysis
  • Biomedical Signal Processing
  • Microcontrollers Fast Algorithms

Cite this

Escalona, OJ ; Passariello, G ; Mora, F. / An algorithm for microprocessor-based QRS detection. In: Journal of Clinical Engineering. 1986 ; Vol. 11, No. 3. pp. 213-219.
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Escalona, OJ, Passariello, G & Mora, F 1986, 'An algorithm for microprocessor-based QRS detection', Journal of Clinical Engineering, vol. 11, no. 3, pp. 213-219.

An algorithm for microprocessor-based QRS detection. / Escalona, OJ; Passariello, G; Mora, F.

In: Journal of Clinical Engineering, Vol. 11, No. 3, 01.05.1986, p. 213-219.

Research output: Contribution to journalArticle

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T1 - An algorithm for microprocessor-based QRS detection

AU - Escalona, OJ

AU - Passariello, G

AU - Mora, F

PY - 1986/5/1

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N2 - QRS detection is fundamental for intensive care cardiac instrumentation, such as in arrhythmia recognition or ambulatory recording of cardiac events. Reliable detection of the QRS is necessary in order to obtain accurate measurements of the R-R intervals and QRS width. This paper describes a real-time algorithm for QRS detection based upon the theory of maxima and minima to locate peaks in the digitized ECG. A three-point sliding window is used to determine the presence of peaks and valleys on the signal. Analysis of the difference in amplitude and the time interval between consecutive peaks and valleys allows QRS detection. Thresholds used for detection are adapted according to changes in the amplitude and duration of the previous QRS complexes. Testing was performed with a normal ECG recording contaminated with different levels of added Gaussian noise, giving an average detection rate from 99.7 percent True Positive for a signal-to-noise ratio of 19 dB to 86.5 percent for a low SNR of 7 dB.QRS detection is fundamental for intensive care cardiac instrumentation, such as in arrhythmia recognition of ambulatory recording of cardiac events. This paper describes a real-time algorithm for QRS detection based upon the theory of maxima and minima to locate peaks in the digitized ECG. A three-point sliding window is used to determine the presence of peaks and valleys on the signal. Analysis of the difference in amplitude and the time interval between consecutive peaks and valleys allows QRS detection. Thresholds used for detection are adapted according to changes in the amplitude and duration of the previous QRS complexes. Testing was performed with a normal ECG recording contaminated with different levels of added Gaussian noise, giving an average detection rate from 99. 7 percent True Positives for a signal-to-noise ratio of 19 db to 86. 5 percent for a low snr of 7 db.

AB - QRS detection is fundamental for intensive care cardiac instrumentation, such as in arrhythmia recognition or ambulatory recording of cardiac events. Reliable detection of the QRS is necessary in order to obtain accurate measurements of the R-R intervals and QRS width. This paper describes a real-time algorithm for QRS detection based upon the theory of maxima and minima to locate peaks in the digitized ECG. A three-point sliding window is used to determine the presence of peaks and valleys on the signal. Analysis of the difference in amplitude and the time interval between consecutive peaks and valleys allows QRS detection. Thresholds used for detection are adapted according to changes in the amplitude and duration of the previous QRS complexes. Testing was performed with a normal ECG recording contaminated with different levels of added Gaussian noise, giving an average detection rate from 99.7 percent True Positive for a signal-to-noise ratio of 19 dB to 86.5 percent for a low SNR of 7 dB.QRS detection is fundamental for intensive care cardiac instrumentation, such as in arrhythmia recognition of ambulatory recording of cardiac events. This paper describes a real-time algorithm for QRS detection based upon the theory of maxima and minima to locate peaks in the digitized ECG. A three-point sliding window is used to determine the presence of peaks and valleys on the signal. Analysis of the difference in amplitude and the time interval between consecutive peaks and valleys allows QRS detection. Thresholds used for detection are adapted according to changes in the amplitude and duration of the previous QRS complexes. Testing was performed with a normal ECG recording contaminated with different levels of added Gaussian noise, giving an average detection rate from 99. 7 percent True Positives for a signal-to-noise ratio of 19 db to 86. 5 percent for a low snr of 7 db.

KW - ECG

KW - ECG Processing

KW - QRS Detection

KW - Microcomputer Applications

KW - Arrhythmia Analysis

KW - Biomedical Signal Processing

KW - Microcontrollers Fast Algorithms

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VL - 11

SP - 213

EP - 219

JO - Journal of Clinical Engineering

T2 - Journal of Clinical Engineering

JF - Journal of Clinical Engineering

SN - 0363-8855

IS - 3

ER -