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QRS Detection Section 6.2 - 6.2.5

QRS Detection Section 6.2 - 6.2.5. 18.11.2004 Linda Henriksson BRU/LTL. QRS Complex. P wave: depolarization of right and left atrium QRS complex: right and left ventricular depolarization ST-T wave: ventricular repolarization. QRS Detection.

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QRS Detection Section 6.2 - 6.2.5

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  1. QRS DetectionSection 6.2 - 6.2.5 18.11.2004 Linda Henriksson BRU/LTL

  2. QRS Complex P wave: depolarization of right and left atrium QRS complex: right and left ventricular depolarization ST-T wave: ventricular repolarization

  3. QRS Detection • QRS detection is important in all kinds of ECG signalprocessing • QRS detector must be able to detect a large number of different QRS morphologies • QRS detector must not lock onto certain types of rhythms but treat next possible detection as if it could occur almost anywhere

  4. QRS Detection • Bandpass characteristics to preserve essential spectral content (e.g. enhance QRS, suppress P and T wave), typical center frequency 10 - 25 Hz and bandwidth 5 - 10 Hz • Enhance QRS complex from background noise, transform each QRS complex into single positive peak • Test whether a QRS complex is present or not (e.g. a simple amplitude threshold)

  5. Signal and Noise Problems • Changes in QRS morphology • of physiological origin • due to technical problems • Occurrence of noise with • large P or T waves • myopotentials • transient artifacts (e.g. electrode problems)

  6. Signal and Noise Problems http://medstat.med.utah.edu/kw/ecg/image_index/index.html

  7. Estimation Problem • Maximum likelihood (ML) estimation technique to derive detector structure • Starting point: same signal model as for derivation of Woody method for alignment of evoked responses with varying latencies

  8. QRS Detection Unknown time of occurrence 

  9. QRS Detection

  10. QRS Detection Unknown time of occurrence and amplitude a

  11. QRS Detection Unknown time of occurrence, amplitude and width

  12. QRS Detection

  13. QRS Detection Peak-and-valley picking strategy • Use of local extreme values as basis for QRS detection • Base of several QRS detectors • Distance between two extreme values must be within certain limits to qualify as a cardiac waveform • Also used in data compression of ECG signals

  14. Linear Filtering • To enhance QRS from background noise • Examples of linear, time-invariant filters for QRS detection: • Filter that emphasizes segments of signal containing rapid transients (i.e. QRS complexes) • Only suitable for resting ECG and good SNR • Filter that emphasizes rapid transients + lowpass filter

  15. Linear Filtering • Family of filters, which allow large variability in signal and noise properties • Suitable for long-term ECG recordings (because no multipliers) • Filter matched to a certain waveform not possible in practice • Optimize linear filter parameters (e.g. L1 and L2) • Filter with impulse response defined from detected QRS complexes

  16. Nonlinear Transformations • To produce a single, positive-valued peak for each QRS complex • Smoothed squarer • Only large-amplitude events of sufficient duration (QRS complexes) are preserved in output signal z(n). • Envelope techniques • Several others

  17. Decision Rule • To determine whether or not a QRS complex has occurred • Fixed threshold  • Adaptive threshold • QRS amplitude and morphology may change drastically during a course of just a few seconds • Here only amplitude-related decision rules • Noise measurements

  18. Decision Rule • Interval-dependent QRS detection threshold • Threshold updated once for every new detection and is then held fixed during following interval until threshold is exceeded and a new detection is found • Time-dependent QRS detection threshold • Improves rejection of large-amplitude T waves • Detects low-amplitude ectopic beats • Eye-closing period

  19. Performance Evaluation • Before a QRS detector can be implemented in a clinical setup • Determine suitable parameter values • Evaluate the performance for the set of chosen parameters • Performance evaluation • Calculated theoretically or • Estimated from database of ECG recordings containing large variety of QRS morphologies and noise types

  20. Performance Evaluation Estimate performance from ECG recordings database

  21. Performance Evaluation

  22. Performance Evaluation Receiver operating characteristics (ROC) • Study behaviour of detector for different parameter values • Choose parameter with acceptable trade-off between PD and PF

  23. Summary • QRS detection important in all kinds of ECG signal processing • Typical structure of QRS detector algorithm: preprocessing (linear filter, nonlinear transformation) and decision rule • For different purposes (e.g. stress testing or intensive care monitoring), different kinds of filtering, transformations and thresholding are needed • Multi-lead QRS detectors

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