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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

QRS DetectionSection 6.2 - 6.2.5


Linda Henriksson


qrs complex
QRS Complex

P wave: depolarization of right and left atrium

QRS complex: right and left ventricular depolarization

ST-T wave: ventricular repolarization

qrs detection
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
qrs detection4
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)
signal and noise problems
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)
signal and noise problems6
Signal and Noise Problems


estimation problem
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
qrs detection8
QRS Detection

Unknown time of occurrence 

qrs detection10
QRS Detection

Unknown time of occurrence and amplitude a

qrs detection11
QRS Detection

Unknown time of occurrence, amplitude and width

qrs detection13
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
linear filtering
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
linear filtering15
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
nonlinear transformations
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
decision rule
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
decision rule18
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
performance evaluation
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
performance evaluation20
Performance Evaluation

Estimate performance from ECG recordings database

performance evaluation22
Performance Evaluation

Receiver operating characteristics (ROC)

  • Study behaviour of detector for different parameter values
  • Choose parameter with acceptable trade-off between PD and PF
  • 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