Baseline Suppression in ECG-signals

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Baseline Suppression in ECG-signals
Lisette Harting
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Contents

• Introduction to the problem
• Problem approach
• ECG analysis
• Common used solutions ideas
• Results
• Conclusions and recommendations
• Questions

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Introduction to the problem
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Function of the heart

• Distribute oxygen and nutrition

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Electrophysiology
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Pathology

• Bad conductance of signal
• Second pacer also initiates contraction
• Needs to be destructed destructor
• 2 types of operation
• Open chest
• Minimal surgery (catheters, ablation)

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Measuring ECG/EG

• Where does the ECG origin?
• Chest (only) resistive ? potentials on the skin
  potentials on heart factor
• Three deductions of ECG

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Measuring ECG/EG

• Extremity leads
• Einthoven
• Goldberger

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Measuring ECG/EG

• Precordial leads

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Application

• Diagnostic system
• Exercise ECG
• Operation room system

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Baseline drift

• In exercise ECG caused by
• Movements of the patient
• Breathing
• Changing electrode skin contact
• In operation room merely caused by
• Breathing
• Ablation

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Assignment

• Design of baseline drift filter for
  operation-room ECG
• With test-signals for breathing originated
  baseline drift
• Later to be used in exercise ECG and other
  applications

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Specifications

• Input
• Multiple channels (6 to gt 12)
• Already first order high pass-filtered with
  cutoff frequency 0.5 Hz or 0.05 Hz

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• Desired output
• Cutoff frequency 0.5 Hz
• 0.5 Hz and lower minimal 6 dB attenuation
• Delay maximal 120 ms
• Minimize signal to noise ratio
• Minimize distortion of signal
• Must work real time on a normal computer

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Problem approach
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Work

• Literature study
• Oscillation filter on synthetic test signal
• IIR / FIR
• Analyzed experimental signals
• Made for-backward filter with heart rate
  adaptation
• Demonstration program

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• To do
• Write report
• Optimize chosen filter further
• Work out theoretical problem
• No time for
• Adaptive filters

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ECG-signal analysis
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Time domain
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PSD
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SNR

• Signal to noise ratio (from PSD)
• S/N 10 10log(Ps/Pn)
• Signal
• Heart rate and higher frequencies
• Noise
• Rest of signal
• Compared qualities of the signals from the 19
  experiments

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Heartrate

• Varied between 25 and 35
• Was detected correctly 100 by the algorithm (to
  be discussed later)
• Not tested with ill patients

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Common solutions
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Filters

• Idea behind hp digital filters
• Out In(delayed) In(filtered)

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Average based filters

• Average based filters
• Moving average filters (box)
• Triangular FIR-filter
• With smart size of window to be able to use
  shifting instead of division after adding
• FIR
• May be linear phase
• But need large calculation power

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Bidirectional filters

• Input hardware filter is reversed in time and
  sampled
• Symmetric filter (zero phase shift)
• Problem fixed cutoff frequency

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Incrementally changing filter

• Incrementally changing filter for QRS-complex and
  rest of ECG-signal

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Slew rate limiter

• slew rate limiter
• Against fast increase of baseline drift (optimize
  step response)
• Limit rising and falling rate of the signal

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Other solutions
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Heart rate detection

• Simple algorithm
• Derivative lt minimal value ? count1
• Derivative gt minimal value ? reset count
• If count gt limit ? QRS-complex detected
• reset count
• pause detection algorithm 100 ms
• adjust cutoff frequency filter
• Time between 2 complexes heart rate

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Envelope method

• Baseline drift estimation
• envelope around input signal
• Estimation is mean of the envelope
• Idea
• Use information about ECG phase
• to correct for distortion of ECG
• based on (measured) phase dependent distortion of
  a pure ECG-signal

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Adaptive oscillator

• Principle
• Suppress ECG-signal (SLR or lp-filter)
• After SLR-interval average is BLD-estimation
• Use 2 BLD-estimates to predict 3rd (IIR)
• d(n) a(n) d(n-w) d(n-2w)
• Update a
• a(n1) a(n) d_real(n) d_meas(n) / d(n-1)

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Cross-Correlation filter

• Principle
• Do not adapt filters one by one, but use
  knowledge about other signals
• Why?
• There is a high correlation between the signals

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Cross-correlation filter

• Why not?
• Fast (10 s) and high (90) changes of the
  correlation between the signals
• Low frequencies need a lot of time memory to
  calculate correlation
• Non-linear relation between signals
• Heart rate would need to be filtered out too

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For-backward filtering

• Principle
• Minimize calculation time
• decimation
• IIR-filtering
• Linearize and increase steepness IIR-filter by
  filtering also backward

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For-backward filtering

• Working
• Prefilter signal with cutoff freq. 10 Hz.
• Decimate signal with 50 to 40 Hz.
• Filter signal again with cutoff freq. 0.5 Hz.
• Interpolate signal
• Filter out high frequency components introduced
  by interpolated signals

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For-backward filtering

• Filtering
• IIR
• Continuously forward
• Backward over window
• window gt max. delay filter for all frequencies
• last filtered sample is filtered value
• Apply together with heart rate adaptation

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Which can be tried

• Adaptive oscillator
• FIR
• IIR
• For-backward filter Heart rate adaptive filter
• Envelope (but no time)

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Results
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Adaptive oscillator

• The adaptive oscillator was not stable
• Step-adaptation of parameters -in order to
  stabilize- deformed the shape of the ECG-signal
• Because of fast changes of sinusoid ? unstable
• Non-linear
• Does not work when other noise is present

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FIR

• Principle
• The ideal response of an analogue filter is
  truncated
• Length half (180 degrees) cutoff frequency
• 0.5 Hz 1 sec 0.05 Hz 10 sec.
• It is the standard solution
• But delay gt 1 second

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Moving average (2000 points)
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Chebyshev
(1000-points 10dB sidelobe-supression)
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IIR

• IIR
• Fast (minimal one sample)
• But
• phase shift causes
• Distortion of ECG-signal
• The same delay of the signal

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Time-domain
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Frequency domain
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Filters
Prefilter before decimation
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Heart rate filter
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SNR

• SNR-improvement is quite high
• But for signals with little noise, the SNR
  improvement can be negative

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Distorsion
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Step response
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Calculation power

• Depend on window width (win) decimation
  factor (dec)
• Decimation filter forward 1 backward 1
• Filter forward 1/dec backward win/dec
• Interpolation forward 1 backward dec
• TOTAL 3 (win dec 1)/dec

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Delay (samples)

• Decimation filter forward 1 backward dec
• Filter forward dec backward decwin
• Zero order interpolation 0.5 dec
• Interpolation forward 1 backward dec
• TOTAL 2 (win 3.5)dec (samples)

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• TOTAL 2 (win 3.5)dec (samples)
• Optimal
• win 13 (minimal)
• dec 40
• This makes total
• (2 16.540) / 2000 662 / 2000 0.331 sec.

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Demonstration

• ..\Program

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Demonstration
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Conclusions and recommendations
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Conclusions

• Heart rhythm adaptation works good, in this case
• Heart rate filter is working, but needs to be
  improved

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Recommendations

• The heart-rate filter should be tested on more
  data
• Does the simple heart rate detection system work
• in all situations?
• on all patients?
• It needs to be improved for e.g. systoles en PVCs

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• Automatically calculate minimal window-width
• Introduce 2 delay-modes
• Optimize parameters
• Quantize distortion
• Use only analogue low-pass filter
• Try other bidirectional filters (same as our
  filter)

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Questions!