Baseline Suppression in ECG-signals
Lisette Harting
Contents
• Introduction to the problem
– Problem approach
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ECG analysis
Common used solutions & ideas
Results
Conclusions and recommendations
Questions
contents - introduction - approach - ECG - literature - results - conclusions
Introduction to the problem
contents - introduction - approach - ECG - literature - results - conclusions
Function of the heart
• Distribute oxygen and nutrition
contents - introduction - approach - ECG - literature - results - conclusions
Electrophysiology
Pathology
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Bad conductance of signal
Second pacer also initiates contraction
Needs to be destructed: destructor
2 types of operation:
– Open chest
– Minimal surgery (catheters, ablation)
contents - introduction - approach - ECG - literature - results - conclusions
Measuring ECG/EG
• Where does the ECG origin?
– Chest (only) resistive  potentials on the
skin ~ potentials on heart * factor
• Three deductions of ECG
contents - introduction - approach - ECG - literature - results - conclusions
Measuring ECG/EG
• Extremity leads
– Einthoven
– Goldberger
contents - introduction - approach - ECG - literature - results - conclusions
Measuring ECG/EG
• Precordial leads
contents - introduction - approach - ECG - literature - results - conclusions
Application
• Diagnostic system
– Exercise ECG
• Operation room system
contents - introduction - approach - ECG - literature - results - conclusions
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– Movements of the patient
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contents - introduction - approach - ECG - literature - results - conclusions
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
contents - introduction - approach - ECG - literature - results - conclusions
Specifications
• Input:
– Multiple channels (6 to > 12)
– Already first order high pass-filtered with
cutoff frequency 0.5 Hz or 0.05 Hz
contents - introduction - approach - ECG - literature - results - conclusions
• 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
contents - introduction - approach - ECG - literature - results - conclusions
Problem approach
contents - introduction - approach - ECG - literature - results - conclusions
Work
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Literature study
Oscillation filter on synthetic test signal
IIR / FIR
Analyzed experimental signals
Made for-backward filter with heart rate
adaptation
• Demonstration program
contents - introduction - approach - ECG - literature - results - conclusions
• To do:
– Write report
– Optimize chosen filter further
– Work out theoretical problem
• No time for:
– Adaptive filters
contents - introduction - approach - ECG - literature - results - conclusions
ECG-signal analysis
contents - introduction - approach - ECG - literature - results - conclusions
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Time domain
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PSD
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contents - introduction - approach - ECG - literature - results - conclusions
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
contents - introduction - approach - ECG - literature - results - conclusions
Heartrate
• Varied between 25 and 35
• Was detected correctly 100% by the
algorithm (to be discussed later)
• Not tested with ill patients
contents - introduction - approach - ECG - literature - results - conclusions
Common solutions
contents - introduction - approach - ECG - literature - results - conclusions
Filters
• Idea behind hp digital filters:
– Out = In(delayed) – In(filtered)
delay
+
low pass filter
contents - introduction - approach - ECG - literature - results - conclusions
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
contents - introduction - approach - ECG - literature - results - conclusions
Bidirectional filters
• Input hardware filter is reversed in time
and sampled
• Symmetric filter (zero phase shift)
• Problem: fixed cutoff frequency
contents - introduction - approach - ECG - literature - results - conclusions
Incrementally changing filter
• Incrementally changing filter for QRScomplex and rest of ECG-signal
contents - introduction - approach - ECG - literature - results - conclusions
Slew rate limiter
• slew rate limiter
– Against fast increase of baseline drift
(optimize step response)
– Limit rising and falling rate of the signal
contents - introduction - approach - ECG - literature - results - conclusions
Other solutions
contents - introduction - approach - ECG - literature - results - conclusions
Heart rate detection
• Simple algorithm:
– Derivative < minimal value  count+1
– Derivative > minimal value  reset count
– If count > limit  QRS-complex detected
• reset count
• pause detection algorithm 100 ms
• adjust cutoff frequency filter
– Time between 2 complexes = heart rate
contents - introduction - approach - ECG - literature - results - conclusions
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
contents - introduction - approach - ECG - literature - results - conclusions
Adaptive oscillator
• Principle:
– Suppress ECG-signal (SLR or lp-filter)
– After SLR-interval: average is BLDestimation
– Use 2 BLD-estimates to predict 3rd (IIR):
• d(n) = a(n) * d(n-w) – d(n-2w)
– Update ‘a’
• a(n+1) = a(n) + [d_real(n) – d_meas(n)] / d(n-1)
contents - introduction - approach - ECG - literature - results - conclusions
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
contents - introduction - approach - ECG - literature - results - conclusions
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
contents - introduction - approach - ECG - literature - results - conclusions
For-backward filtering
• Principle
– Minimize calculation time
• decimation
• IIR-filtering
– Linearize and increase steepness IIR-filter
by filtering also backward
contents - introduction - approach - ECG - literature - results - conclusions
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
contents - introduction - approach - ECG - literature - results - conclusions
For-backward filtering
• Filtering:
– IIR
– Continuously forward
– Backward over window
• window > max. delay filter for all frequencies
• last filtered sample is filtered value
– Apply together with heart rate adaptation
contents - introduction - approach - ECG - literature - results - conclusions
Which can be tried
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Adaptive oscillator
FIR
IIR
For-backward filter +
Heart rate adaptive filter
• Envelope (but no time)
contents - introduction - approach - ECG - literature - results - conclusions
Results
contents - introduction - approach - ECG - literature - results - conclusions
Adaptive oscillator
• The adaptive oscillator was not stable
• Step-adaptation of parameters -in order to
stabilize- deformed the shape of the ECGsignal
• Because of fast changes of sinusoid 
unstable
• Non-linear
• Does not work when other noise is present
contents - introduction - approach - ECG - literature - results - conclusions
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 >= 1 second
contents - introduction - approach - ECG - literature - results - conclusions
Moving average (2000 points)
ECG-signal
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Chebyshev
ECG-signal
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1000 samples)
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contents - introduction - approach - ECG - literature - results - conclusions
IIR
• IIR
– Fast (minimal one sample)
• But
– phase shift causes
• Distortion of ECG-signal
• The same delay of the signal
contents - introduction - approach - ECG - literature - results - conclusions
raw ECG-signal
Time-domain
forward prefiltered ECG-data
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contents - introduction - approach - ECG - literature - results - conclusions
raw ECG-signal
forward prefiltered ECG-data
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contents - introduction - approach - ECG - literature - results - conclusions
Frequency domain
raw ECG-signal
forward prefiltered ECG-data
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contents - introduction - approach - ECG - literature - results - conclusions
raw ECG-signal
forward prefiltered ECG-data
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contents - introduction - approach - ECG - literature - results - conclusions
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Filters
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contents - introduction - approach - ECG - literature - results - conclusions
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Heart rate filter
contents - introduction - approach - ECG - literature - results - conclusions
SNR
• SNR-improvement is quite high
• But for signals with little noise, the SNR
improvement can be negative
Distorsion
contents - introduction - approach - ECG - literature - results - conclusions
Step response
raw ECG-signal
forward prefiltered ECG-data
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contents - introduction - approach - ECG - literature - results - conclusions
Calculation power
• Depend on
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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
contents - introduction - approach - ECG - literature - results - conclusions
Delay (samples)
• Decimation filter forward:
1
backward:
dec
• Filter
forward:
dec
backward:
dec*win
• Zero order interpolation
0.5 * dec
• Interpolation
forward
1
backward
dec
• TOTAL = 2 + (win + 3.5)*dec (samples)
contents - introduction - approach - ECG - literature - results - conclusions
• TOTAL = 2 + (win + 3.5)*dec (samples)
• Optimal:
– win = 13 (minimal)
– dec = 40
• This makes total:
– (2 + 16.5*40) / 2000 = 662 / 2000 = 0.331 sec.
contents - introduction - approach - ECG - literature - results - conclusions
Demonstration
• ..\Program
contents - introduction - approach - ECG - literature - results - conclusions
Demonstration
contents - introduction - approach - ECG - literature - results - conclusions
Conclusions and recommendations
contents - introduction - approach - ECG - literature - results - conclusions
Conclusions
• Heart rhythm adaptation works good, in
this case
• Heart rate filter is working, but needs to
be improved
contents - introduction - approach - ECG - literature - results - conclusions
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 PVC’s
contents - introduction - approach - ECG - literature - results - conclusions
• Automatically calculate minimal windowwidth
• Introduce 2 delay-modes
• Optimize parameters
• Quantize distortion
• Use only analogue low-pass filter
• Try other bidirectional filters (same as
our filter)
contents - introduction - approach - ECG - literature - results - conclusions
Questions!