wavelet package decomposition of heart sound in heart dysfunction identification
TRANSCRIPT
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8/14/2019 Wavelet Package Decomposition of Heart Sound in Heart Dysfunction Identification
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W a ve le t Pa c k a g eD e c o m p o s i t io n o fH e a r t S o u n d in H e a r t
Dys fu n c t i o nIdent i f i ca t ion
Jaganatha Pandian.BJaganatha Pandian.B
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Outline
Phonocardiogram Heart murmur
Heart Dysfunction Impact on PCG
Wavelet Package Decomposition
PCG analysis using WPD
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Heart Sounds A normal cardiac cycle contains two major
audiblesounds:
The first heart sound(S1) : As the ventricularpressure exceeds the atrium pressure, themitral and the tricuspid valves close and the
vibrations of S1 begin.
The second heart sound(S2). At the end of the
ventricular systole and the beginning ofventricular relaxation, S2 occurs following the
closure of the aortic and the pulmonary valves.
More audible sounds include: S3, S4, and murmurs.
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The Origin of Heart Sounds Valvular theory
Vibrations of the heart valves during their closure
Cardiohemic theory Vibrations of the entire cardiohemic system: heartcavities, valves, blood
Normal
Abnormal
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Heart Dysfunction
Aortic Stenosis (AS)
Aortic Septal Defect (ASD)Mitral Stenosis (MS)
Ventricular Septal Defect (VSD).
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Aortic Stenosis (AS):
Occurs when Aortic Valve does not open
completely.
Impact: Systolic murmur, which is smallat first, rising to a peak in mid-systole
and then decreasing so that it is small or
absent before reaching S2.
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Atrial Septal Defect (ASD)
A hole in the septum between the hearts
two upper chambers
Impact: Presence of systolic murmurs between S1
and S2.
A wide split in the S2 components.
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Mitral Stenosis (MS)
Occurs when a Mitral Valve does not
open completely.
Impact: A low pitched diastolic rumble Presence of S3
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Ventricular Septal Defect (VSD)
A hole in the septum between the hearts
two lower chambers
Impact : The presence of rough highpitched pan systolic murmurs begins with
S1 and extends to cover S2.
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Signal Analysis
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Wavelet Transform (WT)
Signal is multiplied with a function, but, Width
of the Window is Changed as the Transform is
Computed for Every Spectral Components
Split the Signal into a Bunch of Signals
Representing the Same Signal, but all
Corresponding to Different Frequency Bands
Provides What Frequency Bands Exists atWhat Time Intervals
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Step 1: The wavelet is placed at the beginning of the signal, and sets=1 (the most compressed wavelet);
Step 2: The wavelet function at scale 1 is multiplied by the signal, andintegrated over all times; then multiplied by ;
Step 3: Shift the wavelet to t= , and get the transform value at t=and s=1;
Step 4: Repeat the procedure until the wavelet reaches the end of the
signal; Step 5: Scale s is increased by a sufficiently small value, the aboveprocedure is repeated for all s;
Step 6: Each computation for a given s fills the single row of the time-scale plane;
Step 7: CWT is obtained if all s are calculated.
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Signal Decomposition using WT
The decomposition of the signalinto different frequency bands issimply obtained by successive
high-pass and low-pass filteringof the time domain signal. WTcan be used to Decompose thesignal to the depth of interest.
CA Approximation coefficients CD Detail coefficients
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Wavelet Packet Decomposition (WPD)Wavelet Packet Decomposition (WPD)
Only difference it makes with WT is that it splits Detailcoefficients also in each level of decomposition.
WPD analysis after the final level of decompositiongives number of packets. Each packet corresponds tospecific Frequency Band.
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Wavelet used
Daubechies db4 Wavelet
oscillations are similar to that of a PCG signalDepth of decomposition - 10
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Frequency Band coverage per
Node At the depth of 10 in WPD, each Node
covers a frequency band of,
fs sampling frequency, here 44000 Hz
j depth of decomposition, here 10
The band coverage here is 21.74 Hz per Packet.
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Energy of Packets
The energy of Node (i) in Depth (j) is given by,
where N is number of coefficients in each Node in depth J.
N = D2-j,
D is Length of original signal (or) Total number of samples in original signal
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Result
While analyzing a PCG signal, it is
sufficient to monitor its behavior for the
frequency range 30-256Hz. So it is sufficient for us to monitor WPD
nodes 2 to 13 in our case.
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Coefficient plotsCoefficient plots
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Coefficient plotsCoefficient plots
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Energy Levels
Disease Nodeaffected
Under normalcondition
Under diseasedcondition
Aortic Stenosis (AS) (10 ,5) 25.10 9.03
Aortic Septal Defect(ASD)
(10 ,4) 6.68 10.53
Mitral Stenosis (MS) (10 ,6) 7.69 13.95
Ventricular SeptalDefect (VSD)
(10 ,4) 6.68 19.98
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