Wavelet Package Decomposition of Heart Sound in Heart Dysfunction Identification

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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Wavelet Package Decomposition of Heart Sound in Heart Dysfunction Identification

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