Multichannel Phonocardiogram Source Separation

PGBIOMEDUniversity of Reading

20th July 2005

Conor Fearon and Scott Rickard

University College Dublin

Outline

• Anatomy & Physiology of Heart Sound Generation

• Problem Definition• Why Solve This

Problem?• Heart Sound Model• Source Separation

Techniques• Results• Future Work

www.blaufuss.org

First and Second heart sounds (S1 and S2)

• S1: composite of mitral (M1) and tricuspid (T1) valve closure sounds

• S2: composite of aortic (A2) and pulmonic (P2) valve closure sounds

M1

A2

P2

T1

Constituent1

Constituent2

Constituent3

Constituent4

Original Signal

S1 S2

Separation of a heart sound containing tricuspid regurgitation into its spatially distributed constituents: mitral (M1), tricuspid (T1) with regurgitant murmur, aortic (A2) and pulmonic (P2) components.

Problem Definition

Auscultation is Difficult

• Signal properties– A large portion of heart

sound energy is subaudible

• Multiple noise sources– Background: air

conditioning, door closings, conversation, alarms

– Internal: breathing, crying, coughing, bowel sounds, speaking

• Auscultation is subjective.

• Auscultation has taken a back seat to more expensive technology.

Zargis Acoustic Cardioscan

• Computer-assisted auscultation system.

• Obtains heart sound recordings using an electronic stethoscope.

• Performs integrated analysis of recorded heart sounds.

• Provides quantitative measures of acoustic features with physiological significance.

• Delivers auscultatory findings in a way that is already familiar to physicians.

• Provides archival record of heart sounds and analysis for substantiation of referral, guidance for echo studies, and serial comparisons.

• FDA clearance for S1, S2, murmur detection.

Zargis Acoustic Cardioscan

• Whereas this system analyses the timing, intensity and frequency content of heart sounds, it utilises sequential single channel recordings, in keeping with standard auscultatory protocol, and thus, does not derive location information.

• So it can detect S1 and S2 but cannot separate into their constituent components.

• Can detect murmurs but cannot determine where in the heart they arise.

• We propose a method which uses multichannel heart sound recordings to localise heart sound energy and to disambiguate the physiological significance of similar constituents of the phonocardiogram .

Applications of Localisation

• When tachycardia or arrhythmia is present, distinguishing S1 and S2 can be challenging and often requires a synchronous reference signal.

• Distinguishing mitral valve from tricuspid valve.

• Distinguishing aortic valve from pulmonic valve.

• Heart sounds originating in other parts of the cardiac mass can also be accurately located, which would have far-reaching diagnostic value.

Heart Sound Model

• Modeled M1, T1, A2, P2 as Daubechies wavelets.

• Used realistic timings.• Created synthetic mixtures

at four main points of auscultation.

• Homogeneous intervening tissue with c=1530m/s.

• Stationary sound sources.• Assumed no scattering or

reflection of sound.

Blind Source Separation

X AS N • Recover the original signals of interest

given only mixtures of the signals with no knowledge or limited knowledge of the mixing process and the underlying sources.

• Assumptions about sources.• Heart sounds not independent or

stationary.• Sparse Methods: many entries zero or

nearly zero in a given basis.

Sparse Source Separation Techniques

• Instantaneous Mixing:

111 12 131

221 22 232

3

( )( )

( )

sa a ax t

s n ta a ax t

s

• Line orientations correspond to columns of A.

• Use clustering algorithm to find line orientations.

Sparse Source Separation Techniques

• Anechoic Mixing: 1 1 11

2 2 21

( ) ( )

( ) ( )

N

j j jj

N

j j jj

x t a s t

x t a s t

• DUET:

-speech is sparse in t-f domain.

-only one source active at any t-f point.2

1

22

1 1

( , )

( , )

j

j j

j

j

i

j i

ji

j

a e sxa e

x a e s

Sparse Source Separation Techniques

• DUET:2

1

22

1 1

( , )

( , )

j

j j

j

j

i

j i

ji

j

a e sxa e

x a e s

Estimate parameters

at each point in t-f

domain and use power-

weighted histogram to

estimate true values.

Multichannel Phonocardiogram Source Separation

• Heart sounds are sparse in time-domain.• Delays are <0.2msecs.• Instantaneous mixing.• Take pairwise ratios of four mixtures.• Place in a 6-dimensional histogram.• Find peaks and partition time-domain.

Results

Future Work

• Unknown Channel.• Real Recordings.• Time or Time-Scale Domain?• Instantaneous or Anechoic Mixing?• Results are preliminary but

potential is there.

Thank You!