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11.2.9.3 Differential Diagnosis of MVOS Versus S3 Gallop
The OS is of higher pitch and occurs earlier in diastole than S3. In addition, the S3
gallop tends to be localized to the lower left sternal border (LSB) (RV S3G) or the
cardiac apex (LV S3G), whereas the MVOS is usually widely transmitted.
In patients with significant isolated mitral valve stenosis, the MVOS is usually
heard in association with the typical diastolic murmur of mitral stenosis. An S3 gal-
lop is not heard because mitral stenosis prevents rapid ventricular filling and thus
the generation of an S3. An S3 gallop in patients with mitral stenosis suggests that
the mitral stenosis is trivial, the predominant lesion is mitral regurgitation, or the
origin of the S3 gallop is from right ventricle dysfunction.
11.2.9.4 Differential Diagnosis of MVOS Versus Splitting of S2
The MVOS is of higher pitch and is usually louder than P2. In addition, the A2–OS
interval is usually wider than the A2–P2 interval.
The OS is usually louder at the lower LSB than at the upper LSB, is heard well at
the apex, and is often audible in the suprasternal notch. Splitting of S2 is best heard
in the pulmonic area or at Erb’s point, is not audible at the apex in the absence of
pulmonary hypertension, and is not heard in the suprasternal notch.
The A2–OS interval usually widens in the upright position, whereas physiologic
splitting of S2 narrows. Exercise-induced tachycardia decreases the A2–OS interval
in patients with significant mitral stenosis, whereas the A2–P2 interval is usually
unchanged.
11.2.10 Maneuvers
Heart sounds and murmurs can be altered by changes in venous return, vascular
resistance, stroke volume, heart rate, and pressure gradients. Maneuvers (Figure
11.7) can be used alter these hemodynamic variables in a predictable manner.
11.3 Phonocardiograph
The technique of listening to the sounds produced by the organs and vessels of the
body is called auscultation. In the early days of auscultation a physician listened to
heart sounds by placing his ear on the chest of the patient, directly over the heart.
The idea of transmitting the heart sounds from the patients chest’s to the physician’s
ear via a section of cardboard tubing was then developed. This was the forerunner of
the stethoscope, which has become a symbol of the medical profession. The stetho-
scope (from the Greek words stehos, meaning “chest,” and skopein, meaning “to
examine”) is simply a device that carries sound energy from chest of the patient to
the ear of the physician via a column of air. Because the system is strictly acoustical,
there is no amplification of sound, except for any that might occur through reso-
nance and other acoustical characteristics.
Instruments for graphically recording the heart sounds have been more success-
ful. The device used to record the phonocardiogram of the heart sounds is called a
phonocardiograph. It is true that a phonocardiogram reveals things that cannot be
294 Diagnosis of Heart by Phonocardiography
detected by a skilled auscultator. Phonocardiography has the ability to render sus-
ceptible to precise measurement time relationships, which hitherto could only be
expressed in vague descriptive terms. It also produces a permanent record of what
11.3 Phonocardiograph 295
Inspiration is associated with increased negative intrathoracic pressure, increased venous return
to the heart, increased right ventricle stroke volume, and decreased pulmonary vascular resis
-
tance. These changes tend to increase the intensity of right-sided S3 and S4 gallops, mitral and
tricuspid clicks, tricuspid and pulmonic stenosis, and tricuspid and pulmonic regurgitation mur
-
murs. Splitting of S2 is also more apparent during inspiration when blood flow into the right
side of the heart is increased and right ventricular ejection is prolonged.
Expiration is associated with decreased negative intrathoracic pressure and reduced venous
return to the heart. Right-sided flow is decreased and splitting of S2 diminishes or disappears.
However, murmurs and sounds originating on the left side of the heart (such as the left-sided S3
and S4, the mitral opening snap, and the murmurs of aortic stenosis and aortic regurgitation)
are accentuated during expiration. In addition, the right-sided pulmonary ejection click may be
appreciated best in expiration.
The Recumbent Position often accentuates the murmurs of mitral tricuspid stenosis.
The Left Semilateral Position can be used to accentuate the mitral opening snap and mitral
regurgitation murmurs, in addition to the left-sided S3 and S4.
Exercise is associated with an increased heart rate, shorter diastole, elevated left atrial pressure,
more abrupt closure of the heart valves, and increased blood velocity. These changes help
account for the increased intensity of S1, S2, the mitral opening snap, the left-sided S3 and S4,
the right-sided S4, and the murmurs of mitral regurgitation, mitral stenosis, and patent ductus
arteriosus, noted in normal patients immediately following exercise.
Prompt Standing shifts blood to lower extremities and reduces left ventricle filling and size. This
maneuver can often be used to accentuate the mitral and tricuspid clicks.
Sitting Up tends to accentuate the tricuspid valve opening snap.
Sitting Up, Leaning Forward tends to bring out the murmurs of aortic and pulmonic regurgita-
tion, in addition to the murmur of aortic stenosis.
Figure 11.7 Maneuvers.
would otherwise be an ephemeral memory or an all-too-often inadequate note in
words or diagrammatic form.
11.3.1 Instrument Description
The phonocardiograph consists of a transducer, filter, amplifier, computer equipped
with a soundcard, and diagnosis software. The computer serves as a display for the
graphical presentations, as a recorder for the phonocardiogram, and as a memory and
disk storage for the documentation data. The computer also offers a way to send the
auscultation data to other computers and printing devices. The phonocardiographic
transducer (microphone or piezoelectric crystal) picks up the sounds within the
patient’s body and transforms them into electrical signals. The transducer is con-
nected to an amplifier, which amplifies the signals to a suitable level and passes them
to the filter. The filter allows only a useful range of frequencies for the soundcard. The
soundcard transforms the signals into the digital domain for the diagnosis software.
The general block diagram of the instrument is shown in Figure 11.8.
11.3.1.1 Transducer
A transducer is a device that converts a nonelectrical quantity into an electrical
quantity. The purposeof the transducer in a phonocardiograph instrument is to con-
vert the sound waves into electrical signals. Many sensing devices are commercially
available for this purpose, including piezoelectric crystals and condensor micro-
phones. Though the first offer good sensitivity, their stability is weak compared to
the latter and they are also expensive. A good transducer is said to have the follow-
ing properties:
•
Extremely high impedance;
•
Excellent frequency response;
296 Diagnosis of Heart by Phonocardiography
Chest wall
Personal
computer
Filter
Amplifier
Microphone
Figure 11.8 Block diagram of a phonocardiograph.
•
Low distortion;
•
Excellent transient response;
•
Inexpensive.
11.3.2 Amplifier
The output voltage of the transducer is quite small and at a high impedance level;
thus, an amplifier having high input impedance and gain is used at the condenser
microphone. The amplifier can be a relatively simple ac amplifier, because a
response to static or slowly varying voltages are not required. Condenser micro-
phones are often used for the first stage of an FET input amplifier.
The output voltage is amplified from the millivolts range to a range in volts that
is equivalent to an amplification factor of 1,000. To provide this gain, a two-stage
noninverting operational amplifier can be used. A typical two-stage amplifier with
an amplification factor of about 50 and 20 can be used. The output of the
preamplifier will have the frequency response for the entire input. To obtain a fre-
quency bandwidth that is limited to that of the heart sounds, the output is passed
through a filter circuit.
11.3.3 Filter
An electrical filter is a frequency-selective circuit that passes a specified band of fre-
quencies and blocks or attenuates signals of various frequencies outside this band.
The requirement of a typical phonocardigraph filter is to attenuate any frequency
below 10 Hz and above 1 kHz. Because the maximum cutoff frequency is only 1
kHz, active filters can be used. An active filter offers the following advantage over
the passive filter:
1. Gain and frequency adjustment flexibility: Because the opamp is capable of
providing a gain, the input signal is not attenuated as it is in a passive filter.
In addition, the active filter is easier to tune or adjust.
2. No loading problem: Because of the high input resistance and low output
resistance of the opamp, the active filter does not cause loading of the source
or load.
3. Cost: Active filters are far more economical than passive filters. This is
because of the variety of cheaper opamps and the absence of inductors.
11.3.4 Analysis
Many pathological conditions that occur in the cardiovascular system surface as
murmurs and aberrations in a phonocardiogram long before they are reflected in
other symptoms, such as changes in the electrocardiogram signal [1]. Furthermore,
modern analytical methods provide new insights into heart sounds and murmurs
and the precise timings of valve movements, firmly establishing auscultation as a
cornerstone of the detection and evaluation of valvular disease and other cardiac
disorders [2]. Auscultation has its demerits as well, because it is a highly subjective
task and depends largely on the experience and training of the observer. Therefore,
11.3 Phonocardiograph 297
extensive training is required for a person to associate a particular heart sound with
a particular diagnosis as well as classifying heart sounds into pathological or inno-
cent sounds. Nonoptimal listening conditions (such as a busy hospital ward), the
coexistence of other sounds or murmurs, a rapid heart rate, or the presence of
noncardiac sounds such as chest wheeze lung sounds can make auscultation of little
help in contributing to a diagnosis [3].
A phonocardiogram (PCG) together with an automatic algorithm assists a phy-
sician to localize and classify heart sounds and murmurs. Cardiac sounds have been
the subject of many kinds of analyses. Frequency analyses of heart sounds have
included application of the Fourier transform, short-time Fourier transform, and the
Stransform [4]. Heart sounds have also been analyzed using parametric and
nonparametric methods [5], as well as energy-based methods [6, 7]. Most of the cur-
rent and past research focused on time segmenting the heart sound or finding
features.
Heart sound segmenting [8] is useful for finding the traditional heart sounds, the
major sounds S1and S2, and the intervals in between—the systole and diastole—but
does not give any clues as to the cause of the sounds. In the case of other advanced
techniques, such as artificial neural networks, wavelet neural networks, and wavelet
transforms, features are extracted from the heart sounds, and then these features are
used to train a neural network to differentiate between pathological and physiologic
sounds.
11.3.5 Segmentation of S1 and S2
Segmentation refers to partitioning of the PCG signal into cardiac cycles, and detec-
tion of the main events (S1, S2, murmur) and intervals (systole, diastole) in each
cycle:
•
The most common approach is to use other signals (mainly ECG) as an exter-
nal reference to identify the beginning of S1. The CP notch may be used to
identify the beginning of S2.
•
Groch et al. [8] used the time-domain properties of the PCG signal (the systole
S1–S2 is shorter and more constant than the diastole S2–S1).
•
Iwata et al. [9] used the frequency-domain properties of the PCG: spectral
tracking at designated frequencies of 100 and 150 Hz for S1 and S2, respec-
tively, by a sliding 25-ms window, and peak detection by differentiating the
tracking levels.
•
Liang et al. [10] used the normalized average Shannon energy to compute the
PCG envelope, and then applied a heuristic algorithm for peak selection and
identification. They improved their method by discrete wavelet transform
(DWT) decomposition and reconstruction.
•
A more modern SP technique, matching pursuit (MP; a generalized wavelet
transform), was applied by Sava et al. [11]. MP is a method proposed by
Mallat and Zhang [12] for adaptive time–frequency decomposition. It decom-
poses the signal into waveforms selected from a dictionary of time–frequency
atoms and selects the atoms that best match the local structure of the signal.
298 Diagnosis of Heart by Phonocardiography
Two methods used for segmentation of PCG are explained in the following
sections.
11.3.5.1 Blind Source Separation
Blind source separation (BSS) [13] denotes observing mixtures of independent
sources, and making use of these mixture signals only—and nothing else—to
recover the original signals. The basic BSS problem assumes instantaneous mixing
of sources, and this is modeled by a linear relation between the observations x and
sources s given by
xAsxRSRAR
mmn
=∈′′∈ ∈
×
,,,
The components of s are assumed to be statistically independent and have prob-
ability distributions that are not Gaussian except for at most one component. To
obtain a unique separation of sources given a set of mixtures, m, the number of
sources is assumed to be less than or equal to n, the number of observations. The
goal of BSS is to estimate a separation matrix W that satisfies
WA PD W R P R D R=∈ ∈ ∈
×× ×
,,,
mn mn mm
where P is a permutation matrix that has one large entry in each of its rows and col-
umns and D is a diagonal matrix. With the separation matrix, the source can be
reconstructed with
yx= W
Learning W by observing x merely requires us to make use of higher order sta-
tistics (HOS). Typically, the separating matrix W is calculated iteratively by opti-
mizing some cost function of the source estimates y [14]. Currently reported
approaches to this problem can be divided into two categories. One category makes
use of HOS explicitly [15, 16], and the other category makes use of HOS implicitly
through the nonlinearity of neurons in a neural network [17–19]. Bell and
Sejnowski [13] showed thatanother criterion for BSS can be the mutual information
among the output y components.
11.3.5.2 Complexity-Based Gating
Simultaneous recordings of S2 from two acoustic sensors will contain other heart
sounds, including S1 sounds. To extract S2 from two simultaneous PCG recordings,
a simplicity-based gating scheme can be used. The simplicity-based gating scheme
assumes that the human heart behaves like a hidden dynamic system whose evolu-
tion in time gives rise to the PCG. Because the complexity of the generated PCG is
directly proportional to the complexity of the hidden dynamic system [20], major
heart sounds such as S1 and S2, which are simpler in appearance, are generated
when the hidden dynamic system exhibits a lower number of states (modes). By
measuring the number of states (complexity) of the hidden dynamic system over
time, the time intervals where S1 and S2 occur can be isolated, because during these
11.3 Phonocardiograph 299
intervals the PCG will exhibit lower complexity (higher simplicity). The details of
the algorithm to measure simplicity of the PCG over time are given in Nigam and
Priemer [20]. The simplicity profile of a PCG peaks at times when S1 and S2 occur
and it is subsequently thresholded to identify these time intervals.
While comparing the simplicity-based gating scheme with a conventional gating
scheme that uses Shannon energy, some noteworthy points were observed. The con-
ventional energy-based gating method yields a time gate of duration, T
e
, which gates
only a portion of S2, the simplicity-based gating method gives a time gate of dura-
tion T
s
, which encompasses most of the S2. This is because the energy-based gating
method relies on the amplitude of the signal being gated and tends to gate only that
portion of the signal that has significant amplitude. On the other hand, the simplic-
ity-based gating method is invariant to amplitude fluctuations and depends only on
the inherent simplicity of the signal (the number of modes of the hidden dynamic
system).
Another advantage of using simplicity-based gating is that simplicity values of
S1 and S2 are almost similar in all PCG recordings irrespective of their relative
amplitudes, whereas their energy values depend on their absolute amplitudes.
Therefore, when either of S1 or S2 is relatively weak in amplitude as compared to
the other, simplicity-based gating will still gate both of them correctly, whereas
energy-based gating might lose the sound with weaker amplitude.
11.3.6 Decomposition of PCG
A typical PCG can be decomposed by either parametric or nonparametric methods.
Some of the parametric methods are autoregressive (AR), autoregressive moving
average (ARMA), and adaptive spectrum analysis. Nonparametric method can be
further classified into linear or quadratic where short-time Fourier transform
(STTF) and continuous wavelet transform (CWT) come under the earlier one and
Wigner-Ville distribution (WVD) and Choi-Williams distribution (CWD) are
grouped under the later case.
In nonparametric methods, certain parameters in the analysis routine have to be
set. For example, the spectrogram’s window duration and degree of smoothing
(cross-term reduce) in CWD. If a signal is a linear combination of some frequency
components, its linear TFR is the same linear combination of each individual com-
ponent. The STFT is a linear TFR that has been widely studied and used. A more
recent linear TFR is the CWT. STFT suffers from a trade-off between time and fre-
quency resolutions: The shorter analysis windows produce better temporal resolu-
tion, but degrade the spectral resolution.
Quadratic or bilinear TFRs can be expressed in a general form proposed by
Cohen [21], where the particular distribution is defined by a kernel function of the
frequency and the time lag. WVD might add artifact cross-terms to the TF represen-
tation of multicomponent signals, which can make the analysis difficult. CWD
belongs to reduced interference distributions (RIDs), which reduces cross terms
between components.
With parametric methods, the investigated signal is first modeled by an equiva-
lent system, and then the parameters of the model are estimated from measurements
of the signal made over a limited period of time. In the AR model, the current output
300 Diagnosis of Heart by Phonocardiography
of the system depends only on the values of previous outputs. In the ARMA model,
the output depends on previous outputs and inputs. In adaptive signal decomposi-
tion, the largest amplitude component is identified (via STFT), reconstructed, and
subtracted from the original signal to form a residual signal. The STFT is reapplied
to the residual signal, and the procedure is repeated. MP is another adaptive spec-
trum analysis method that has been applied to heart sound decomposition.
11.3.7 Short-Term Fourier Transform
The FFT can provide a basic understanding of the frequency contents of the heart
sounds. However, FFT analysis remains of limited value if the stationary assump-
tion of the signal is violated. Because heart sounds exhibit marked changes with
time and frequency, they are therefore classified as nonstationary signals. To under-
stand the exact feature of such signals, it is thus important, to study their time–
frequency characteristics. To gain information from both the time and frequency
domains, Dennis Gabor [22] adapted the Fourier transform to analyze only a small
section of the signal at a time. This adaptation is called the short-time Fourier trans-
form. The STFT is formally defined by the integral transform:
() ()( )
Xf xtwt e dt
T
jft
T
T
=−
−
−
∫
τ
π2
2
2
/
where x(t) is the signal, w(t) is the conjugate of the window function, T is duration,
and τ is the time location.
STFT maps a time signal into a 2-D time–frequency plane. The time and fre-
quency information can only be obtained with limited precision, as determined by
the size of the window. As the window function (time domain) gets narrower, the
localization information in the frequency domain gets wider, and vice versa. In
addition, once the window function is chosen, the STFT uses the fixed time–
frequency window to analyze the whole signal (Figure 11.9), making it inaccurate
for signals having relatively wide bandwidths that change rapidly with time. If a
high degree of accuracy is required, the STFT must be repeatedly applied to the sig-
nal with a varying width each time. Due to the assumption of stationarity of the sig-
nal within the time interval of the window, the length of the stationary interval
determines the time and frequency resolution.
In fact the spectrogram (STFT) cannot track very sensitive sudden changes in
the time direction. To deal with these time changes properly, it is necessary to keep
11.3 Phonocardiograph 301
Amplitude
Frequency domain (Fourier)
Frequency
Time
STFT (Gabor)
Time
Wavelet analysis
Frequency
Frequency
Figure 11.9 The windows of FT, STFT, and WT. (From: [7]. © 1998 IEEE. Reprinted with permis
-
sion.)
the length of the time window as short as possible. This, however, will reduce the
frequency resolution in the time–frequency plane. Hence, there is a trade-off
between time and frequency resolutions [23]. A typical STFT of a normal PCG for a
single cardiac cycle is shown in Figure 11.10.
11.3.8 Continuous Wavelet Transform
The CWT returns a coefficient that is a measure of the similarity between the wave-
let at a particular scale and time point and the signal at that instance. The coefficient
is calculated by scaling and shifting the basic wavelet Ψ to cover the whole signal:
()
C
a
xt
tb
a
dt a
ab,
,=
−
⎛
⎝
⎜
⎞
⎠
⎟
≠
−∞
∞
∫
1
0Ψ for
where
Ψ()t
is the conjugate of the basic wavelet, a is the scale factor, b is the transla-
tion factor, and x(t) is the signal to be analyzed.
The introduction of a scale parameter in CWT makes the time–frequency win-
dow flexible by allowing dilation and compression of the wavelet. This concept of
scale gives CWT an optimized time–frequency resolution: good time resolution at
high frequencies and good frequency resolution at low frequencies. This property
makes wavelets useful in the detection of short-time transients.
Scale a is related to the frequency by the following expression:
F
F
a
a
c
=
Δ
where a is a scale, Δ is the sampling period, F
c
is the center frequency of a wavelet in
hertz, and F
a
is the pseudofrequency corresponding to scale a, also in hertz.
302 Diagnosis of Heart by Phonocardiography
0 0.5
0
50
100
150
200
250
300
350
400
450
500
STFT of a PCG
Time (sec.)
Frequency (Hz)
1
1.5
2
2.5
Figure 11.10 STFT of a PCG for one cardiac cycle.
A lower scale a results in a more compressed wavelet, which is used to analyze
rapidly changing details. On the other hand, a higher-scale a results in a stretched
wavelet, which is used to analyze coarse features.
Owing to the fact that windows are adapted to the transients of each scales, a
wavelet lacks the requirement of stationarity. Hence, a WT is better suited to ana-
lyzing nonstationary signals than STFT. Because heart signals are transient signals,
the WT is the technique chosen in this research.
11.3.8.1 Resolution
The resolution in time Δt and the resolution in frequency Δf for both the STFT and
WT are bounded by Heisenberg’s principle of uncertainty:
ΔΔft≥
1
4π
Only windows with a Gaussian shape satisfy the principle of uncertainty with
an exact equality [24, 25].
11.3.9 Wigner Distribution
The subject of a time-frequency signal description is motivated by the need 5m of
describing the fraction of total energy of a signal at a time t and frequency f. In light
of this aim, the Wigner distribution was first proposed by Ville and hence is com-
monly referred to as the Wigner-Ville distribution [21]:
() ()()
Wt e s t st d
j
,*ωτττ
ωτ
=−+
−
−∞
∞
∫
12 12
The Wigner distribution is particularly useful because of its high resolution and
its ability to satisfy a great number of desirable time-frequency properties. These
clear advantages are offset by its poor performance when applied to
multicomponent signals. Applying the Wigner distribution to a signal with two dis-
tinct components results in interference terms that appear in time and frequency
where a true signal does not exist. Although it is generally accepted that these
cross-terms contain signal information, their interpretation and significance are not
as yet fully understood. The Wigner distribution of a typical PCG for a single
cardiac cycle is shown in Figure 11.11.
From the WVD the instantaneous frequency of the signal can be derived that
helps us classify heart sound and murmurs. Figure 11.12 shows the instantaneous
frequency of a PCG for a single cardiac cycle.
11.3.10 The Choi-Williams Distribution
After the discovery of the Wigner distribution, a number of other distributions with
various advantages were developed. These distributions showed common desirable
properties, such as satisfying the margins, and all of them present a reasonable
time–frequency (T-F) description of a signal. The appearance of this large array of
11.3 Phonocardiograph 303