Download An Electronic Stethoscope with Diagnosis Capability

An Electronic Stethoscope with Diagnosis Capability
Wah W. Myint
Bill Dillard
Department of Comp. Science and Software Eng.
Auburn University, College of Engineering
Auburn, AL 36849 USA
Department of Electrical & Computer Eng.
Auburn University, College of Engineering
Auburn, AL 36849 USA
Abstract— A means of analyzing acoustic heart sounds is
described. The system analyzes heart sound data, isolating
key features of the heartbeat signatures to produce a list of
possible diagnoses. The system has six major components:
heart beat extraction, segmentation of the heart sound cycle,
extraction of murmur data, time-frequency analysis of
murmurs, statistical metrics and diagnoses algorithms.
Implementation of each component is discussed in detail.
Diagnoses algorithms for the following events/conditions
are included: heart rate, sinus arrhythmia, tachycardia,
bradycardia, aortic stenosis and mitral regurgitation.
blood can move through the smaller opening. If a valve
does not close tightly, blood may leak backward, or
regurgitate. In auscultation, these conditions are called
murmurs and can occur in either systole or diastole, as
seen in Fig. 1b.
Stenosis occurs when a valve does not open
completely. The valve may have become hardened or stiff
with calcium deposits or scarring, so it is hard to push
open. Blood has to flow through a smaller opening, so less
blood gets through the valve into the next chamber.
Insufficiency (also called regurgitation) results when
the valve does not close tightly. The valve's supportive
structures may be loose or torn. Or the valve itself may
have stretched or thinned. Blood then may leak back in the
wrong direction through the valve.
I. INTRODUCTION
It has been reported that a disturbing percentage of
medical school graduates cannot properly use a
stethoscope for diagnosing common heart conditions [1].
This is true despite the availability of computerized
training modules that provide acoustic and visual display
of heart sounds [2,3]. Furthermore, many physicians rely
heavily on ECG and EKG specialists, leading to higher
health care costs and a general decline in stethoscope
skills.
In this work, we consider analyzing heart sounds
using time-frequency techniques, beginning with
auscultation and moving towards diagnosis. Results will
be applied to an electronic diagnostic stethoscope (EDS)
suitable for medical school training.
S1
S2
Sys tole
0
0.25
Dias tole
0.5
0.75
S2
Sy stole
1
1.25
1.5
1
1.25
1.5
tim e (s ec)
II. HEART SOUNDS AND MURMURS
The heart is divided into four chambers. The upper
chambers are called atria and the lower chambers are
called ventricles. The heart muscle squeezes blood from
chamber to chamber. At each squeeze, the valves open to
let blood through to the next chamber. Then the valves
close to stop blood from moving backward. In this way,
the valves keep blood moving as efficiently as possible
through the heart and out to the body.
Under normal heart conditions, there are basically two
heart sounds, S1 and S2, shown in Fig. 1a. S1 sound
corresponds to the near simultaneous closure of the mitral
and tricuspid valves after blood has returned from the body
and lungs. This is the start of systole. The S2 sound,
signaling the end of systole and the beginning of diastole,
is created by the closing of the aortic and pulmonic valves
as blood exits the heart to the body and lungs.
Valvular disease occurs when a valve does not work
the way it should. If a valve does not open all the way, less
S1
(a)
0
Sys tolic
Dias tolic
Murmur
Murmur
0.25
0.5
0.75
tim e (s ec)
(b)
Fig. 1. Representative heart sounds for (a) a normal heart and (b) a heart
with both systolic and diastolic murmurs.
III. THE ALOGRITHMS
A. Heart Rate Issues
The system is capable of extracting heart rate as well as
diagnosing abnormal heart rate conditions:
sinus
arrhythmia, bradycardra and tachycardia. Acoustic heart
sound data is acquired by a microcomputer sound card and
stored as a wave table file. The algorithm for extracting the
heart rate is diagrammed in Fig. 2a and begins with the
normalization of the data file such that the maximum value
is unity. Next, the envelope is detected and compared
against a pre-selected hysteresis window, as shown in Fig.
2b. Upon crossing the positive threshold, the sample index
number at the envelope maximum is found. When the
envelope falls below the negative threshold, the index is
stored. This technique rejects noise in the envelope. This
is repeated for three envelope peaks, providing either a S1S2-S1 or a S2-S1-S2 timing sequence. In either case the
first and last stored times provide the heart rate through the
equation
60 f S
(1)
∆n
where fs is the sampling frequency, ∆n = n3 – n1 is the
number of samples in the heart beat cycle and HR is the
heart rate in beats per minute.
Three heart rate conditions are combined into the single
algorithm diagrammed in Fig. 3. The first condition, sinus
arrhythmia, is characterized by a heart rate within normal
limits but irregular [4]. It is detected by comparing the
duration of adjacent heart beats. If the ratio (n3-n2)/(n2-n1)
falls where a pre-determined window, then the heart beat
pair is regular and the Normal Beat Counter (NBC) is
incremented. Otherwise, the Sinus Arrhythmia Counter
(SAC) is incremented. The ratio of the counters provides
an indication of sinus arrhythmia.
HR =
nmax = 0
max = 0
Read next
data value
data > pos.
threshold?
no
yes
data > max?
nmax = n
max = data
yes
no
data < neg.
threshold?
no
Get n1 n2, n3
yes
DONE
Calculate ∆n
(a)
normaliz ed magnitude
1.25
∆n = too large;slow HR
n2
n1
1
n3
positiv e
Increment
BC
no
threshold
0.75
yes
∆n = too small; fast HR
negativ e
yes
threshold
Increment
TC
0.5
0.25
0.5 <= ∆ni /∆ni-1< 1.5
yes
Increment
NBC
no
0
0
0.5
1
time
1.5
2
(b)
Fig. 2. Extracting the heart rate relies on finding the index number,
nmax, of the envelope peaks. The flowchart (a) describes how a single
peak index number is determined. Hysteresis (b) is employed to reject
noise in the envelope data.
Increment SAC
DONE
Fig.3. A flowchart describing how a series of counters are used to detect
heart beat related conditions.
The phenomenon of bradycardia is characterized by a
heart rate below 60 beats per minute and a regular rhythm
[5]. It is easily detected by monitoring the heart rate. If a
heart beat duration is too long, the Bradycardia Counter
(BC) is incremented. The nearness of the ratio BC/NBC to
unity indicates the severity of the bradycardia.
At the opposite end of the heart rate spectrum, in
tachycardia, the heart rate exceeds 150 beats per minute
[5]. In a manner similar to the bradycardia algorithm, the
Tachycardia Counter (TC), in conjunction with the NBC,
is used to diagnose this condition.
B. Segmentation of the Heart Cycle
Before murmur analysis can begin, the systolic and
diastolic phases must be delineated. This process is called
segmentation. True segmentation based solely on acoustic
data is a difficult procedure [6,7]. In fact, many researchers
use ECG recordings to identify the S1 sound [8,9]. In this
work, a simple approach is used based on the peak index
numbers, n1, n2 and n3 defined in Fig. 2a. Generally, the
systolic phase is shorter than the diastolic. This leads to the
segmentation scheme described in (2).
If
n3 − n 2
> 1 then n1 is S1
n 2 − n1
If
n3 − n 2
< 1 then n1 is S 2
n 2 − n1
(2)
As heart rate increases, the duration of the diastolic
phase decreases while the systolic phase is relatively fixed.
At very high heart rates the inequalities in (2) may reverse.
This is the weakness of such a simple segmentation
scheme and the impetus for using ECG in segmenting.
fully, leading to abnormally high pressure in the left
ventricle. The time-frequency signature for AS, shown in
Fig. 4a, exhibits a crescendo-decrescendo in magnitude at
relatively uniform frequency across the systolic phase.
In mitral regurgitation (MR), the mitral valve does not
completely close during systole, allowing blood to flow
backwards into the left atrium. As shown in Fig. 4b, MR
exhibits relatively uniform magnitude and frequency
during systole.
Unfortunately, it is common for murmur characteristics
to vary between heart cycles. Additionally, the S1 and S2
sounds often dominate the murmurs. It is vital, therefore,
that a time-frequency analysis be performed on each
systolic and diastolic murmur with S1 and S2 sounds
rejected [11]. To this end, a separate data file will be
created for each murmur event. Referring to Fig. 2a, when
the envelope falls below the negative threshold, a new file
can be opened and data written until crossing the positive
threshold, where the file is closed. This will isolate the
murmur from S1 and S2. The segmentation results form
(2) identify the murmur phase – systolic or diastolic. In
this way, murmur files are created sequentially, alternating
between systole and diastole.
The time-frequency analysis is performed using the
specgram function in MATLAB, which produces a local
spectrum versus time. A representative spectrograph for
an AS murmur is shown in Fig. 5, where magnitude is
displayed in grayscale. The characteristic crescendodecrescendo pattern is readily apparent. A spectrograph is
S1
s ys tole
S2
C. Time-Frequency Analysis Of Murmur Sounds
At the beginning of systole, the mitral and tricuspid
valves close, causing the S1 sound. The ventricles squeeze,
forcing blood through the aortic and pulmonary valves into
the atria and on to the body and lungs. When ventricular
pressure drops, the arotic and pulmonary valves close,
causing S2, signaling the end of systole and the beginning
of diastole. During diastole, blood reenters the atria. The
increased pressure opens the mitral and tricuspid valves,
allowing the ventricles to refill. Valves that do not open
fully cause reduced flow, whereas, those that do not close
properly, allow blood to flow backwards. In either case,
the faulty valve causes turbulence, producing murmurs.
Some murmurs have characteristic time-frequency
signatures that can be used for identification and diagnoses
[10]. In this work, two specific systolic murmurs are
selected for analysis, aortic stenosis and mitral
regurgitation. In aortic stenosis (AS), the aortic valve is
thickened and narrowed. As a result, it does not open
time (sec)
(a)
S1
s ys tole
S2
time (sec)
(b)
Fig. 4. Representative waveforms for (a) aortic stenosis and (b) mitral
regurgitation demonstrate their time-frequency characteristics.
Perform spectrogram
Calculate statistics:
(peak magnitude, frequency at peak,
average magnitude,
standard deviation of magnitude)
for each localized spectrum.
Calculate the averages and
standard deviations of the peak
magnitudes and their frequencies
across the murmur duration
Small standard deviations and
reasonable average values
indicate MR
Fig. 6. A procedure of analyzing spectrogram data for the occurrence of
mitral regugitation within a single murmur.
produced for each murmur data file and statistical
characteristics, such as peak magnitude, frequency at peak
magnitude, average and standard deviation are calculated
for each local spectrum. These statistics are used to
produce diagnoses of AS and MR.
D. Murmur Diagnoses
As seen in Fig. 4b, mitral regurgitation is characterized
by the uniform frequency and magnitude during the
systolic phase. The severity of MR is indicated by the
magnitude relative to S1 and S2. Thus, a reasonable
procedure for diagnosing MR is diagrammed in Fig.6.
From the spectrograph data, the averages and standard
deviations of the peak spectral component and its
corresponding frequency are calculated for the duration of
the murmur. If the standard deviations are small, and the
average frequency is reasonable, then MR is diagnosed,
with the average of the peak frequency component
indicating severity.
The same procedure can be used for characterizing the
frequency of the aortic stenosis murmur sounds. However,
referring to Fig. 4a., it is obvious that a different approach
is required to identify the crescendo-decrescendo nature of
the magnitude. As shown in Fig. 7, the time at which the
maximum magnitude occurs is first extracted. Separate
least square fits are performed on the magnitude-time data
on both sides of the peak and the resulting slopes and
correlation parameters are stored. Based on the slopes,
correlation parameters and peak magnitude, numerical
values can be assigned to the possibility and severity of
AS. After each murmur event has been analyzed, the
results are tabulated and evaluated for consistency across
M axim um P eak
Murmur Wavef orm
Fig. 5. A spectrogram for a representative aortic stenosis murmur
indicating the crescendo-decrescendo, uniform frequency
nature of the data.
Cres cendo
Dec res cendo
LSF
0.2
LSF
0.3
time (s)
0.4
Figure 7. A simple procedure for diagnosing AS relies on its crescendodecrescendo nature at relatively uniform frequency.
the entire auscultation period, yielding a final diagnosis of
heart conditions.
IV. RESULTS
To test the algorithm, a wave table file of acoustic data
obtained from a patient with mitral regurgitation was
segmented and a single murmur isolated for analysis.
From the spectrogram of the murmur is shown in Fig. 8,
where the murmur duration is divided into 10 time
segments. In each time segment, the peak spectral content
and its frequency are extracted. Mean and standard
deviation are then calculated across the murmur duration.
Results, listed in Table I, show peak magnitude and
frequency values, consistent with the mitral regurgitation
condition. Time segments 1 and 10 contain S1-S2 artifacts
and have been omitted in the analysis.
Algorithms for aortic stenosis were also conducted on
an isolated AS murmur. The murmur envelope was
normalized and least square fits were obtained on both
sides of the maximum peak. Results are shown in Figure 9
and Table II. Correlation parameters of 0.857 and –0.829,
in conjunction with the slope magnitudes, are compatible
with aortic stenosis.
V. CONCLUSIONS
The initial work towards an electronic diagnostic
stethoscope has been reported. Methods for analyzing
acoustic heart sound data have been presented, including
algorithms for extracting heart rate and diagnosing raterelated abnormalities. Additional algorithms for murmur
analysis, specifically mitral regurgitation and aortic
stenosis have been demonstrated. Future tasks include data
acquisition hardware design, software integration and
graphic user interface (GUI) development.
VI. ACKNOWLEDGEMENTS
Fig. 8. Spectrograph of a murmur from a patient with mitral
regurgitation.
TABLE I
STATISTICS FOR THE MITRAL REGURGITATION
SPECTROGRAPH IN FIG. 8
Stat
Max.
Freq.
Time Segment
2
3
4
5
6
7
8
0.46 0.49 0.42 0.81 0.60 0.71 0.68
215 118 129
11
215
97
129
Full Murmur
9
Mean STD
0.72 0.61 0.14
86
125
67
Fig. 9. The algorithm for detecting aortic stenosis performs least
squares fits to the normalized murmur envelope before and after peak
magnitude
TABLE II
LEAST SQUARES FIT FOR THE
AORTIC STENOSIS MURMUR IN FIG. 9
Slope
Intercept
Correlation (r)
Before
10.549
0.519
0.857
After
-4.371
0.913
-0.829
The authors gratefully acknowledge Dr. Jennifer Chu
of University of Pennsylvania Medical Center, Mr. Zaw
Maung of Nortel Networks and Dr. Gerry Dozier of
Auburn University for their medical and technical
contributions to this work.
VII. REFERENCES
[1]
S. Mangione, L. Nieman, “Cardiac aucsultatory skills
of internal medicine and family practice trainees,”
Journal of the American Medical Association, vol.
278, pp 717 – 722, 1997.
[2] RALE: Lung and Heart Sound CAI Software,
Published by PixSoft. Inc.
[3] CardioSim® Digital Heart Sound Simulator,
Published by Cardionics, Inc.
[4] J. Constant, Bedside Cardiology, Boston, MA: 1993,
pp. 143.
[5] L.M. Porterfield, ECG Fundamentals, Springhouse
Corporation, Springhouse, PA: 1997, pp 65.
[6] H. Liang, S. Lukkarinen, I. Hartimo, “Heart sound
sementation algorithm based on heart sound
envelogram,” in Computers in Cardiology, 1997, pp.
105-108.
[7] H. Liang, S. Lukkarinen, I. Hartimo, “A boundary
modification method for heart sound segmentation
algorithm,” in Computers in Cardiology, 1998, pp.
593-595.
[8] B. Altrabsheh, J.N. Torry, “Detecting the split within
heart sounds using a switched-capacitor filter,” in
Computers in Cardiology, 1998, pp. 597-600.
[9] M.W. Groch, J.R. Domnanovich and W.D. Erwin,
“A new heart-sounds gating device for medical
imaging,” IEEE Trans. on Biomedical Engineering,
vol. 39, 1992, pp. 307 – 310.
[10] B. Tovar-Corona, J.N. Torry, “Graphical
representation of heart sounds and murmurs,” in
Computers in Cardiology, 1997, pp. 101 – 104.
[11] A. Haghighi-Mood, J.N. Torry, “Time-frequency
analysis of systolic murmurs,” in Computers in
Cardiology, 1997, pp. 113 – 116.