Frequency Korotkoff

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TIMEFREQUENCY ANALYSIS OF

KOROTKOFF

SOUNDS

J.Allen

and

A.Murray

Introduction

Five Korotkoff phases

are

classically described when listening to

sounds

obtaiied from a stethoscope placed over

the brachial

artery

during deflation of a pressure cuff from supra-systolic pressure’. The Korotkoff sounds have

characteristic features and have been described

as;

phase 1 the onset of tapping sounds, phase

2

a

murmur

following each tapping sound, phase

3

reappearance of only the tapping sound, phase

4

‘muffling’, and phase

the disappearance of sounds2.Phase 1 is important in the determination of the systolic blood pressure and

phases

4

and

in

the determination of the diastolic blood pressure. Several hypotheses have been proposed

as

to the nature

of theKorotkoff ounds

ncluding; turbulence, cavitation, arterial

‘flapping’,

and water

hammer’.

Studying the transitions in the spectral properties of the sounds for each of the phases may contribute towards

a better understanding of their nature.

Traditional frequency analysis using spectrograms obtained using Fourier transforms indicated that the major

frequency components

of

Korotkoff sounds fall in a band ranging approximately from 20 Hz o

300 Hz,

with

significant peaks at

40 90

and 150

H2 .

These key frequency componentsare generalisations and do not consider

the subtle and complex changes in frequency and amplitude which contribute towards the resultant sound. This

complexity is indicative of non-stationary properties and it may, therefore, be more appropriate to use a joint

time-frequency analysis4

JTFA)

approach rather than the conventional Fourier methods for spectral analysis.

Traditionally, the time-frequency behaviour

of

non-stationary signals has been studied using short time Fourier

transforms

STFT),

but now advanced techniques such as the exponential distribution ED) have been proposed.

The ED technique is popular, partly because it can produce much better time and frequency resolutions than the

STFT approach, and it has also been applied in studying a wide range of biomedical signal^^ ^ In this study

an

exponential distribution time-frequency algorithm5has been implemented and used to study the time-frequency

behaviour

of

Korotkoff sounds obtained fiom a normal volunteer.

The aims

of

this work are to determine if the exponential distribution can be implemented in order to study the

time-frequency behaviour of Korotkoff sounds, and also to identify key features in each of the different phases

of the Korotkoff sounds.

Methods

Recording

Sounds were measured using an adapted cardiology stethoscope fitted with a microphone, and the signal was pre

amplified, and low pass filtered before data capture by computer. The

arm

cuff pressure was also captured to

computer with the microphone sounds at a frequency of

2500

Hz.

Figures la and b show the captured recordings

over a

1

minute period for the cuff pressure and sounds respectively. Here, the pressure calibration marker 100

mmHg), cuff inflation and deflation stages can clearly be identified along with the corresponding sounds

produced. It is the Korotkoff sounds seen at between

26

and

36

seconds that are extracted and highlighted in

Figure

IC.

Figure Id is a magnificationof the cuff pressure trace during these Korotkoff sounds.

The authors are with the Regional Medical Physics Department, Freeman Hospital, Newcastle upon Tyne.

The Institutio n of Elec trical Engineers.

and publish ed by the IEE, Savoy Place, London

WCPR

OBL, UK.

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Pre-processing of Korotkoff sounds

In total

12

Korotkoff sounds were identified from the

60 s

recording from between

26

and

36

seconds. From

these, 4 were considered to represent the Korotkoff phases to 4. Each of the4 ounds were placed into epochs

of length

2“)

where the mean value was subtracted typical data lengths were either

5 12

samples

0.2048

s) or

1024 samples 0.4096 s .Next, the analytic version of the signal was obtained by taking the Fourier transform

of

the data, multiplying the positive harmonics by

2,

the negative harmonics by

0,

and then taking

its

inverse

Fourier transform.

hhponential distribution time-frequency analysis

A running window form of the exponential distribution RWED) has been implemented and is fully described

by Ma y . The functionality

of

the algorithm was

fust

verified using

an

assortmentof simulated test signal?

and

then used to obtain time-frequency distributions for the Korotkoff

sounds.

The tuning parameters used for the

algorithm were; N symmetrical sliding window determining frequency resolution), M rectangular window

determining the range of the time-indexed auto-correlation function), and sigma

U, a

key parameter in

determining cross-term interference rejection). The parametersN, M, and Uwere finally chosen to be

5 12,8,

and

0.4

respectively where good frequency resolution, time resolution and reduced cross4erms were observed.

Time-Pequency visualization

The time-frequency distributions produced by the RWED algorithm were visualized using the graphics facilities

available with MATLAB

software.

For clarity, contour plots were produced with

25

contour levels normalised

for the maximum and minimum levels of the time-frequency distribution. The contour plots allow changes in key

frequency components to be observed.

Results

Figure 2 shows contour plots of the time-frequency distributions for 4 selected sounds extracted from the sound

recording. The four selected sounds are identified on Figure

IC

and

are

named in sequential order

as

sound

Sl) ,

sound

2

S2) ,

sound

3

S3)

and sound

4

S4).

These sounds may be representative of the Korotkoff phases

1,2,

3 and 4, respectively. The transitions

of

all 4 sounds with respect to time can be seen. The sound

S1

is a very

short duration ‘tap‘ with frequencies concentrated below 300 Hz dominant frequency 100 Hz .

S2 is

a larger

amplitude signal with 3 frequency components about 10 ms apart with frequencies concentrated below 200 Hz

dominant frequency60 Hz .Similarly

S3

is a large amplitude signal with frequencies concentrated below 200

Hz

dominant frequency 110

Hz ,

nd

S4

is a lower level signal with frequencies concentrated below

300 Hz

dominant frequency 150Hz .The rapid change in frequency components following the

start

of S4 tie in well

with the characteristic definition of the Korotkoff phase 4 ‘muffling’ of sounds, with frequency components

of

less than 200

Hz.

iscussion

The application of the RWED algorithm is appropriate for analysing the complex non-stationary Korotkoff

sounds. The algorithm was relatively straightforward to implement, although it proved to be computationally

demanding, especially for long data sets from signals sampled at a high frequency. It

is

perhaps more suited to

‘off-line’ signal processing.

The

preliminary study described in this paper suggests that the 3 tuning parameters

N,

M and

a

allow effective tailoring

of

the algorithm for Korotkoff sound analysis. The time resolution obtained

showed detail of the complex time-frequency distribution for the signal.

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It is important to consider the measurement system and protocol used in obtaining the Korotkoff sounds. The

specially adapted stethoscope with microphone allows sounds to be obtained which are perhaps closer to how

the human observer would hear them. Furthermore, care must also be taken to prevent aliasing since higher audio

frequencies will be present if effective anti-alias filtering is not used prior

to

data capture. The measurement

protocol needs to be rigorously followed

so

that artifact is reduced, e.g. subjects should be relaxed and breathing

normally, and also the cuff pressure must be deflated at a rate which allows a sufficient number of Korotkoff

sounds to be recorded.

The techniques used in visualization

of

the time-frequency distributions are also important since it is possible

to significantly alter the time-frequency landscape. For this study, simple contour plots proved to be effective.

The results presented here are mainly visual, but future work would include developing quantitative measures

to

summarize the graphical information representing the Korotkoff sound time-frequency distributions.

Conclusion

In this evaluation study the running window exponential distribution time-frequency approach shows promise

for studying the time-frequency behaviour of Korotkoff sounds, and also for identifying key features in each of

the different phases of the Korotkoffsounds.Further work is now needed to evaluate the technique when applied

to a larger number of volunteers, to optimise protocols for Korotkoff sound recording, and to assess different

approaches in time-frequency distribution visualization

References

1

2.

3.

4.

5

6.

GeddesL A, Hoff H E, and Badger A S: “Introduction of the auscultatory method of measuring blood

pressure including a translation of Korotkov’s original paper”, Cardiovascular Research Centre

Bulletin, Vol5, pp 57-74, 966.

Constant

J:

“Bedside Cardiology” 3“‘ Edition, Little, Brown and Company:Boston), pp 54-65, 985.

Webster J G: “Medical Instrumentation: Application and Design”, 2“dEdition, Houghton Mifflin:

Boston), pp

394-9, 992.

Qian S and Chen D: “Joint Time-Frequency Analysis Methods and Applications”, Prentice Hall:NJ),

1996.

Akay M: “Time-frequency epresentations

of

signals”,Detection and Estimation Methods for Biomedical

Signals, Academic Press, Califomia), pp 1 1 1 156, 1996.

Lin ZY nd De Z Chen J: “Time-frequency representation of the electrogastrogram application of

the exponential distribution”,IEEETrans BME Vol41,pp267-75, 994.

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Frequency Korotkoff

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