SPECTRAL ANALYSIS OF FIRST AND SECOND HEART SOUNDS BEFORE AND AFTER MECHANICAL HEART VALVE

IMPLANTATION

H. P. Sava', J. T. E. McDonnelll and K.A.A. Fox2 'Department of Electrical Engineering,' Cardiovascular Research Unit,

The University of Edinburgh, King's Buildings, Mayfield Road, Edinburgh EH9 3JL, U. K.

ABSTRACT This paper investigates the difference in spectral com-

position of first (Sl) and second (S2) heart sounds before and after mechanical prosthesis implantation. The frequency spectrum is obtained using the forward- backward overdetermined Prony's method (FBPM). It is shown that the difference in spectra before and after mechanical valve implantation yields useful information regarding the frequency characteristics of the heart- chest system and the valve itself.

INTRODUCTION Spectral phonocardiography (SPCG) is an effective

method of detecting malfunction in mechanical heart valves [2]. This is based on the fact that any significant alteration in valve properties causes changes in the spec- tra of the opening and closing sounds. However, the ma- jor difficulty encountered in externally recorded SPCG is that of differentiating resonance characteristics related to mechanical valve closure from those of the heart- thorax system [5]. This paper is concerned with the changes in the spectrum of recorded phonocardiograms (PCG) before and after mechanical heart valve implant- ation. This method allows the separation of those reson- ance -components which are related to mechanical valve closure from those related to the heart-thorax system. An advanced spectral analysis method based on FBPM is used and a new dynamic estimation of the model or- der based on an analysis/synthesis process is proposed.

MATERIALS AND METHOD

Data Acquisition: The electrocardiogram (ECG) and PCG of 33 patients, aged between 40 to 72 years were re- corded one day before and four to six days after mechan- ical heart valve implantation. Three of these cases had a leaky prostheses which was subsequently replaced. A Hewlett-Packard (21050A) contact microphone was used to record the PCG. The PCG was preprocessed using a third-order high-pass Butterworth filter with a cutoff frequency o€ 50Hz and a sixth-order low-pass filter with a cutoff frequency of 2kHz. The analysis presented in this paper concentrates on that part of the spectrum

up to 2kHz where the major distribution of spectral en- ergy occurs for both native heart valves and mechanical prostheses. The ECG and PCG were then digitised to 12-bits at a sampling rate of 5 kHz. Spectral analysis: An ensemble average was taken of S1 and S2 respectively. This process was achieved auto- matically using cross-correlation with a known S1 and S2 template. Only sounds achieving a cross-correlation of 80% or more were admitted into the ensemble aver- age. Figure 1 gives an ensemble of S1 for a patient before and after implantation.

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Figure 1: (dashed line) valve implantation.

An ensemble of S1 before (full line) and after

The average ensemble sound was then processed using the FBPM. This method uses the singular value decom- position (SVD) of an augmented-order matrix which di- minishes the effect of noise in both the data vector and the data matrix [4] [5]. This method is very accurate for analysing short transient signals composed of decayed sinusoids such as S1 and S2 and provides a full para- metrization of the spectrum regarding the amplitude, frequency, phase and damping factor [3] [5]. Selec- tion of the proper model order is based on a match- ing technique between the real signal and a synthesised version. The degree of matching can be measured by using the cross-correlation and normalised root-mean- square error [4] [5] of the synthesised signal with respect to the actual signal. Results based on synthetic signals show that the optimum model order ( p ) , for an eigen- value relative magnitude IC, lies in the amplitude range -32dB 5 K 5 -40dB. Thus the order of the model is chosen based on two criteria which must be satisfied simultaneously: (1) p must be even in the above amp- litude range of K and (2) the ratio of two consecutive

0-7803-2050-6/94 $4.00 01994 IEEE 1280

eigenvalues $& is a maximum [l] . This method is very attractive for comparative studies because it compares the spectral components at the same relative level of energy before and after mechanical valve implantation.

RESULTS AND DISCUSSION

Figures 2 and 3 present the spectrum of an ensemble of S1 and S2 before and after mechanical valve implant- ation.

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Figure 2: Spectrum of S1 before (full line) and after (dashed line) valve implantation. A 23 mm Carbomedics prosthetic valve was implanted in the mitral position.

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Figure 3: Spectrum of S2 before (full line) and after (dashed line) valve implantation. A 23 mm Aortech prosthesis was implanted in the aortic position. Spectra produced from all other patients show similar results. In the case of mitral implantation, regardless of the type of valve, the frequencies of the two to three dominant components in the range 20-100 Hz remain more or less stable. In the frequency range 100-200 Hz, we have observed that there is one frequency component which is not affected by valve implantation. However, it should be said that the energy and the frequency value of these components change from patient to patient, which suggests that these components are dependent on the anatomical characteristics of the heart and the size of the chest-thorax system. It was also found that a number of new components (at least three to four) in the range 300-1500 Hz appear after valve implantation. Similar characteristics are also found for S2. However, in this case there are two componmts which remain un- changed between 20-100 Hz and the average number of extra components above 300 Hz is two to three. Based on the range of these frequencies (i.e. more than 300 Hz) and the fact that none of these components were present before mechanical implantation, one can attribute these components to closure vibration of the mechanical pros- thetic valve and its interaction with the non-linear chest- thorax system. In the examined cases of leaky valves, it has been found

that components above 400 Hz disappear or move to the lower part of the spectrum.

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Figure 4: Spectrum of S2 in a patient with leaky valve in aortic position. Figure 4 shows the spectrum of a patient with a leaky valve in the aortic position. It is clear that there are no predominant peaks above 300 Hz and a large part of the energy is concentrated between 250 and 400 Hz.

CONCLUSION Spectral analysis of S1 and S2 before and after mech- anical prosthetic valve implantation is presented. A clear difference in spectral components is found espe- cially above 300 Hz. The frequency of these components is a function of valve type and the chest-thorax charac- teristics of the patients. From preliminary results it is shown that these components move down in the lower part of the spectrum in the case of leaky valves. There- fore, these components might provide a feature set for the condition monitoring of this particular mechanical prosthetic valve. However, more examples are needed to establish the significance of these spectral components.

ACKNOWLEDGMENT The authors would like to thank the University of Edinburgh and the Chest ‘Heart and Stroke Association for providing the financial support to purchase the sound recording equipment.

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REFERENCES

K. Kostantinides, K. Yau, “Statistical Analysis of Effective Singular Values in Matrix Rank Determ- ination”, IEEE Trans. on ASSP, Vol. 36, No. 5 , pp. 757-763, May 1988. H. Koymen et al., “A Study of Prosthet,ic Heart Valve Sounds”, IEEE Trans. on BME, Vol. 34, pp.

H. P. Sava, J . T. E. McDonnell, ‘Compar- ison of spectral analysis algorithms for use in spectral phonocardiography”, E USIPCO-94, Edin- burgh 1994. H. P. Sava, “A New Analysis/Synthesis of First Heart Sounds by Using an Overdetermined Forward-Backward Prony’s Method”, Tech. Report, Dept. of Elec. Eng., Univ. of Edinburgh, 1994, Y. Tang et al., “The Synthesis of the Aortic Valve Closure Sound of the Dog by the Mean Filter of Forward and Backward Predictor”, IEEE Trans. on BME, Vol. 39, No. 1, pp. 1-8, Jan. 1992.

853-863, 1987.