Comparison of envelope extraction algorithms for cardiac sound signal segmentation

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1 Available online at Expert Systems with Applications Expert Systems with Applications 34 (2008) Comparison of envelope extraction algorithms for cardiac sound signal segmentation Samjin Choi, Zhongwei Jiang * Micro-Mechatronics Laboratory, Department of Mechanical Engineering, Faculty of Engineering, Yamaguchi University, , Tokiwadai, Ube, Yamaguchi , Japan Abstract This paper describes a comparative study of the envelope extraction algorithms for the cardiac sound signal segmentation. In order to extract the envelope curves based on the time elapses of the first and the second heart sounds of cardiac sound signals, three representative algorithms such as the normalized average Shannon energy, the envelope information of Hilbert transform, and the cardiac sound characteristic waveform (CSCW) are introduced. Performance comparison of the envelope extraction algorithms, and the advantages and disadvantages of the methods are examined by some parameters. Ó 2006 Elsevier Ltd. All rights reserved. Keywords: Cardiac sound characteristic waveform (CSCW); Envelope extraction algorithm; Hilbert transform; Normalized average Shannon energy; Segmentation 1. Introduction * Corresponding author. Tel./fax: addresses: choi@yamaguchi-u.ac.jp (S. Choi), jiang@yamaguchi-u.ac.jp (Z. Jiang). It is the fact that the body function change will take place, particularly in the nervous system when the body is abnormality. The activity of the sympathetic part of the nervous system usually causes a change of the heart valvular function. It is certain that these reactions come from the important survival function of human. However, these continuous reactions lead the body system to reaches its limit, and the resulting heart valve will be damaged before you know it. The electrocardiogram (ECG) is the popular method for the checkup of wrong with cardiorespiratory function over the decades. However, since the heart defect comes from the structural abnormalities and the characterization by heart murmur sounds, it is difficult to detect it using ECG. From a view for monitoring the cardiac sound signals due to mechanical vibrations generated in the organs, it is obvious that auscultation is a fundamental tool in the diagnosis of heart disease. But, the auscultation of cardiac sound signals through either a conventional acoustic or an electronic stethoscope needs a long-term practice and experience. Namely, note that it can take years to acquire them. Although the stethoscope is the symbol of physicians, primary care physicians are documented to have poor auscultatory skill in actuality. The need for the primary care physicians to improve the cardiac auscultation skill is still very strong in the primary screening examination and becomes stronger for the general users to perform the auscultation at home (Jiang & Choi, 2006; Reed, Reed, & Fritzson, 2004). Basic cardiac sound signals are mostly comprised of four sound classes: two outstanding sounds named as the first heart sound (S1) and the second heart sound (S2), and two weak sounds named as the third (S3) and the fourth heart sounds (S4). These four sounds may be audible by the auscultation of heart and occur in the frequency range of Hz. However, most researches will restrict the S1 and S2 because S3 and S4 appear at very low amplitudes with low frequency components and are difficult to /$ - see front matter Ó 2006 Elsevier Ltd. All rights reserved. doi: /j.eswa

2 S. Choi, Z. Jiang / Expert Systems with Applications 34 (2008) be caught in usual auscultation. As for heart defect, in the meanwhile, the unitary murmurs as a systolic ejection murmur (e.g., aortic stenosis) and a pansystolic murmur (e.g., mitral regurgitation) mostly appear between the S1 and S2 with different noise patterns like the diamond and rectangular shapes (Jiang & Choi, 2006). The complex and highly non-stationary nature of the cardiac sound signal can make it leading to analyze in an automatic way. Namely the cardiac sound signal needs to be segmented into features for the automatic analysis and classification of them. Unfortunately, most of researches were concerning on the characteristic extraction by local frequency analysis method (Akay, 1994; Bulgrin, Rubal, Thompson, & Moody, 1993; Fazzalari, Mazumdar, Ghista, Allen, & Bruin, 1984; Iwata, Ishii, Suzumura, & Ikegaya, 1980; Kanai, 1995; Reed et al., 2004; Wu, Lo, & Wang, 1995). But, some methods have been applied to study the fundamental mechanisms underlying the production of sound by the heart and the correlation between the normal cardiac sound and various heart defects (Choi & Jiang, 2005; Gupta, Palaniappan, Swaminathan, & Krishnan, in press; Jiang & Choi, 2006; Liang, Lukkarinen, & Hartime, 1997; Martínez-Alajarín & Ruiz-Merino, 2005). The characteristic and correlation information with advances in digital signal processing in the frequency domain and in the time domain each other, gives aid not only to doctor as busy as a bee, but also to an inexperienced or non-clinical experience person to monitor the healthy condition easily, especially the correlation in time domain. From this point, the envelope of cardiac sound signals would give passable information on investigating intrinsic characteristics of signal while envelope might be poor than cardiac sound. The researches on envelope extraction (Choi & Jiang, 2005; Feldman & Braun, 1997; Gupta et al., in press; Jiang & Choi, 2006; Liang et al., 1997; Martínez-Alajarín & Ruiz-Merino, 2005) were concentrated in a segmentation process to be carried out based on the normalized average Shannon energy calculated from Shannon energy. Namely, since most cardiovascular sounds mostly would occur in below 1 khz, it was to classify the normality and abnormality of cardiac sound signals by Shannon energy calculated from pre-processing signals filtered by low pass filter below 1 khz or bans pass filter of certain frequency band (Gupta et al., in press; Liang et al., 1997; Martínez-Alajarín & Ruiz-Merino, 2005; Xu, Durand, & Pibarot, 2000). Furthermore, the method based on Hilbert transform used the Envelope (E) information, which was the decimated signal of the real part of a complex analytic signal, and the Instantaneous Frequency (IF) information, which was the derivative of the imaginary part of a complex analytic signal (Feldman & Braun, 1997; Martínez-Alajarín & Ruiz-Merino, 2005; Xu et al., 2000). At last, the cardiac sound characteristic waveform algorithm used an analytical model based on a singledegree-of-freedom to extract characteristic waveforms from cardiac signals, and the response curves of analytical model were used to decide on whether they were normal or abnormal (Choi & Jiang, 2005; Jiang & Choi, 2006). Meanwhile, for actually auscultation of heart sounds, the stethoscope hardware system, or recording conditions, personal gap might affect directly on the quality of sound signals, especially different noise variances. The envelopes are affected directly by noise components and the resulting features may deteriorate the segmentation rate of cardiac sounds. In this paper, the representative envelope extraction algorithms on cardiac sound signal segmentation are investigated. For comparison of the effects on how extracts the envelope curves of first heart sound S1 and the second heart sound S2, three most popular envelope extraction algorithms like the normalized average Shannon energy, the Envelope of Hilbert transform and the cardiac sound characteristic waveforms, are introduced. And performance of three envelope extraction algorithms according to degrees of additive noise or energy is also investigated by the mean-absolute-value (MAV) of signals (Clancy & Hogan, 1999). Furthermore, the features named as the diagnostic parameters are proposed for cardiac sound segmentation. And since the parameter derivation is influenced directly by threshold values (THVs), two selecting methods are used to segment singular cardiac cycle of the parameters, namely for the suitable THVs selection. Normal and abnormal cardiac sounds which are included in the medical textbooks and internet web databases and recorded directly by the wireless stethoscope system are used to test them experimentally (Choi & Jiang, 2005; Nakao; Sawayama, 1994; UWCM). 2. Envelope extraction The cardiac sound usually has enormous volumes and is easily affected by noises. Furthermore, for reasons of the complex and highly non-stationary nature of cardiac sound signals, they should be segmented into components for the first step of automatic analysis and classification. The envelope of cardiac sound signals gives passable information on investigating intrinsic characteristics of signal, while envelope is poor than sound signal, for instance, a normal cardiac sound and its envelope curve are shown in Fig. 1. Itis obvious that the envelope curve based on the first heart sound S1 and second heart sound S2 gives more simple and conspicuous representation than the original sound signals. Then, in this study, we examine three of the most representative envelope extraction techniques, i.e., (1) the Shannon envelope based on the normalized average Shannon energy (Gupta et al., in press; Liang et al., 1997; Martínez-Alajarín & Ruiz-Merino, 2005), (2) the Envelope of Hilbert transform (Feldman & Braun, 1997; Martínez- Alajarín & Ruiz-Merino, 2005; Xu et al., 2000), (3) the cardiac sound characteristic waveforms (Choi & Jiang, 2005; Jiang & Choi, 2006), which are frequently used for extracting envelope curves of the first sound and second sound on cardiac signals.

3 1058 S. Choi, Z. Jiang / Expert Systems with Applications 34 (2008) Fig. 1. Cardiac sound and its envelope curve. An envelope curve gives more simple and conspicuous representation than cardiac sound signal Shannon envelope The normalized average Shannon energy named as Shannon envelope is known as the popular technique on envelope extraction of cardiac sound signals (Gupta et al., in press; Liang et al., 1997; Martínez-Alajarín & Ruiz-Merino, 2005). This algorithm is described in detail in following Pre-processing Suppose the original cardiac sound recorded using any stethoscope by x(t), which is recorded with 16 bit-depth and 11,025 Hz sampling frequency. At first, signal x(t) is decimated by factor 5 from 11,025 Hz to 2205 Hz, and filtered by a second-order Butterworth-type low pass filter with cut-off frequency of 882 Hz. Here, the filtered signal is refiltered reverse direction so that there is no time delay in the resulting signal x 2205 (t). Next, the normalization is applied by setting the variance of the signal to a value of 1.0. The resulting signals can express by x norm ðtþ ¼ x 2205 ðtþ j max i ðx 2205 ðiþþj : ð1þ Shannon energy The envelope signal of pre-processed signal is then calculated. Here, the Shannon energy of signal x norm (t) can be calculated as follows: Shannon energy ¼ x 2 norm ðtþ log x2 norm ðtþ: ð2þ For example, using the normal cardiac sound x(t) (Fig. 2a) recorded by stethoscope, the resultant waveforms calculated by Shannon energy and other methods are represented in Fig. 2. Fig. 2b e show the waveforms calculated by Shannon energy as Eq. (2), Shannon entropy as x norm (t) logjx norm (t)j, absolute value as x norm (t), and square value as x 2 normðtþ, respectively. From figures, it is obvious that Shannon energy (Fig. 2b) emphasizes the medium intensity signal and attenuates the effect of low intensity signal much more than that of high intensity signal, while Shannon entropy (Fig. 2c) attenuates the effect of low value noise that makes the envelope too noisy to read. Furthermore, absolute value (Fig. 2d) gives the same weight to all signals and squared value (Fig. 2e) will bury the low intensity sounds under the high intensity ones by enlarging the intensity ratio. Consequently, Shannon energy (Fig. 2b) is better than absolute value (Fig. 2d) in shortening the difference of the envelope intensity between low and high intensity sounds. This shortening makes the finding of low intensity sounds easier (Liang et al., 1997). At next, the average Shannon energy is calculated in continuous 0.02 s segments via the signal with 0.01 s segment overlapping. Then, the average Shannon energy is calculated as E s ¼ 1 N X N i¼1 x 2 norm ðiþ log x2 norm ðiþ; where x norm (t) is the pre-processed signal and N is signal length in 0.02 s segment, which corresponds to a frame, here N = 44. Lastly, it normalizes the average Shannon energy over all of the frames, so the normalized average Shannon energy becomes PðtÞ ¼ E sðtþ MðE s ðtþþ : ð4þ SðE s ðtþþ where E s (t) is the average Shannon energy for frame t, M(E s (t)) is the mean value of E s (t), and S(E s (t)) is the standard deviation of E s (t). Hereinafter, the normalized average Shannon energy P(t) is called as the Shannon envelope Hilbert envelope Envelope extraction based on Hilbert transform can be mostly divided into two branches, i.e., (1) the Envelope E is the decimated signal of the real part of a complex analytic signal, and (2) the Instantaneous Frequency IF is the derivative of the imaginary part of complex analytic signal. However, in this paper, the Instantaneous Fre- ð3þ

4 S. Choi, Z. Jiang / Expert Systems with Applications 34 (2008) Fig. 2. The normal cardiac sound x(t) (a) and the resultant waveforms calculated by Shannon energy (b), Shannon entropy (c), absolute value (d), and square value (e) of the normalized it. quency to show the fundamental different envelope patterns was ignored, because we just focused on algorithm based on the time elapses of the first heart sound and second heart sound on the time domain (Feldman & Braun, 1997; Martínez-Alajarín & Ruiz-Merino, 2005; Xu et al., 2000).

5 1060 S. Choi, Z. Jiang / Expert Systems with Applications 34 (2008) Hilbert transform First of all, to summarize the key ideas in Hilbert transform, let the cardiac sound signal x(t) recorded by stethoscope be a real-valued signal. The Hilbert transform becomes as H½xðtÞŠ ¼ 1 p Z 1 1 xðsþ s t ds ¼ xðtþ 1 pt ; where the principle value of the integral is used and * indicates convolution operator. Hilbert transform is often interpreted as a 90 phase shifter, [H[x(t)]] = x(t). Further, from the given signal x(t), a complex analytic signal A[x(t)] can be expressed as A½xðtÞŠ ¼ xðtþþjh½xðtþš ¼ EðtÞe juðtþ ; qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi with EðtÞ ¼ xðtþ 2 þ H½xðtÞŠ 2 and u(t) = tan 1 (H[x(t)]/ x(t)). Such a representation has been found useful many types of signal, especially narrowband ones, where E(t) is usually slow compared to the signals temporal variations. Therefore, E(t) and the time derivative of /(t) are named as the Envelope E and the Instantaneous Frequency IF, respectively Pre-processing In order to extract the envelope using Hilbert transform, the original cardiac sound signal is firstly decimated by same factor used to the Shannon envelope P(t), and filtered by a second-order Butterworth-type low pass filter with cut-off frequency of 500 Hz. The normalization described same by Eq. (1) is applied sequentially Envelope Here, Hilbert transform is applied to the resultant signal x norm (t) and the Envelope E(t) is calculated by Eqs. (5) and (6). However, the Envelope might still have a fast vibration because it has very high sampling frequency. Then, E(t) is recalculated by applying a moving average filter with window length of s and s overlapping. Hence the final signal becomes GðtÞ ¼ XW i¼1 EðiÞwðiÞ; where W is the number of samples of the window and w is the hamming window. Hereinafter, the signal G(t) is called as the Hilbert envelope Cardiac sound characteristic waveform In the previous study (Choi & Jiang, 2005; Jiang & Choi, 2006), a cardiac sound characteristic waveform method for in-home heart disorder monitoring with electric stethoscope was presented firstly. In here, the internal process for the envelope extraction part from this algorithm will be described firstly in detail. ð5þ ð6þ ð7þ Table 1 Coefficients of the equivalent filter bank h 0 h , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Pre-processing In cardiac sound characteristic waveform, suppose the original signal recorded using any stethoscope by x(t), where a bit-depth is 16 bits and sampling frequency is 8000 Hz. And then, for applying down-sampling factor similar to other algorithms, signal x(t) is decimated from 8000 Hz to 2000 Hz. Nextly, we try using filter bank based on wavelet transform for cancellation of noises over 1 khz, because the cardiovascular sounds mostly occur in below 1 khz. The MATLAB program was used for filter bank implementation. Coefficients of the Daubechies Db10 type wavelet used as a mother wavelet are listed in Table 1, where h 0 and h 1 are biorthogonal filters by a 2 1 scale, respectively. By applying the filter bank to signal x 2000 (t) to 2-level, the approximation at A2 ( Hz) was used to cut off the high frequency components over 1 khz. That is, it is found that the signal for noise cancellation of cardiac sounds is reconstructed by A2 component. Then, the resultant signals x norm (t) are normalized to absolute maximum of the signal by applying Eq. (1) Cardiac sound characteristic waveform extraction From information as the heart sound patterns could be classified into several types, an analytical model based on single-degree-of-freedom (SDOF) (Feldman & Braun, 1997; Jiang & Choi, 2006) was used for extracting cardiac sound characteristic waveforms from the original signals. As for SDOF analytical model, assume that the mass, the coefficient of the spring, and the damping coefficient of the damper in this model are M, K, and C, respectively. Denote the cardiac sound recorded by stethoscope as the input signal X(t)= x norm (t). The output response Y(t) is then given by M Y ðtþþc _Y ðtþþky ðtþ ¼X ðtþ; which can be expressed as, Y ðtþþ2xf _Y ðtþþx 2 Y ðtþ ¼ X ðtþ; ð9þ with the signal X ðtþ ¼jX pðtþ=m j, the resonant angular frequency parameter x ¼ ffiffiffiffiffiffiffiffiffiffi pk=m ðrad=sþ, and the damping rate parameter f ¼ C=2 ffiffiffiffiffiffiffiffi MK 100 ð%þ. These parameters x and f can be selected as a certain values to extract the characteristics of the normal and abnormal cardiac sounds. In following analysis, they are set by rad/s (x) and 70.7% (f) as default. Herein, in order to compensate time delay (see Fig. 3a) by applying the low angular frequency on the cardiac characteristic waveforms, we used the cross correlation CC(i) ð8þ

6 S. Choi, Z. Jiang / Expert Systems with Applications 34 (2008) Fig. 3. Process of time delay compensation by cross correlation. (a) The relative time delay between X(t) and Y(t), (b) the results of cross correlation where N is the number of samples and CC p is the peak of cross correlation, and (c) time moving by CC p. (see Fig. 3b) between input signal X(t) and output signal Y(t) as P N 1 t¼0 ½ðX ðtþ l CCðiÞ¼ X ÞŠ½ðY ðt iþ l Y ÞŠ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P q ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi N 1 t¼0 ðx ðtþ l X Þ 2 P ; i ¼ 1;...;N; N 1 t¼0 ðy ðtþ l Y Þ 2 ð10þ where N is the number of samples; l X and l Y are the average values of X(t) and Y(t), respectively. The relative time delay was estimated by selecting the peak of cross correlation between two signals, namely CC p as Fig. 3b. And then, the resulting signal C(t) (see Fig. 3c) comes to Eq. (11). These processes are depicted in Fig. 3. Hereinafter, the heavy line C(t) is called as the cardiac sound characteristic waveform (CSCW). CðtÞ ¼Y tþ CC p N 2 : ð11þ 2.4. Results and discussions Three most popular envelope extraction algorithms were introduced in the above section. In this section the validation of them is investigated by some case studies. First, Fig. 4 shows the normal and abnormal cardiac sounds and their envelope curves corresponding to envelope extraction methods. For a normal case, the absolute value of the pre-processed signal x norm (t) recorded from volunteer is shown in the top row of left column of Fig. 4, and its Shannon envelope P(t), where the mean (Eq. (4)) ism(e s (t)) = and the standard deviation (Eq. (4)) iss(e s (t)) = , is shown in the second row of left column of Fig. 4. From figures, it is shown that the first heart sound S1 and second heart sound S2 in normal cardiac sounds are extracted clearly. For an abnormal case, meanwhile, the pre-processed mitral regurgitation sound and its Shannon envelope, where the mean and the standard deviation are and , respectively,

7 1062 S. Choi, Z. Jiang / Expert Systems with Applications 34 (2008) Fig. 4. The absolute values of pre-processed signals x norm (t) in top row and their Shannon envelopes P(t) in second row, Hilbert envelopes G(t) in third row, and CSCWs C(t) in bottom row for a normal sound case (left column) and a mitral regurgitation case (right column). are shown in top and second row of right column of Fig. 4. It is obvious that the regurgitation as noise signals is well extracted between the S1 and S2, while it seems to be different pattern compared to normal case. With Hilbert transform, envelope curves calculated from the same sound signals used to Shannon envelope are shown in the third row of Fig. 4. From figures, the heavy lines describe the envelopes G(t) extracted by Hilbert transform for the normal case (left column) and the mitral regurgitation sound (right column). It is obvious that two examples show clear peaks of the S1 and S2 for a normal case, and to be noisy curves between the S1 and S2 for a mitral regurgitation, while the envelopes include a little noisy or minute curves as compared to the results of the Shannon envelope P(t). As a result, these sources might cause low and insufficient envelope efficiency. Lastly, the bottom row of Fig. 4 shows the normal and abnormal cardiac sound signals and their CSCWs, when the parameters x and f for the SDOF analytical model are set at rad/s and 70.7%, respectively. It is obvious that heavy lines C(t) give more simple and conspicuous representation than sound signals x norm (t), and may be easy to be treated in cardiac sound segmentation. From Fig. 4, it is shown that the CSCW C(t) gives the uniform S1 and S2 and low noise envelopes compared to different algorithms as Shannon envelope P(t) in the second row of Fig. 4 and Hilbert envelope G(t) in the third row. In the meanwhile for the cardiac sound auscultation, the stethoscope hardware system, or recording conditions, personal gap and etc might affect directly on the quality of sound signals, especially the different noise and energy variances. Namely, according to the kinds of cardiac sounds with varying degrees of additive noise or energy, envelopes are different each other and the resulting features may deteriorate the cardiac sound segmentation efficiency. For instance, six examples with varying degrees of additive noise or energy for normal cases (left column) and abnormal cases (right column) are shown in Fig. 5. Fig. 5a and d show sound examples with the relative low noise or energy, while Fig. 5c and f present them with the relative high noise or energy. In case of these examples, it might be difficult whether they would identify the normality or not, without the aid of envelope curves. And then to quantitate the degree of noise or energy among examples, mean-absolute-values (MAVs) (Clancy & Hogan, 1999) of normalized signals were used as three stages, i.e., MAVs = [0.1270, , ] and [0.0873, , ] for normal cases (Fig. 5a c) and abnormal cases (Fig. 5d f), respectively.

8 S. Choi, Z. Jiang / Expert Systems with Applications 34 (2008) Fig. 5. Representative cardiac sounds with varying degrees of additive noises or energy for normal cases (a c) and mitral regurgitation cases (d f) where the mean-absolute-values (MAVs) of normalized cardiac signals (a f) are , , , , , and , respectively. Hereinbefore, the envelopes of sound signals with the relative low noise or energy such as a normal case where the MAV of x norm (t) is (Fig. 5a), and an abnormal case where the MAV of x norm (t) is (Fig. 5d), were investigated in Fig. 4. Envelopes in case of other normal cases (Fig. 5b and c) are shown in Fig. 6. The left column of Fig. 6 shows the envelopes like Shannon envelope (top row), Hilbert envelope (middle row), and CSCW (bottom row) extracted from a sound signal (MAV = ) of Fig. 5b. The right Fig. 6. Envelope curves by Shannon envelope P(t), Hilbert envelope G(t) and CSCW C(t) from top to bottom rows, for normal sound signals of Fig. 5b (left column, MAV = ) and Fig. 5c (right column, MAV = ).

9 1064 S. Choi, Z. Jiang / Expert Systems with Applications 34 (2008) column shows them (MAV = ) of Fig. 5c. From figures, in case of Shannon envelopes P(t), we easily find out the S1 and S2 from the envelope in MAV = , but it may be difficult to find out them from envelope in MAV = In case Hilbert envelopes G(t), it may be impossible to diagnosis cardiac sounds from two envelopes, even if the S1 and S2 are shown in figure. However, CSCWs C(t) as the bottom row of Fig. 6 give good envelope curves without varying degrees of additive noise or energy. And then, it is obvious that the CSCW may be validated on the envelope extraction of normal cardiac sounds compared to other algorithms. As for abnormal cases, the envelopes for mitral regurgitation sounds as shown in Fig. 5e and f are shown in Fig. 7. The top row of Fig. 7 shows Shannon envelopes P(t), the middle row shows Hilbert envelopes G(t), and the bottom row presents CSCWs C(t) extracted from two abnormal cases for different degrees of additive noise or energy such as MAVs are in left column (Fig. 5e) and in right column (Fig. 5f). From figures, it easily finds out noise signals between the S1 and S2 for a mitral regurgitation in case of Shannon envelopes and CSCWs, while it may be impossible to find out them from Hilbert envelopes. And then, it is obvious that the cardiac characteristic waveform Fig. 7. Envelope curves by Shannon envelope P(t), Hilbert envelope G(t) and CSCW C(t) from top to bottom rows, for mitral regurgitation sound signals of Fig. 5e (left column, MAV = ) and Fig. 5f (right column, MAV = ). Fig. 8. Data processing procedures of three envelope extraction algorithms as (a) Shannon envelope P(t), (b) Hilbert envelope G(t), and (c) CSCW C(t).

10 S. Choi, Z. Jiang / Expert Systems with Applications 34 (2008) CSCW and the Shannon envelope may be validated on the envelope extraction of mitral regurgitation sounds as an abnormal case, regardless of the degree of additive noise or energy. Additionally, we are looking forward to deriving higher and efficient envelope curves from original signals, and promoting the resulting segmentation efficiency. From above results, both for normal and abnormal cases, it can be surely concluded that the cardiac sound characteristic waveform (Choi & Jiang, 2005; Jiang & Choi, 2006) provides sufficient envelope curves and representation as compared with conventional envelope extraction techniques like Shannon envelope and Hilbert envelope. Furthermore the data processing procedures according to three of the most representative envelope extraction techniques are summarized in Fig. 8. These algorithms were implemented with the MATLAB program. At next, using the diagnostic parameters computed from extracted envelopes, we testify the efficiency and usefulness of three most popular envelope extraction algorithms. especially cardiac sound segmentation. Based on this reason, using three algorithms like Shannon envelope P(t), Hilbert envelope G(t), and CSCW C(t) as mentioned in the previous chapter, they could clearly extract the S1 and S2 although there were sometimes cases to be difficult to find out them in case of Hilbert envelope. However, in order to classify normal and abnormal cardiac sounds, there is need for them to be segmented into components. Therefore, for the next step for the analysis and diagnosis of cardiac sounds, we used the diagnostic parameters T1, T2, T11, and T12 (see Fig. 9 and Table 2)(Choi & Jiang, 2005; Jiang & Choi, 2006). Unfortunately, these raw parameters may include not only correct parameters but also incorrect them, for several reasons, e.g., varying degrees of additive noise or energy as mentioned hereinbefore, the resulting threshold value selection problem as mentioned hereinafter, and so forth. And then, in this chapter, the validation of three most popular envelope extraction algorithms is investigated by the segmentation efficiency. 3. Cardiac sound segmentation The first heart sound S1 and second heart sound S2 might play an important role in the cardiac sound analysis, 3.1. Defining the diagnostic parameters First of all, conceptual diagram for defining the diagnostic parameters is described in Fig. 9. The time elapses of the Fig. 9. Conceptual diagram for defining the diagnostic parameters and their representations on scattergram. (a) Normal cardiac sound, (b) definition of the parameters [T1, T2, T11, T12] from its envelope curve, (c) scattergram on (T1, T2), and (d) scattergram on (T11, T12).

11 1066 S. Choi, Z. Jiang / Expert Systems with Applications 34 (2008) Table 2 Definition of the diagnostic parameters Diagnostic parameters The definition and significance T1 The time elapse of the S1 T2 The time elapse of the S2 T11 Cardiac cycle T12 The time elapse of S1 and S2 events S1 and S2 computed from the envelope curve (Fig. 9b) of cardiac sound signals (Fig. 9a) were used as the parameters, which were defined by T1, T2, T11, and T12 as summarized in Table 2. Here, a threshold value (THV), which may be changed dependently on the person individual or the type of pathology, can be selected at suitable value (Jiang & Choi, 2006). In order to evaluate the computed parameters [T1, T2,T11,T12] i, where i =[P(t),G(t),C(t)] indicates three envelopes (Choi & Jiang, 2005; Gupta et al., in press; Jiang & Choi, 2006; Liang et al., 1997; Martínez-Alajarín & Ruiz-Merino, 2005), two-dimensional plots, named as the scattergram, on (T1, T2) and (T11, T12) are introduced as shown in Fig. 9c and d. For normal sound case, the duration of a cardiac cycle T11 is about 0.8 s, the auricular systole or ventricular systole [T1, T2] around 0.1 s and the duration of systole and diastole T12 is about 0.4 s (Choi & Jiang, 2005; Jiang & Choi, 2006). Hence, suppose two circles, where each radius is 0.08 s, 0.2 s, and 0.15 s with respect to T1 or T2, T11, and T12, represent the normal cardiac cycle areas, which two center points are supposed at (0.1,0.1) s and (0.8,0.4) s corresponding to (T1, T2) and (T11, T12). If two data sets (T1, T2) and (T11, T12) are placed within each circle, the heart sound could be identified as the normal condition, e.g., normal group s offig. 9c and d, otherwise it could be done as the abnormal condition, e.g., abnormal group d of Fig. 9c and d. Therefore we are confident that the scattergram will help users understanding their heart conditions viscerally The role of THV on the diagnostic parameter derivation It was shown that a THV would be sensitive factor in the cardiac sound analysis with the envelope extraction algorithm because it was strongly related to the recording condition by the stethoscope hardware, personal difference, and so on. Furthermore, the resulting diagnostic parameters [T1, T2, T11, T12] maybe have a good potential for diagnosis and screening of heart defects (Choi & Jiang, 2005; Jiang & Choi, 2006). However, these parameters are depended significantly on a selected THV, for example, in spite of the normal sound, it is sometimes recognized as the abnormal sound by applying an improper THV or artifacts, and vice versa. A suitable THV should be selected with a consideration on the person individual or the type of pathology. Furthermore, THVs could be selected ideally within the range from 0% to 100%, but the stethoscope hardware system, recording conditions, personal gap, heart disorders and more might affect directly on the quality of cardiac sounds, e.g., THV could have the selectable ranges less than 10% at it s worst. So, the selectable range of THV is better to be experientially selected within 10 70% (Jiang & Choi, 2006). For example, by applying THVs = [20%, 40%, 60%] to three envelope results as mentioned in Fig. 4, the detected artifacts and incorrect S1 or S2 (AISSs) are shown in top panels of each envelope results in Fig. 10. The left column in Fig. 10 shows the AISSs, marked with s (20%), h (40%), and n (60%), when THVs are set, respectively by [20%,40%, 60%] corresponding to three envelope extraction algorithms for a normal sound case, and the right column shows AISSs for a mitral regurgitation case. Looking at the quantitative results, as for a normal sound (left column of Fig. 10), it may be shown that the lowest AISS is along the regions THVs = 40 60% (P(t) of Eq. (4) = ) for case Shannon envelope. For case Hilbert envelope G(t), the lowest AISS is along the regions THVs = 20 40%, it leaves nevertheless something to be desired. Fortunately, it is shown that the lowest AISS is along the THV ranges from 20% to 60% for case CSCW C(t). That is these regions indicate the selectable ranges of THV. On the other hand, as for a mitral regurgitation, the selectable THVs can be determined easily at ranges of 62 73% for case of Shannon envelope, 58 70% for case Hilbert envelope, and 60 90% for case CSCW from right column of Fig. 10. These phenomena, which the restricted and narrow ranges are selected compared to a normal case, can be caused by the noise signal to occur between the first heart sound and second heart sound. Consequently, it is obvious that THV can be differently selected according to kinds of the envelope extraction algorithm in the face of the same cardiac sound case, namely, the diagnostic parameter derivation is influenced directly by THV Identifying the reliable diagnostic parameters It could say clearly that the problem how to determine a THV should be solved before one wants to perform cardiac sound analysis. Of course, one simple way in practice use is to select a THV manually and calculate the parameters, and then check they are acceptable or not subjectively. If they are not acceptable, change to another THV and follow the same process. Therefore, in order to select a suitable THV to segment singular cardiac cycle of parameters, it firstly used the empirical or manual way, defined as method 1(Fig. 11a). In method 1, the results, percentages of the correct parameters [T1, T2, T11, T12] of samples for 500 cardiac cycles computed from several normal and abnormal sound examples (Choi & Jiang, 2005; Nakao; Sawayama, 1994; UWCM) corresponding to three envelope extraction algorithms, are defined as MODSG1. In a sense, method 1 may be the best way, but this process is very harsh for an untrained user or a busy physician. How to select the THV automatically is too an important factor

12 S. Choi, Z. Jiang / Expert Systems with Applications 34 (2008) Fig. 10. Examples of artifacts and incorrect S1 or S2 (AISSs) to be picked up by applying THVs, like e.g., 20% (s), 40% (h), and 60% (n), to three envelope curves for a normal case (left column) and a mitral regurgitation case (right column). in the cardiac sound segmentation. Therefore, an automatic selecting method of suitable THV ranges to segment singular cardiac cycle of parameters was used secondly by the aid of so-called Fuzzy C-means clustering method (Bezdek, Hathaway, Sabin, & Tucker, 1992; Bezdek & Pal, 1992; Guldemir & Sengur, 2006; Hammouda, 2000; Jiang & Choi, 2006). This process is shown in Fig. 11b. In method 2, the results, percentages of the correct parameters [J m,v 1,v 2,v 3,v 4 ], where J m is the cost function of Fuzzy C- means clustering and v 1 v 4 are cluster centers according to parameters [T1, T2,T11,T12], of samples for 500 cardiac cycles computed from several sound examples with respect to three envelope extraction algorithms, are named as MODSG2. Furthermore, the specifications of Fuzzy C- means clustering parameters used in this work are summarized in Table 3 (Jiang & Choi, 2006). The schematic representation of two cardiac sound segmentation methods proposed for selecting the reliable diagnostic parameters is shown in Fig Results and discussions Several examples of normal cardiac sounds, which are recorded directly by the wireless stethoscope system (Choi & Jiang, 2005), and abnormal cardiac sounds, which are included in the medical textbooks and internet web databases (Nakao; Sawayama, 1994; UWCM), were tested experimentally to estimate segmentation efficiency with respect to three envelope extraction algorithms. The results of segmentation rates such as MODSG1 and MODSG2, which mean percentages of correct parameters in each method, computed during 500 cardiac cycles extracted from several normal and abnormal sound examples are summarized in Table 4. From these statistic results, Shannon envelope G(t) shows high segmentation efficiency for abnormal cases (e.g., MODSG1 = 75.7% and MODSG2 = 89.4%) rather than normal cases (e.g., 65.9% and 78.2%), while Hilbert envelope P(t) shows low segmentation rates both cases. However CSCW C(t) shows high segmentation efficiency both cases, e.g., [96.2%, 100%] for normal cases and [72.7%, 88.2%] for abnormal cases. At a result, it is obvious that the envelope curves by CSCW algorithm are validated both for normal and abnormal cardiac sounds. Furthermore, from the results in Table 4, it is shown that the automatic selecting way (method 2) is more efficient the empirical or manual way (method 1). Consequently, these might indicate that the combination of CSCW and Shannon envelope extraction methods would lead to higher segmentation efficiency so that the resultant classification efficiency might be promoted. The classifica-

13 1068 S. Choi, Z. Jiang / Expert Systems with Applications 34 (2008) Fig. 11. Schematic representation of cardiac sound segmentation methods such as (a) method 1: the sense or experience of the operator and (b) method 2: the aid of data clustering technique. MODSG1 and MODSG2 indicate the segmentation results in each method. Table 3 Specifications of the clustering parameters used in this work Parameters Specifications Clustering technique Fuzzy C-means Data sets Sequential data series of (T1, T2) and (T11, T12) Group number Two fuzzy groups Distance function Euclidean distance Weighting exponent 2 Tolerance Table 4 The segmentation rates according to three envelope extraction algorithms for the normal and abnormal sounds Envelope extraction Segmentation rates (%) algorithms MODSG1 MODSG2 Normal Abnormal Normal Abnormal Shannon envelope G(t) Hilbert envelope P(t) CSCW C(t) tion efficiency for normal and abnormal cases will be continued in our future works. 4. Conclusions A comparative study was presented for three popular envelope extraction algorithms, Shannon envelope P(t) based on the normalized average Shannon energy, Hilbert envelope G(t) based on the Envelope information of Hilbert transform, and CSCW C(t) by an analytical model based on SDOF system. That is to say it is a fact that the S1 and S2 play an important role in the cardiac sound analysis, the envelope curves of S1 and S2 with respect to three representative envelope extraction algorithms were extracted and estimated experimentally. From the results, it was shown that CSCW curves gave the uniform S1 and S2 envelope information and had low noise as compared with Shannon envelope and Hilbert envelope curves. Furthermore, in order to test the effect of the different noise and energy variances in primary cardiac sound auscultation on the quality of sound signals, the envelope curves according to degree of noise or energy were investigated too. From the results, it was shown that CSCW algorithm provided sufficient performance compared to conventional Shannon envelope and Hilbert envelope algorithms. Two selecting methods, i.e., the empirical or manual way and

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