Detection of the third heart sound using a tailored wavelet approach

The third heart sound is normally heard during auscultation of younger individuals but disappears with increasing age. However, this sound can appear in patients with heart failure and is thus of potential diagnostic use in these patients. Auscultation of the heart involves a high degree of subjectivity. Furthermore, the third heart sound has low amplitude and a low-frequency content compared with the first and second heart sounds, which makes it difficult for the human ear to detect this sound. It is our belief that it would be of great help to the physician to receive computer-based support through an intelligent stethoscope, to determine whether a third heart sound is present or not. A precise, accurate and low-cost instrument of this kind would potentially provide objective means for the detection of early heart failure, and could even be used in primary health care. In the first step, phonocardiograms from ten children, all known to have a third heart sound, were analysed, to provide knowledge about the sound features without interference from pathological sounds. Using this knowledge, a tailored wavelet analysis procedure was developed to identify the third heart sound automatically, a technique that was shown to be superior to Fourier transform techniques. In the second step, the method was applied to phonocardiograms from heart patients known to have heart failure. The features of the third heart sound in children and of that in patients were shown to be similar. This resulted in a method for the automatic detection of third heart sounds. The method was able to detect third heart sounds effectively (90%), with a low false detection rate (3.7%), which supports its clinical use. The detection rate was almost equal in both the children and patient groups. The method is therefore capable of detecting, not only distinct and clearly visible/audible third heart sounds found in children, but also third heart sounds in phonocardiograms from patients suffering from heart failure.     

Detection of the third heart sound using a tailored wavelet approach: Method verification

Heart sounds can be considered as mechanical fingerprints of myocardial function. The third heart sound normally occurs in children but disappears with maturation. The sound can also appear in patients with heart failure. The sound is characterised by its low-amplitude and low-frequency content, which makes it difficult to identify by the traditional use of the stethoscope. A wavelet-based method has recently been developed for detection of the third heart sound. This study investigated if the third heart sound could be identified in patients with heart failure using this detection method. The method was also compared with auscultation using conventional phonocardiography and with characterisation of the patients with echocardiography. In the first study, 87% of the third heart sounds were detected using the wavelet method, 12% were missed, and 6% were false positive. In study 2, the waveletdetection method identified 87% of the patients using the third heart sound, and regular phonocardiography identified two (25%) of the subjects.     

Spectral analysis of second heart sound in normal children by selective linear prediction coding

A spectral analysis technique using selective linear prediction (SLP) coding based on an all pole model is applied to determine the spectral distribution of second heart sounds (SII) in 17 normal children. The SLP spectra are compared with the conventional spectra obtained using the fast Fourier transform (FFT) technique. It is observed that the SLP technique produces spectra with better definition of spectral peaks. Average spectral energy distribution of second heart sound in normal children is presented. Spectral energies in different frequency bandwidths are correlated with the aortic valve size parameter obtained echocardiographically. It is found that the best correlation is obtained in the 120–140 Hz bandwidth. A possible interpretation in terms of documented second heart sound determinants is also discussed.     

Estimation of pulmonary arterial pressure by a neural network analysis using features based on time-frequency representations of the second heart sound

The objective of the study was to develop a non-invasive method for the estimation of pulmonary arterial pressure (PAP) using a neural network (NN) and features extracted from the second heart sound (S2). To obtain the information required to train and test the NN, an animal model of pulmonary hypertension (PHT) was developed, and nine pigs were investigated. During the experiments, the electrocardiogram, phonocardiogram and PAP were recorded. Subsequently, between 15 and 50 S2 heart sounds were isolated for each PAP stage and for each animal studied. A Coiflet wavelet decomposition and a pseudo smoothed Wigner-Ville distribution were used to extract features from the S2 sounds and train a one-hidden-layer NN using two-thirds of the data. The NN performance was tested on the remaining one-third of the data. NN estimates of the systolic and mean PAPs were obtained for each S2 and then ensemble averaged over the 15–50 S2 sounds selected for each PAP stage. The standard errors between the mean and systolic PAPs estimated by the NN and those measured with a catheter were 6.0 mmHg and 8.4 mmHg, respectively, and the correlation coefficients were 0.89 and 0.86, respectively. The classification accuracy, using 23 mmHg mean PAP and 30 mmHg systolic PAP thresholds between normal PAP and PHT, was 97% and 91% respectively.     

基于小波变换的R波奇异性分析的研究

奇异性是生物信号的基本特征。依据小波变换模在多尺度上的变化特征,研究了信号奇异性分析的基本定理,在此基础上提出了奇异度Lipschitz指数的估计算法,并将之用于心电图(ECG)的R波奇异性分析。研究发现,随机选取的10名健康受试者与10名心律不齐患者的ECG的R波的奇异性存在显著的差异,健康受试者的奇异性结果显著高于心律不齐的患者。     

Use of the fast Fourier transform in the frequency analysis of the second heart sound in normal man

The second heart sounds of 26 normal, young males, recorded at the aortic and pulmonary areas, were analysed for their frequency content by means of the fast Fourier transform. For both locations of measurement, peaks were observed in the frequency spectra in the lowfrequency range (10–80 Hz), the medium-frequency range (80–220 Hz), and the high-frequency range (220–400 Hz). In both the aortic and pulmonary areas, 25 of the 26 subjects had at least two peaks in the 80–400 Hz range, and a majority had one peak in the low-frequency range. A correlation coefficient of 0·75 was obtained between the medium frequency peaks in the aortic and pulmonary areas. The average frequency spectrum obtained for the entire study at each area indicates that the attenuation characteristics are nonlinear in the region of 10–160 Hz. For 160–400 Hz the attenuation in the pulmonary area is about 18 dB per octave and in the aortic area about 23 dB per octave. The observed peaks are probably related to the fluid-dynamic events causing the second heart sound. Thus important diagnostic information can probably be obtained from frequency analysis studies of cardiovascular sounds, and these studies will help in understanding the basic mechanisms which produce the sounds.