Time-frequency analysis of the first heart sound. Part 1: Simulation and analysis

The authors propose a simulated first heart sound (S1) signal that can be used as a reference signal to evaluate the accuracy of time-frequency representation techniques for studying multicomponent signals. The composition of this simulated S1 is based on the hypothesis that an S1 recorded on the thorax over the apical area of the heart is composed of constant frequency vibrations from the mitral valve and a frequency modulated vibration from the myocardium. Essentially, the simulated S1 consists of a valvular component and a myocardial component. The valvular component is modelled as two exponentially decaying sinusoids of 50 Hz and 150 Hz and the myocardial component is modelled by a frequency modulated wave between 20 Hz and 100 Hz. The study shows that the simulated S1 has temporal and spectral characteristics similar to S1 recorded in humans and dogs. It also shows that the spectrogram cannot resolve the three components of the simulated S1. It is concluded that it is necessary to search for a better time-frequency representation technique for studying the time-frequency distribution of multicomponent signals such as the simulated S1.     

基于匹配追踪的癫痫脑电信号的时频分析

目的 鉴于匹配追踪算法具有良好的参数化描述特性,应用匹配追踪算法研究癫痫脑电的时频分布特征.方法 通过仿真算例,将匹配追踪算法与短时傅里叶变换、Wigner-Ville分布结果进行比较,验证该方法的频率分辨率高及参数化表征的优越性;应用上述3种方法对癫痫脑电和正常脑电进行时频分析,研究癫痫异常放电在时频平面的表现.结果 仿真结果表明基于匹配追踪算法能得到较好的时频分布;对癫痫脑电和正常脑电进行时频分析,癫痫脑电和正常脑电在时频平面上存在明显的差异.结论 基于匹配追踪的时频分析方法,能够更好地揭示脑电类非平稳信号的特征.     

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.     

Investigating body motion patterns in patients with Parkinson's disease using matching pursuit algorithm

Several recent studies have quantified abnormalities in Parkinsonian gait. However, few studies have attempted to quantify the regularity of body motion during walking in patients with Parkinson's disease. The aim of the paper was to characterise body motion patterns in healthy, elderly subjects and patients with Parkinson's disease during walking. Body motion was recorded during walking for 16 patients with Parkinson's disease and ten healthy, elderly subjects using a tri-axial accelerometer device. To characterise the body motion patterns, time-frequency patterns of the body acceleration signal were estimated using a matching pursuit algorithm. Data from the study showed that the healthy, elderly subjects and patients with Parkinson's disease had different time-frequency patterns. The time-frequency patterns were classified into four distinct patterns based on their time durations: vertical (<0.15 s), circular (0.15–0.5 s), short horizontal (0.5–2.0 s) and long horizontal (>2.0 s). The data showed that the energy of the long horizontal patterns, representing long-term smooth and regular (rhythmic) activities, significantly decreased, but the energy of the circular patterns, representing irregular activities, increased in the patients with mild Parkinson's disease, compared with those of the healthy, elderly subjects (p<0.01). Futhermore, these features were seen more clearly in the body motions of severe case patients than is that of mild case patients. It was concluded that these differences are probably due to a lack of ability to control normal and smooth movement is Parkinson's disease.     

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.     

First heart sound spectra in relation to anterior mitral-leaflet closing velocity

A mathematical model is presented to relate mitral valve leaflet closing velocity to the subsequent vibrational magnitude following valve closure. This relationship is investigated experimentally by means of phonocardiographic and echocardiographic recordings from 17 human subjects. Fast Fourier transform analysis of digitised first heart sounds from each subject reveals that the sound intensities in different frequency bands are not uniformly related to the valve-leaflet closing velocity, obtained from the anterior mitral leaflet echocardiogram. It is found that, in the frequency range up to 150 Hz, closing velocity correlates best with sound intensity in the 30–45 hz bandwidth.     

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.     

Time-frequency analysis of the first heart sound: Part 3: Application to dogs with varying cardiac contractility and to patients with mitral mechanical prosthetic heart valves

The cone-kernel distribution (CKD) is first applied to the analysis of the intracardiac and the thoracic first heart sound (S1) of dogs in various cardiac contractile states, and secondly to the S1 of patients with mitral mechanical prosthetic heart valves. The CKD of native S1 in dogs shows that the dominant components of S1 are generally concentrated in a band at around 50 Hz with a horizontal flat or a semi-lunar shape, independently of the myocardial contractile state. There is no significant systematic rising frequency component. The instantaneous frequency of S1 shows a good cross-correlation with the time derivative of the left ventricular pressure (dP/dt), but the maximum frequency is not proportional to the maximum of dP/dt. The CKD of S1 in patients with mitral mechanical prosthetic heart valves showed a pulse-like component with a high-frequency bandwidth, which is distinct from the low constant-frequency components of S1 produced by native heart valves     

心音信号识别的意义及其方法的研究

本文概述了心音信号识别的意义,并对心音自动识别技术的发展进行了介绍,最后总结了今后工作可能的研究方向。     

Algorithm for detecting the first and the second heart sounds by spectral tracking

A detection algorithm for the first and the second heart sounds, which is one of the most important problems in an automatic diagnostic system for phonocardiograms, has been developed. It is based on the frequency-domain characteristics of heart sounds analysed by a linear-prediction method. The performance of the algorithm has been evaluated in 187 samples that contain 881 cardiac cycles including normal and abnormal subjects. The algorithm uses low frequency spectral tracking for the time series of the phonocardiogram. It can track spectral level smoothly so that it is fairly effective for the detection of heart sounds. This tracking procedure can be used in other applications such as electroencephalogram processing.     

Modeling of heart systolic murmurs based on multivariate matching pursuit for diagnosis of valvular disorders

Heart murmurs are pathological sounds produced by turbulent blood flow due to certain cardiac defects such as valves disorders. Detection of murmurs via auscultation is a task that depends on the proficiency of physician. There are many cases in which the accuracy of detection is questionable. The purpose of this study is development of a new mathematical model of systolic murmurs to extract their crucial features for identifying the heart diseases. A high resolution algorithm, multivariate matching pursuit, was used to model the murmurs by decomposing them into a series of parametric time–frequency atoms. Then, a novel model-based feature extraction method which uses the model parameters was performed to identify the cardiac sound signals. The proposed framework was applied to a database of 70 heart sound signals containing 35 normal and 35 abnormal samples. We achieved 92.5% accuracy in distinguishing subjects with valvular diseases using a MLP classifier, as compared to the matching pursuit-based features with an accuracy of 77.5%.     

Time-frequency analysis of the first heart sound. Part 2: An appropriate time-frequency representation technique

A simulated first heart sound (S1) signal is used to determine the best technique for analysing physiological S1 from the following five time-frequency representations (TFR): the spectrogram, time-varying autoregressive modelling, binomial reduced interference distribution, Bessel distribution and cone-kernel distribution (CKD). To provide information on the time and frequency resolutions of each TFR technique, the instantaneous frequency and the −3 dB bandwidth as functions of time were computed for each simulated component of the S1. The performance index for selecting the best technique was based on the relative error and the correlation coefficient of the instantaneous frequency function between the theoretical distribution and the computed TFR. This index served to select the best technique. The sensitivity of each technique to noise and to small variations of the signal parameters was also evaluated. The results of the comparative study show that, although important limitations were found for all five TFRs tested, the CKD appears to be the best technique for the time-frequency analysis of multicomponent signals such as the simulated S1.     

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.     

Analysis of EEG transients by means of matching pursuit

Matching pursuit (MP), a new technique of time-frequency signal analysis, was applied to simulated signals and the awake and sleep EEG. With the MP algorithm, waveforms from a very large class of functions were fitted to the local signal structures in a recursive procedure. By means of this technique, sleep spindles were localized in the time-frequency plane with high precision, and their intensities and time spans were found. The MP technique makes following the temporal evolution of transients and their propagation in brains possible. It opens up new possibilities in EEG research providing a means of investigation of dynamic processes in brains in a much finer time-frequency scale than any other method available at present.     

Automatic de-noising of knee-joint vibration signals using adaptive time-frequency representations

A novel de-noising method for improving the signal-to-noise ratio of knee-joint vibration signals (also known as vibro-arthrographic (VAG) signals) is proposed. The de-noising methods considered are based on signal decomposition techniques, such as wavelets, wavelet packets and the matching pursuit (MP) method. Performance evaluation with synthetic signals simulated with the characteristics expected of VAG signals indicates good de-noising results with the MP method. Statistical pattern classification of non-stationary signal features extracted from time-frequency distributions of 37 (19 normal and 18 abnormal) MP method-de-noised VAG signals shows a sensitivity of 83.3%, a specificity of 84.2% and an overall accuracy of 83.8%.     

基于周期提取的心音分段方法

目的心音分段是心音分析的基础,传统方法是利用心音基本成分进行识别,而病变的心音信号中含有的杂音使识别受到干扰,易产生误分段。本文提出了基于周期提取的信号分段方法,可以避免对心音基本成分的识别。方法以虚拟仪器Lab VIEW为开发平台,首先利用小波变换对原始心音进行去噪预处理,然后利用快速Hilbert变换提取心音包络,再利用其自相关分析函数求出心动周期,进而从原始心音信号中提取整周期的信号,避免对心音基本成分的识别。结果对30例心音信号做实验验证,得到的心动周期长度能够直观显示,正确率达98%以上。结论作为一种无需识别心音基本成分的分段方法,此方法为后续的特征提取等研究打下了坚实基础。     

Automatic measure of the split in the second cardiac sound by using the wavelet transform technique

This paper is concerned with the identification and automatic measure of the split in the second heart sound (S2) of the phonocardiogram signal (PCGs) for normal or pathological case. The second heart sound S2 consists of two acoustic components A2 and P2, the former is due to the closure of the aortic valve and the latter is due to the closure of the pulmonary valve. The aortic valve usually closes before the pulmonary valve, introducing a time delay known as "split". A automatic technique based on the discrete wavelet transform (DWT) and the continuous wavelet transform (CWT) is developed in this paper to measure the split of the second cardiac sound (S2) for the normal and pathological cases of the PCG signals. To quantify the splitting, the two components in S2 (i.e. A2 and P2) are identified and, the delay between the two components can be estimated. It is shown that the wavelet transform can provide best information and features of the split of S2 and the major components (A2 and P2) and consequently aid in medical diagnosis.     

一种基于短时傅立叶变换的心音分裂时间计算方法

目的应用现代信号处理的方法定量计算心音分裂的时间间隔，为某些心脏早期器质性病变的诊断提供数据依据。方法在频率分辨率较高的情况下，利用短时傅立叶变换（STFT）声谱图和香农能量，提取出第1心音（S1）的主要成分二尖瓣关闭音（M1）、三尖瓣关闭音（T1）及第2心音（S2）的主要成分主动脉瓣关闭音（A2）、肺动脉瓣关闭音（P2）。然后，在时间分辨率较高的情况下，通过瞬时能量密度包络图，计算出心音分裂的时间间隔。结果对南开大学医学院提供的心音数据的仿真结果表明该方法能够较精确地计算出房间隔缺损（ASD）、右束支传导阻滞（RBBB）及其他常见心音分裂类型的分裂时间。结论笔者提出的计算心音分裂时间间隔的方法比已有的方法更简单快捷，其结果能够为某些心脏早期器质性病变的诊断提供定量依据。     

Diastolic mechanics and the origin of the third heart sound

The third heart sound (S₃) is observed for various hemodynamic conditions in both the normal and diseased heart. A theory is proposed in which myocardial viscoelasticity is primarily responsible for S₃. A mathematical model is developed based on the mechanical aspects of diastolic function: nonlinear elasticity, viscoelasticity, and pressure generation. The model is provided as an electrical analogy of the left ventricle and circulatory system. S₃ is predicted for the normal heart and the heart with dilated cardiomyopathy. An elevation of S₃ intensity is indicated for cardiomyopathy, as is often observed in the clinic. S₃ is produced experimentally by volume loading of the open-chest canine preparation and mathematically by imposing the conditions of volume loading on the model. Consistency of theory and experiment imply that it is valid to attribute S₃ to myocardial viscoelasticity. The animal whose heart possessed the largest constant of viscoelasticity produced the greatest level of S₃, in both cases. Nonlinear ventricular compliance is not found to be an essential requirement for sound generation, although increased compliance led to an increase in sound. S₃ is predicted to change in response to venous return, ventricular stiffness, contractility, heart rate, and duration of contraction, as observed by others. In general, the coupling of these quantities to S₃ is explained in terms of an excitation of viscous properties of the ventricle.