Reduction of heart sound interference from lung sound signals using empirical mode decomposition technique

During the recording time of lung sound (LS) signals from the chest wall of a subject, there is always heart sound (HS) signal interfering with it. This obscures the features of lung sound signals and creates confusion on pathological states, if any, of the lungs. A novel method based on empirical mode decomposition (EMD) technique is proposed in this paper for reducing the undesired heart sound interference from the desired lung sound signals. In this, the mixed signal is split into several components. Some of these components contain larger proportions of interfering signals like heart sound, environmental noise etc. and are filtered out. Experiments have been conducted on simulated and real-time recorded mixed signals of heart sound and lung sound. The proposed method is found to be superior in terms of time domain, frequency domain, and time-frequency domain representations and also in listening test performed by pulmonologist.     

Heart sound cancellation from lung sound recordings using time-frequency filtering

During lung sound recordings, heart sounds (HS) interfere with clinical interpretation of lung sounds over the low frequency components which is significant especially at low flow rates. Hence, it is desirable to cancel the effect of HS on lung sound records. In this paper, a novel HS cancellation method is presented. This method first localizes HS segments using multiresolution decomposition of the wavelet transform coefficients, then removes those segments from the original lung sound record and estimates the missing data via a 2D interpolation in the time-frequency (TF) domain. Finally, the signal is reconstructed into the time domain. To evaluate the efficiency of the TF filtering, the average power spectral density (PSD) of the original lung sound segments with and without HS over four frequency bands from 20 to 300 Hz were calculated and compared with the average PSD of the filtered signals. Statistical tests show that there is no significant difference between the average PSD of the HS-free original lung sounds and the TF-filtered signal for all frequency bands at both low and medium flow rates. It was found that the proposed method successfully removes HS from lung sound signals while preserving the original fundamental components of the lung sounds.     

Heart sound and lung sound separation algorithms: a review

Breath and cardiac sounds are two major bio sound signals. In this, heart sounds are produced by movement of some body parts such as heart valve, leaflets and the blood flow through the vessels, whereas lung sounds generates due to the air in and out flow through airways during breathing cycle. These two signals are recorded from chest region. These two signals have very high clinical importance for the patient who is critically ill. The lung functions and the cardiac cycles are continuously monitored for such patients with the help of the bio sound signal captured using suitable sensing mechanism or with auscultation techniques. But these two signals mostly superimpose with each other, so the separation of these heart sound signals (HSS) and the lung sound signals (LSS) is of great research interest. There are so many different techniques proposed for this purpose. In this paper, a study is carried out on different algorithms used for the separation of HSS from LSS, and also the results of major four separation algorithms are compared.     

Heart sound cancellation from lung sound record using cyclostationarity

From the mechanism of heart sound generation, it is known that heart sounds are cyclic following the frequency of the heartbeat. This paper proposes a short-time cyclic frequency spectrum to calculate the instantaneous cycle frequency (ICF) of heart sounds as an estimation of the frequency of the heartbeat. Heart sounds in a lung sound record are detected with the assistance of ICF. Lung sounds (LSs) are recovered by removing heart sounds from the LS record. An LS record is the only input signal source; no other reference signal is necessary. Evaluation by visual inspection, auditory listening and spectral analysis all show that heart sounds are successfully cancelled without hampering the main components of lung sounds.     

Cardiac sound murmurs classification with autoregressive spectral analysis and multi-support vector machine technique

In this paper, a novel cardiac sound spectral analysis method using the normalized autoregressive power spectral density (NAR-PSD) curve with the support vector machine (SVM) technique is proposed for classifying the cardiac sound murmurs. The 489 cardiac sound signals with 196 normal and 293 abnormal sound cases acquired from six healthy volunteers and 34 patients were tested. Normal sound signals were recorded by our self-produced wireless electric stethoscope system where the subjects are selected who have no the history of other heart complications. Abnormal sound signals were grouped into six heart valvular disorders such as the atrial fibrillation, aortic insufficiency, aortic stenosis, mitral regurgitation, mitral stenosis and split sounds. These abnormal subjects were also not included other coexistent heart valvular disorder. Considering the morphological characteristics of the power spectral density of the heart sounds in frequency domain, we propose two important diagnostic features Fmax and Fwidth, which describe the maximum peak of NAR-PSD curve and the frequency width between the crossed points of NAR-PSD curve on a selected threshold value (THV), respectively. Furthermore, a two-dimensional representation on (Fmax, Fwidth) is introduced. The proposed cardiac sound spectral envelope curve method is validated by some case studies. Then, the SVM technique is employed as a classification tool to identify the cardiac sounds by the extracted diagnostic features. To detect abnormality of heart sound and to discriminate the heart murmurs, the multi-SVM classifiers composed of six SVM modules are considered and designed. A data set was used to validate the classification performances of each multi-SVM module. As a result, the accuracies of six SVM modules used for detection of abnormality and classification of six heart disorders showed 71-98.9% for THVs=10-90% and 81.2-99.6% for THVs=10-50% with respect to each of SVM modules. With the proposed cardiac sound spectral analysis method, the high classification performances were achieved by 99.9% specificity and 99.5% sensitivity in classifying normal and abnormal sounds (heart disorders). Consequently, the proposed method showed relatively very high classification efficiency if the SVM module is designed with considering THV values. And the proposed cardiac sound murmurs classification method with autoregressive spectral analysis and multi-SVM classifiers is validated for the classification of heart valvular disorders.     

Towards classifying non-segmented heart sound records using instantaneous frequency based features

Abstract

Heart sound and its recorded signal which is known as phonocardiograph (PCG) are one of the most important biosignals that can be used to diagnose cardiac diseases alongside electrocardiogram (ECG). Over the past few years, the use of PCG signals has become more widespread and researchers pay their attention to it and aim to provide an automated heart sound analysis and classification system that supports medical professionals in their decision. In this paper, a new method for heart sound features extraction for the classification of non-segmented signals using instantaneous frequency was proposed. The method has two major phases: the first phase is to estimate the instantaneous frequency of the recorded signal; the second phase is to extract a set of eleven features from the estimated instantaneous frequency. The method was tested into two different datasets, one for binary classification (Normal and Abnormal) and the other for multi-classification (Five Classes) to ensure the robustness of the extracted features. The overall accuracy, sensitivity, specificity, and precision for binary classification and multi-classification were all above 95% using both random forest and KNN classifiers.     

Robust heart sound detection in respiratory sound using LRT with maximum a posteriori based online parameter adaptation

This paper investigates the utility of a likelihood ratio test (LRT) combined with an efficient adaptation procedure for the purpose of detecting the heart sound (HS) with lung sound and the lung sound only (non-HS) segments in a respiratory signal. The proposed detection method has four main stages: feature extraction, training of the models, detection, and adaptation of the model parameter. In the first stage, the logarithmic energy features are extracted for each frame of respiratory sound. In the second stage, the probabilistic models for HS and non-HS segments are constructed by training Gaussian mixture models (GMMs) with an expectation maximization algorithm in a subject-independent manner, and then the HS and non-HS segments are detected by the results of the LRT based on the GMMs. In the adaptation stage, the subject-independent trained model parameter is modified online using the observed test data to fit the model parameter of the target subject. Experiments were performed on the database from 24 healthy subjects. The experimental results indicate that the proposed heart sound detection algorithm outperforms two well-known heart sound detection methods in terms of the values of the normalized area under the detection error trade-off curve (NAUC), the false negative rate (FNR), and the false positive rate (FPR).     

心音特征在慢性心力衰竭分型辅助诊断中的应用研究

提取心音时域和时频域特征,比较分析射血分数降低型心衰(HFrEF)和射血分数保留型心衰(HFpEF)患者各特征之间的关系。共采集了72列HFrEF患者和172列HFpEF患者20分钟的心音数据,提取第一心音与第二心音时限之比(TS1/TS2)、第一心音与第二心音幅值之比(S1/S2)、舒张期时限与收缩期时限之比的总体标准差(SDDS)、S1间期总体标准差(SDSSI)等4个时域特征。S变换分析其时频域特性,提取第一心音能量与第二心音能量之比(ES1/ES2),低频能量分数(EF-LF)、高频能量分数(EF-HF)、收缩期低频能量分数(EF-SLF)和高频能量分数(EF-SHF)、舒张期低频能量分数(EF-DLF)和高频能量分数(EF-DHF)7个时频域特征,分别进行统计学分析和聚类分析。TS1/TS2、S1/S2、SDDS、SDSSI、ES1/ES2、EF-SLF、EF-DLF在两组间均有统计学差异(P＜0.05);EF-LF、EF-HF、EF-SHF、EF-DHF无统计学意义(P＞0.05)。选择其中4个相对独立的特征值进行聚类分析,区分HFrEF组和HFpEF组的灵敏性和特异性分别为93.06%和84.88%。提取的心音特征反映了两组信号的差异性,为心音信号在慢性心力衰竭分型辅助诊断中的应用提供了理论依据。     

基于STMHT算法的心音分割研究

第一心音（S1）和第二心音（S2）的定位和提取是利用心音分析诊断心脏病时的首要任务。鉴于此,本研究提出一种基于STMHT的心音分割法,分别提取S1和S2。本研究分为以下3个阶段:第一阶段,采用小波分解对心音信号进行预处理,保留心音信号的有效成分（21.5~689.0 Hz）;第二阶段,用Viola积分波形法提取心音包络;最后,基于STMHT算法自动定位和提取S1和S2。对30例心音信号的提取结果进行评价,结果表明,S1和S2提取的准确率高达97.37%,优于其它已实现的有效方法。     

A robust heart sound segmentation algorithm for commonly occurring heart valve diseases

The first step towards detection of valvular heart diseases from heart sound signal (phonocardiogram) is segmentation. A segmentation algorithm provides the location of the first and second heart sounds which in turn helps to locate and analyse the murmur. Established phonocardiogram based segmentation methods use an electrocardiographic (ECG) signal as a continuous auxiliary input in a complex instrumentation setup. This paper proposes an automatic segmentation method that does not require any such auxiliary signal. Compared to other approaches without auxiliary signal, this work extensively utilizes biomedical domain features for reduction of time and computational complexities and is more accurate. The performance of the algorithm is evaluated for nine commonly occurring pathological cases and normal heart sound for various sampling frequencies, recording environments and age group of subjects. The proposed algorithm yields an overall accuracy of 97.47% and is compared with two competing techniques. In addition, the robustness of the algorithm is shown against additive white Gaussian noise contamination at various SNR levels.     

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.     

基于最大Lyapunov指数的心音信号混沌特性分析

目的寻求无创伤的且能自适应信号变化的方法区分正常和异常的心音信号,为临床诊断提供更简捷的参考方法。方法本文以心音信号非线性时间序列最大Lyapunov指数为主线,根据心音信号不同阶段特性的统一性,提出了对信号分阶段进行研究的方法。首先对7种具有代表性的正常和异常心音信号的S1、S2心音分别分3阶段进行相空间重构,然后结合各阶段求得的相空间重构参数计算对应的最大Lyapunov指数,最后对正常、异常心音信号最大Lyapunov指数均值进行比较分析。结果正常S1心音信号的最大Lyapunov指数均值0．1450,远大于异常S1心音信号,正常S2心音信号的最大Lyapunov指数均值也比异常s2心音信号大很多。结论心音信号中确实存在混沌现象,且正常（健康）心脏运动到S1和S2阶段的混沌程度要比异常（病态）时高。     

A filter bank-based source extraction algorithm for heart sound removal in respiratory sounds

In this paper, we consider the problem of heart sounds (HS) removal from respiratory sounds (RS), and a novel semi-blind single-channel source extraction algorithm is proposed. The proposed method is able to extract the underlying pure RS from the HS corrupted noisy input signals by incorporating the filter banks and template-based matching using FIR filters. For performance evaluation of the presented method, the average power spectral densities (PSD) of the input RS segments without HS have been compared with the PSD of the reconstructed signals over six selected frequency bands from 20 to 800 Hz. The proposed method is tested for various types of RS recordings and found effective by yielding an overall maximum spectral difference of for a frequency range below 800 Hz.     

Performance analysis of seismocardiography for heart sound signal recording in noisy scenarios

This paper presents a system based on Seismocardiography (SCG) to monitor the heart sound signal for the long-term. It uses an accelerometer, which is of small size and low weight and, thus, convenient to wear. Such a system should also be robust to various noises which occur in real life scenarios. Therefore, a detailed analysis is provided of the proposed system and its performance is compared to the performance of the Phoncardiography (PCG) system. For this purpose, both signals of five subjects were simultaneously recorded in clinical and different real life noisy scenarios. For the quantitative analysis, the detection rate of fundamental heart sound components, S1 and S2, is obtained. Furthermore, a quality index based on the energy of fundamental components is also proposed and obtained for the same. Results show that both the techniques are able to acquire the S1 and S2, in clinical set-up. However, in real life scenarios, we observed many favourable features in the proposed system as compared to PCG, for its use for long-term monitoring.     

基于功率谱密度相似性的最佳心音信号选择

心音采集过程中混入的干扰噪声影响着心音诊断,目前多通过手动方式选择干扰较少的信号段做后续分析。为从采集信号中筛选出干扰最少、稳定性最强的最佳心音信号,提出一种最佳心音信号的自动选择方法。对采集的25例正常和119例患先天性心脏病儿童的心音信号,基于离散小波变换与哈达玛积相结合定位心动周期。根据心动周期信号的周期稳定性及功率谱密度相似性计算质量因子,将质量因子最大的连续3个心动周期信号作为最佳心音信号。由心脏病专家通过音频回放对信号选择的成功率和有效性进行评估。结果表明,最佳心音信号自动选择的成功率为95.83%,选择成功信号均包含对应疾病的典型听诊特点。该方法选择性能良好且自动执行,为心音信号的全自动分析提供参考。     

基于信号包络及短时过零率的心音分段算法

心音能有效地反应心脏尤其是瓣膜活动状况,研究基于心音的心脏病决策系统具有重大意义.心音分段是建立心音决策系统的基础和前提,其目的是定位心音的主要成份,为特征提取与模式识别提供定位基准.本文通过使用双门限、迭代等方法,改进了基于信号能量的分段算法,并首次引人短时过零率以更准确地定位分段边界.实验结果表明,该算法对正常心音及常见异常心音分段效果良好.     

心音时域分析的新方法研究

为快速、准确地判断心音的正常与否,本文提出一种新的心音时域分析方法——心音特征波形法。通过数字听诊器将采集到的心音数据由USB接口传输到计算机,建立单自由度分析模型提取心音特征波形,计算出心音特征参数来判别正常与异常的心音。文中通过对正常/异常心音案例分析,验证了心音特征波形法的有效性。另外,为检验已提出的心音时域分析方法对正常与异常的心音判别准确率,选用已采集的40组正常与20组异常的心音数据进行实验及统计分析,准确率分别达到92.5%和95.0%。     

超声多普勒管壁搏动和血流信号的分离研究

本研究提出利用经验模式分解(EMD)算法分解混叠有管壁成分的超声多普勒血流信号来实现管壁搏动和血流信号的分离。该方法首先将混叠有管壁搏动的超声多普勒血流信号分解为少量有限的分量,即内模函数(IMFs),然后根据管壁搏动信号与血流信号的功率比变化曲线,用比值法自动确定并去除低频管壁博动成分。在仿真实验中用提出的方法处理模拟的多普勒信号,对于靠近管腔内壁的血流信号其在频域功率谱上的相对误差为50%,在时域幅度的相对误差为45%,与高通滤波器方法的相对误差95%相比,准确性得到提高。基于个人计算机用C语言编程实现提出的算法,对实际采集的人体颈动脉多普勒信号可实现实时分离处理。结果表明:基于经验模式分解的滤波方法能有效客观地滤除管壁搏动信号,更准确地保留低频血流信号成分。     

一种基于声卡的人体脉搏波采集系统的研制

本文在讨论了人体动脉脉搏波频域特点以及声卡技术特征的基础上，提出用声卡采集动脉脉搏波的一种方法。利用线性调幅的方法，可以将脉搏信号的频谱移动到声卡所能采集的范围内，通过声卡采集后，在数字域中进行调幅信号的解调，从而恢复脉搏信号；同时还讨论了普通调幅波的一种实现方法及数字调幅波的解调原理。     

心音信号分段规则的探讨

目的 如何有效提取心音信号的有效成分（第一心音S1、第二心音S2）是分析心音信号的关键。为提取心音信号的有效成分,必须明确心音信号的分段规则。方法 首先对目前心音研究领域中常用的两类心音分段方法进行分析和比较。根据现有文献资料,结合作者对实际采集的829例心音实例的研究,提取心音的各时域特征并进行统计分析,最后对心音信号分段规则进行了探讨。结果 心音信号的S1、S2及收缩期、舒张期等时域特征呈现一定规律性。结论 可按照上述时域特征对心音信号进行自动分段,并借助心音分段规则进行进一步识别和分析。