Detection of Mitral Valve Diseases by Bicoherence Analysis of Heart Sound Signals

The analysis of 10 normal and 51 mitral valve pathology making a total of 61 heart sound signals that were obtained with Littmann 4100 Digital Stethoscope were conducted in this study.Following the recorded sound signals were denoised by using wavelet filters,the signals were applied bicoherence analysis that is an high order spectral analysis method.It has been demonstrated that varieties of mitral valve pathology could be determined by three-dimensional surfaces of bicoherence and maximum bicoherence values.

Development of laser-based heart sound detection system

In this paper, we demonstrate the prototype of a new stethoscope using laser technology to make the heart-beat signal “visible”. This heartbeat detection technique could overcome the limitation of the acoustic stethoscope brought by the poor ability of human ear to hear low frequency heart sounds. This is important, as valuable information from sub-audio sounds is present at frequencies below the range of human hearing. Moreover, the diagnostic accuracy of the acoustic stethoscope is also very sensitive to noise from immediate environment. In the prototype of laser-based stethoscope, the heartbeat signal is correlated to the optical spot of a laser beam reflected from a thin mirror attached to the patient’s chest skin. The motion of the mirror with the chest skin is generated by the heart sounds. A linear optical sensor is applied to detect and record the motion of the optical spot, from which the heartbeat signal in time-domain is extracted. The heartbeat signal is then transformed to frequency domain through digital signal processing. Both time-domain and frequency-domain signals are analyzed in order to classify different type of heart murmurs. In the prototype of the laser-based stethoscope, we use a heart-sound box to simulate the chest of a human being. The heart-sounds collected from real patients are applied to activate the vibration of the heart-sound box. We also analyze different heart murmur patterns based on the time-domain and frequency-domain heartbeat signals acquired from the optical system.

Research on Heart Sound Signal Denoising Algorithm Based on Variational Mode Decomposition and Wavelet Threshold

Heart sound signals are easy to introduce noise during the acquisition process, and traditional denoising algorithms always remove the characteristic information of the heart sound while removing the noise. The denoising effects in turn affect the subsequent diagnosis results. So an improved algorithm based on variational mode decomposition (VMD) and wavelet threshold method is proposed. First, the number of decomposition modes K of the VMD is selected by analyzing the average instantaneous frequency curve of the different decomposition values, and the noisy heart sound is decomposed into K modes by the VMD algorithm. Then, the modes that need to be retained are decided by the energy curve of each mode. Finally, wavelet threshold denoising method is performed on the retained modes. Experiment simulation results show that under different signal-to-noise ratio conditions, the proposed method can improve heart sounds’ ratio of signal to noise and reduce the root mean square error. Compared with traditional algorithms, it has good noise suppression capabilities under different noise levels.

Sound Narrative of the Suppressed Species in Heart of Darkness

Joseph Conrad in Heart of Darkness calls for the suppressed species on humanitarian grounds.In this sense,this paper aims to dredge how the suppressed species utter their voices of rebellion and form a special soundscape.The African natives make use of unique ways of drumming and shouting,to tell how they are enslaved and how they resist the domination of the British pilgrims.In the meanwhile,the black female also uses voices to rebel against her marginal condition compared to males,including white and her native males.By putting the story’s sound narrative in the framework of ecofeminism,this paper intends to not only disclose the rebellious spirit of those suppressed species,but also raise people’s awareness of caring for the living creatures in the marginal status,including women,the blacks,animals,and the ecological environment as a whole.

Study on the Heart Sound Signal Denoising Technology based on Integrated Filtering Algorithm

In the previous studies of heart sounds, the calculation model of small waveform is often used, and new waveform graph is formed through the decomposition and restructuring of small waveform so as to remove the noise from the new waveform. There are a lot of shortcomings in the use of such a method. The features of new waveform are difficult to be controlled, and thus the noise generated by the wave and the interference of wave will be disturbed by the filter to certain degree. In this paper, the integrated faltering algorithm is introduced, and a wave can be used in the studied use of small waveform, and also the high-order algorithm in mathematics is used, so that the frequency is controlled in a certain range, the frequency of heart sounds to be interfered is effectively reduced, and also the harmonic harm generated by the waveform is considered. After the signal sources are protected with some technologies, the effect of filtering and denoising is eventually achieved.

Machine Learning-Based Intelligent Auscultation Techniques in Congenital Heart Disease:Application and Development

Congenital heart disease(CHD),the most prevalent congenital ailment,has seen advancements in the“dual indi-cator”screening program.This facilitates the early-stage diagnosis and treatment of children with CHD,subse-quently enhancing their survival rates.While cardiac auscultation offers an objective reflection of cardiac abnormalities and function,its evaluation is significantly influenced by personal experience and external factors,rendering it susceptible to misdiagnosis and omission.In recent years,continuous progress in artificial intelli-gence(AI)has enabled the digital acquisition,storage,and analysis of heart sound signals,paving the way for intelligent CHD auscultation-assisted diagnostic technology.Although there has been a surge in studies based on machine learning(ML)within CHD auscultation and diagnostic technology,most remain in the algorithmic research phase,relying on the implementation of specific datasets that still await verification in the clinical envir-onment.This paper provides an overview of the current stage of AI-assisted cardiac sounds(CS)auscultation technology,outlining the applications and limitations of AI auscultation technology in the CHD domain.The aim is to foster further development and refinement of AI auscultation technology for enhanced applications in CHD.

基于CEEMDAN与自相关函数的心音去噪

为有效去除心音信号中的噪声,提出基于CEEMDAN(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)与自相关函数的心音去噪算法。首先,通过CEEMDAN将含噪的心音信号分解为具有不同尺度特征的IMF(Intrinsic Mode Function)分量;其次,根据噪声与心音的自相关函数性质不同,界定IMF分量的信噪分界点;最后,对以噪声为主的IMF分量进行均值滤波,并将其与以心音为主的IMF分量重构得到去噪后信号。实验表明,在不同的噪声水平下,与小波软阈值去噪算法、小波硬阈值去噪算法、CEEMDAN去噪算法相比,所提算法的信噪比最高,均方根误差最小,在去除噪声的同时,可以较好地保留心音信号中的有效信息。

Heart valve closure timing intervals in response to left ventricular blood pressure

This article investigates the relationships between heart valve closure timing intervals and left ventricular systolic blood pressure (LVSBP). For this investigation, the cardiopulmonary system is modeled as an analog circuit, including heart chambers, the distal and proximal aorta, distal and proximal systemic arteries/veins, systemic capillaries, the vena cava, the distal and proximal pulmonary artery, distal and proximal pulmonary arteries/veins, pulmonary capillaries and physiological control of heart rate and cardiac contractibility. In this model, the ventricles, atria and arteries were modeled as advanced pressur-volume relationships. A vagal-sympathetic mechanism was adopted to simulate transient systemic and pulmonary blood pressure. Four intervals, i.e., the timing interval between mitral and aortic valve closure (TIMA), the timing interval between aortic and mitral valve closure (TIAM), the timing interval be- tween aortic and pulmonary valve closure (TIAP) and the timing interval between mitral and tricuspid valve closure (TIMT), are further defined in a heart cycle to illustrate their relationships to LVSBP. Simula- tions showed that the TIMA, TIAM and TIAP have strong negative correlations with LVSBP;meanwhile, the TIMT has a slightly negative relationship with LVSBP. To further validate the relationships, 6 healthy male subjects were experimentally evaluated. The intervals were extracted from non-invasively sampled heart sound signals taken from the surface of the thorax. The experiments showed relationships consistent with those obtained by simulations. These relationships may have potential applications for noninvasively accessing LVSBP in real-time with a high time resolution of one heartbeat.

心音频谱图在二尖瓣反流患者左心室功能监测中的作用

目的探求使用可穿戴设备获取心音频谱图,观察不同程度二尖瓣反流(MR)患者心音频谱特征在左心室功能监测中的作用。方法入选2020年6月至2021年5月因心血管疾病入住上海交通大学医学院附属瑞金医院的102例MR患者,根据心脏超声MR反流束面积/左心房面积比值超过20%将其分为轻度MR组72例和中度及以上MR组30例。基于心音频谱图对心音持续时间、最大声强、最大心音振幅进行分析,并与心脏超声测定的左心室射血分数(LVEF)进行相关性分析。结果心音频谱图第一心音持续时间与收缩期持续时间比值(S11-S12/S11-S21)与LVEF呈线性负相关(r=-0.535,P<0.01),S11-S12/S11-S21用于判定LVEF降低(LVEF<50%)曲线下面积为0.840(95%CI=0.756~0.924,P<0.01),截断值是55.50%,敏感度78.6%,特异度83.3%。结论心音频谱图提示,MR患者心脏收缩期第一心音持续时间占比与LVEF呈负相关性,在二尖瓣病患的心功能居家监测中存在潜在的应用价值。

基于活动性检测动态估计噪声的心音降噪算法

针对基于小波分解和最优改进对数幅度谱估计的心音降噪算法存在噪声残留和心音失真的问题,提出一种基于心音活动性检测(HSAD)动态估计噪声的心音降噪算法。通过设计的HSAD判断当前心音帧是否为基础心音帧(FHS),根据判断结果分别采用改进最小值控制递归平均(IMCRA)算法和递归平滑算法对噪声功率进行动态估计与更新,采用非因果先验信噪比,实现心音信号的降噪。实验结果表明,提出算法能更好在提升降噪性能的同时,降低FHS的失真。

面向可穿戴式的基于LSTM神经网络的智能心音异常诊断芯片

心血管疾病是造成全球死亡人数最多的疾病之一,因此对心血管疾病的预防与提前诊断至关重要。人工听诊技术与计算机心音诊断技术无法满足对心音长时间听诊的需求,因而可穿戴式听诊设备越来越受到关注,但是其具有高精度与低功耗的要求。该文设计了低功耗的面向可穿戴式的基于长短期记忆网络(Long Short-Term Memory, LSTM)的智能心音异常诊断芯片,提出了包括预处理、特征提取以及异常诊断的心音异常诊断系统,并搭建了基于听诊器的心音采集FPGA系统,采用了数据增强的方法解决数据集的不平衡问题。基于预训练模型设计了智能心音异常诊断芯片,在SMIC180 nm工艺下完成了版图设计和MPW流片。后仿真结果表明,智能心音异常诊断芯片的诊断准确率为98.6%,功耗为762μW,面积为3.06 mm×2.45 mm,满足可穿戴式智能心音异常诊断设备的高性能与低功耗的需求。

MEMS心音传感器及检测电路优化设计

对基于微电子机械系统(MEMS)技术的心音传感器声敏结构进行了优化且设计了其检测电路。首先,针对心音信号的特点,设计了二次集成的扁平状仿生纤毛结构,对该结构进行仿真,确定了纤毛的尺寸参数和梁上最大应力1.2×10^(5) N/m^(2),对纤毛进行特征频率仿真,在硅油域中结果为711 Hz;其次由扁平状纤毛结构X轴接收噪声时梁上的应力仿真结果可知该结构具有抗干扰能力;最后设计了后端的放大电路和滤波电路并对传感器封装后进行测试。测试结果表明,该结构的信噪比达到了27 dB,较传统的圆柱形纤毛提高了35%,且其抗干扰能力也优于传统的圆柱形纤毛。优化过后的MEMS心音传感器具有抗干扰、低噪声、低成本、采集的信号不失真等优势,可为临床心音信号的采集提供关键核心部件。

基于Bi-LSTM与状态约束的心音分割算法

心音分割是进行准确心音分类的前提。针对心音分割,提出一种基于双向长短时记忆网络(Bi-LSTM)与状态约束的算法。该文通过网格法确定Bi-LSTM网络中的最佳参数,并训练出心音状态识别模型;统计Bi-LSTM预测的心音状态持续时间,并计算自相关参数;利用自相关参数和心音固有状态转移规则对预测的心音状态进行约束处理。使用五折交叉验证法在PhysioNet/CinC 2016数据集上进行测试,该算法与同类算法相比,整体性能更佳。

基于时频融合特征的肺动脉高压心音分类模型

先心病相关肺动脉高压是一种严重的心血管疾病,致死率高,对其进行早期筛查与识别对于治愈尤为重要。目前临床是通过右心导管术确诊,此为有创检查,不便于在大规模筛查中采用,研究一种无创便捷的识别方法迫在眉睫。文中建立了一种时频融合的心音分类模型。首先对心音信号进行预处理,然后使用融合滤波器组对信号进行转换并求取动态时频特征,最后将得到的融合特征参数输入表格式先验数据拟合网络(TabPFN)中进行分类识别。实验结果表明,该算法在正常、CHD-PAH和CHD中的平均准确率、精确率、灵敏度、特异度和F1分别为92.21%,92.15%,92.15%,96.11%,92.14%。对于先心病相关肺动脉高压的早期筛查与识别具有重要意义。

基于压电传感器的电子听诊器设计及实现

压电式心音传感器作为声敏感单元,传感头一般采用硅胶类亲肤材料进行封装,但由于硅胶与皮肤的声阻抗失配较大,所以心音传感器普遍存在信噪比低、易受外界干扰等缺陷。针对压电式传感器封装存在的问题,提出了一种液体式封装方法。该方法针对压电传感器的结构和封装要求设计了电子听诊器的封装壳体和封装工艺;仿真了不同液体环境下的声场衰减,并制备了以封装去离子水和硅油作为过渡层的电子听诊器探头;用封装后的传感器测试心音信号,并与3M听诊器进行对比。测试结果表明,采用去离子水封装的传感器测到的心音信号幅值更大,且其信噪比远大于3M听诊器。

心音心电检测仪电磁兼容问题的测试分析及整改方法

现代医疗器械更偏向于高精确度、高敏感度和小型化,其所处的电磁环境越来越复杂。外界电磁干扰对医疗设备会产生影响,严重时甚至影响医生的诊断。本文结合心音心电检测仪,基于医疗器械测试标准对传导发射测试、辐射发射测试和静电放电这3个测试项目进行了研究,提出了壳体整改,增加滤波电路、磁环、电荷释放电路等整改方法。对其他类型相关医疗器械的电磁兼容问题分析和整改,有一定的参考借鉴作用。

改进变值逻辑与线性预测在心音分类中的应用

心音对于评价心脏健康状况具有重要作用.文章介绍了一种新的基于变值逻辑与线性预测倒谱系数融合特征的先心病分类算法,有助于提取心音中的深度病理特征.算法首先对心音进行降噪、包络提取;然后进行变值逻辑运算、标记并转换为可分析的测度数据,并计算信号的线性预测倒谱系数进行特征融合;最后使用随机森林,XGBOOST和LIGHTGBM机器学习分类器进行先心病二分类.研究所用心音样本共4000例,测试结果对正常和异常心音分类的平均准确率为0.9138.算法无需对心音进行心动周期分割,大大简化了分析流程,可望用于先心病的筛查.

心音特征智能分析在心血管功能评估和疾病诊断中的应用

数字化心音与人工智能技术的结合可实现对心音进行精确地连续、定量分析和分类识别,使心音特征提取与融合分析在心脏血流动力学监测、心力衰竭分型诊断、先天性或风湿性心脏病分类、冠状动脉疾病检测等领域成为研究热点。本文综述心音特征智能分析在心血管功能评估和疾病诊断中的应用。

基于改进NMF与相位补偿的胎心音降噪算法

在孕妇体表,通过声音传感器对胎儿心音信号进行采集,结果通常包含多种噪声,常用的滤波方法无法取得满意的降噪效果。为了能够更好地滤除与胎儿心音信号同频的噪声信号,提出改进的非负矩阵分解算法(NMF),对短时幅度谱利用奇异值分解得到用于NMF的特征数和初始化矩阵,对NMF增加L2,1稀疏限制,使分解得到的胎心音特征呈现更多细节;对短时相位谱采用了增加稀疏限制的相位补偿算法(PSC)。实验结果表明:与常用降噪方法相比,该方法的信噪比至少提升0.52 dB,能更好保留胎儿心音信号特征。

心肺音分离方法研究进展

听诊是诊断心血管和呼吸系统疾病最有效的方法。为了达到准确诊断的目的,设备必须能够识别各种临床情况下的心肺音。然而,记录的胸腔声音通常为心肺音混合信号。因此,将心肺音混合信号分离对于医生听诊至关重要。本文介绍了心音信号和肺音信号的频率范围和信号特征,综述了目前心肺音分离方法的研究进展,阐述了现有各种心肺音分离方法的优缺点,指出了选取一种合适的心肺音分离算法来分离心肺音对辅助医疗的重要意义。