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.

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.

Theoretical Prediction of the Acoustical Nonlinear Field Radiated from a Concave Focusing Source with a Wide Aperture Angle

We propose a time-domain theoretical approach to predict the acoustic nonlinear field radiated from a concave focusing spherical source with a wide aperture angle. The nonlinear sound propagation is theoretically described by an accurate mathematical model including the continuity and momentum equations. Numerical calculation is implemented by using the finite difference time domain algorithm in the oblate spheroidal coordinate system. To examine the validity of the theoretical model, we calculate the sound fields radiated from concave spherical focusing transducers with aperture angles 30° and 40° and the results are compared with those obtained by the SBE solution.

The Murmur of Dynamic Subortic Stenosis Recognition Through A Novel Graphic Display

Background: Dynamic subaortic stenosis occurs in differing situations, commonly with hypertrophic cardiomyopathy. Regardless of the underlying cause, the resulting murmurs usually possess a characteristic sound spectral pattern, manifesting a sharp and high frequency peak occurring late in systole, often bearing a striking resemblance to the subaortic Doppler flow pattern. Methods: Murmurs found in thirty one subjects with dynamic subaortic stenosis were analyzed after having been recorded with a novel portable device capable of spectral and waveform sound displays. Results: All subjects manifested characteristic frequency patterns, consisting of high and sharp peaks occurring in late systole. With significant subaortic stenosis (resting subaortic flow velocity > 2 m/sec) this pattern was evident at rest. In the presence of little or no resting subaortic obstruction (< 2 m/sec) this pattern was produced regularly by the Valsalva maneuver. Conclusions: Dynamic subaortic stenosis produces a specific sound spectral pattern that may provide a basis for clinical evaluation, especially in early detection of this disorder and in screening situations.

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.

Validation of a Marker of Atrial Contraction in the SonR Signal

Introduction: The main component of the endocardial acceleration signal (SonR) is today used for cardiac resynchronization therapy (CRT) optimization. This prospective, single center pilot study focuses on another signal component, SonR4 that may provide further information on the atrial activity. Methods and Results: SonR signal and ECG tracings were recorded simultaneously during a CRT-D optimization procedure in 15 patients (12 men, 68 ± 9.5 years, ischemic heart disease 53%) indicated for CRT. Correlation between SonR4 signal, recorded using SonR and atrial contraction, identified by Echo Doppler was evaluated by Pearson and Student’s t tests under different Atrio-Ventricular (AV) delay programming. From 15 consecutive screened patients, 9 had concomitant analyzable SonR4 and ECG recordings and were included in the study population. The presence of the SonR4 component was systematically correlated to the presence of the A wave. A significant correlation was observed between SonR4 and A wave timings (r = 0.75, p = 0.02) according to different AV delays, with a high reproducibility in SonR4 assessment. Conclusion: A strong correlation between SonR4 and atrial contraction timings was observed, further suggesting that SonR4 is a marker of the atrial contraction. Additional assessments in larger populations are required to confirm these results and build further applications.

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.

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.

The analysis of heart sounds by using bispectral technique

An advanced signal processing technique, higher-order spectra, is proposed to in vestigate the nonlinear coupling phenomena of the heart sounds. To extract more higher-order information of the heart sounds, a non-Gaussian AR model is selected for parametric bispectral estimation in analyzing several kinds of heart sounds. The non-Gaussian AR model of the sound signals is llsed to detect quadratic nonlinear interactions and to classify two patterns of heart sounds in terms of the parametric bispectral estimate. The bispectral cross-correlation is employed to the order determination of the model. Several real heart sound data are imple mented to show that the quadratic nonlinearity exist in both normal and clinical heart sounds.It was found that bispectral techniques are effective and useful tools in analyzing heart sounds and other acoustical signals

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。

Acoustic cardiography to improve detection of coronary artery disease with stress testing

AIM:To assess if performance of 12-lead exercise tolerance testing(ETT) can be improved by simultaneous acoustic cardiography and to compare the diagnostic performances of electrocardiography(ECG) during ETT and acoustic cardiography for detection or exclusion of angiographically proven coronary artery disease(CAD).METHODS:We conducted an explorative study with retrospective data analysis using a convenience sample of consecutive patients(n = 59,mean age:62 years) from an outpatient clinic in Switzerland,who were referred for ETT by their general practitioner on suspicion of CAD,and in whom,coronary angiography was carried out.Measurements included sensitivity,specificity,likelihood ratios and receiver operating characteristic curves.A standard,symptom-limited,12-lead ECG exercise tolerance test was performed by independent persons with simultaneous acoustic cardiography and subsequent cardiac angiography for determination of significant CAD.RESULTS:Thirty-four of the 59 adult subjects(58%) had a final diagnosis of CAD by angiography,and in 25 subjects,CAD was excluded by angiography.Sensitivity/specificity of ST segment depression in the group was 29%/92%,whereas the most powerful acoustic cardiographic parameter was the strength of the fourth heart sound(S4),with corresponding sensitivity/specificity of 53%/92%.The disjunctive combination of the S4 and ST depression had sensitivity/specificity of 68%/84%.CONCLUSION:In this preliminary pilot study,the use of acoustic cardiography alone during ETT or disjunctively with ST depression has been shown to be a simple and convenient method for the detection of CAD,which was superior to ST depression on the standardized ECG.

Diagnosis of Coronary Artery Disease by Acoustic Analysis of Turbulent Murmur Caused by Coronary Artery Stenosis:A Single Center Study from China

Aim:Intracoronary murmur results from turbulent flow due to coronary artery narrowing.This study evaluated the diagnostic performance of a method for acoustic analysis of turbulent murmur caused by coronary artery stenosis in coronary artery disease(CAD)in Chinese populations.Method:Patients admitted to the cardiovascular department of the Sixth Medical Center of the Chinese People’s Liberation Army General Hospital between September 2021 and June 2022 for elective coronary angiography were prospectively enrolled.A digital electronic stethoscope was used to record heart sounds before angiography.Quantitative coronary angiography(QCA)served as the“gold standard”for CAD diagnosis to evaluate the diagnostic performance of the acoustic analysis method for CAD.Results:A total of 452 patients had complete QCA and heart sound data.The final interpretation results of the acoustic analysis method indicated 310 disease cases and 142 normal results.Increasing the cut-off values of coronary artery diameter stenosis from 30%to 50%,70%,and 90%increased the sensitivity and NPV of the acoustic analysis method;the sensitivity was 75.6%,81.9%,83.3%,and 85.7%,respectively;the NPV was 33.1%,57.0%,69.7%,and 88.0%,respectively;the specificity and PPV decreased(specificity of 75.8%,70.4%,51.0%,and 37.5%,respectively;PPV of 95.2%,89.0%,69.4%,and 32.9%,respectively);and the AUC values were 0.757,0.762,0.672,and 0.616,respectively.The sensitivity of the acoustic analysis method for one-vessel disease was 86.6%when the cut-off value was 50%.The sensitivity for identifying left anterior descending coronary artery lesions was best,at 90.7%.The sensitivity for identifying isolated coronary artery branch lesions was 66.7%,whereas the sensitivity for identifying three-vessel disease in multi-vessel coronary artery lesions was better,at 82.9%.Conclusion:Acoustic analysis of turbulent murmur caused by coronary artery stenosis for diagnosis of CAD may have favorable performance in the Chinese population.This method has good performance in CAD diagnosis with a cut-off coronary artery diameter for stenosis of 50%.

Measurement of two new indicators of cardiac reserve in humans, rats, rabbits, and dogs

Background: It is difficult to observe the phenomena of cardiac fatigue under a low or moderate workload test, and little was reported about cardiac fatigue under a heavy workload test because of the potential risk (sudden death) of prolonged strenuous exercise. Animal experiments would be helpful to resolve this problem. The objective of this study was to obtain baseline data of two new indicators of cardiac reserve in rats, rabbits, and dogs. Methods: Ten New Zealand White rabbits (1. 5 to 2 kg, 4 females), 10 male Wistar rats (2 months old), and 10 Golden Retriever dogs (40 days old, 5 females) were included in this study. Phonocardiogram of each animal was recorded. The basic points concerning heart sound quantitative analysis were: 1) measuring the duration and the amplitude of relevant heart sound components;2) calculating and analyzing relevant indicators based on the data obtained from the above measurements, including the ratio of diastolic to systolic duration (D/S) and the ratio of the amplitude of the first heart sound to the amplitude of the second heart sound (S1/S2). Results: The baseline data of D/S ratio and S1/S2 ratio in rats, rabbits, and dogs were obtained. The swimming time to exhaustion for rabbits was several to a dozen minutes, and for rats, several hours. Conclusion: D/S ratio has an important biological implication, which is a safe and easy indicator for evaluating the cardiac health status of both animals and humans.

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。