Evaluation of a Novel Integrated Sensor System for Synchronous Measurement of Cardiac Vibrations and Cardiac Potentials

The measurement of human body vibrations as a result of heart beating, simultaneously with cardiac potentials have been demonstrated in past studies to bring additional value to diagnostic cardiology through the detection of irregularities in the mechanical movement of the heart. The equipment currently available to the medical community is either large and bulky or difficult to synchronize. To address this problem, a novel integrated sensor system has been developed to record cardiac vibration and cardiac potential simultaneously and synchronously from a single compact site on the chest. The developed sensor system is lightweight, small in size, and suitable for mounting on active moving patients. The sensor is evaluated for its adequacy in measuring cardiac vibrations and potentials. In this evaluation, 45 independent signal recording are studied from 15 volunteers, and the morphology of the recorded signals are analyzed qualitatively (by visual inspection) and quantitatively (by computational methods) against larger devices used in established cardiac vibration studies (reference devices). It is found that the cardiac vibration signals acquired by the integrated sensor has 92.37% and 81.76% identically identifiable systolic and diastolic cardiac complexes, respectively, when compared to the cardiac vibration signals recorded simultaneously from the reference device. Further, the cardiac potential signals acquired by the integrated sensor show a high correlation coefficient of 0.8912 and a high estimated signal-to-noise-ratio of 22.00 dB when compared to the reference electrocardiograph (non-standard leads) acquired through a common clinical machine. The results suggest that the tiny, wearable, integrated sensor system that synchronously measures cardiac vibrations and cardiac potentials may be practical for use as an alternative or assistive cardiac diagnostic tool.     

Fetal Electrocardiogram Extraction and Analysis Using Adaptive Noise Cancellation and Wavelet Transformation Techniques

Birth defect-related demise is mainly due to congenital heart defects. In the earlier stage of pregnancy, fetus problem can be identified by finding information about the fetus to avoid stillbirths. The gold standard used to monitor the health status of the fetus is by Cardiotachography(CTG), cannot be used for long durations and continuous monitoring. There is a need for continuous and long duration monitoring of fetal ECG signals to study the progressive health status of the fetus using portable devices. The non-invasive method of electrocardiogram recording is one of the best method used to diagnose fetal cardiac problem rather than the invasive methods.The monitoring of the fECG requires development of a miniaturized hardware and a efficient signal processing algorithms to extract the fECG embedded in the mother ECG. The paper discusses a prototype hardware developed to monitor and record the raw mother ECG signal containing the fECG and a signal processing algorithm to extract the fetal Electro Cardiogram signal. We have proposed two methods of signal processing, first is based on the Least Mean Square (LMS) Adaptive Noise Cancellation technique and the other method is based on the Wavelet Transformation technique. A prototype hardware was designed and developed to acquire the raw ECG signal containing the mother and fetal ECG and the signal processing techniques were used to eliminate the noises and extract the fetal ECG and the fetal Heart Rate Variability was studied. Both the methods were evaluated with the signal acquired from a fetal ECG simulator, from the Physionet database and that acquired from the subject. Both the methods are evaluated by finding heart rate and its variability, amplitude spectrum and mean value of extracted fetal ECG. Also the accuracy, sensitivity and positive predictive value are also determined for fetal QRS detection technique. In this paper adaptive filtering technique uses Sign-sign LMS algorithm and wavelet techniques with Daubechies wavelet, employed along with de noising techniques for the extraction of fetal Electrocardiogram.Both the methods are having good sensitivity and accuracy. In adaptive method the sensitivity is 96.83, accuracy 89.87, wavelet sensitivity is 95.97 and accuracy is 88.5. Additionally, time domain parameters from the plot of heart rate variability of mother and fetus are analyzed.     

体外循环下心内直视手术中的心脏复跳

本文报道180例体外循环心内直视手术中的心脏复跳情况:先天性心脏病145例,后天性心脏病35例。心功能Ⅲ级或Ⅲ～Ⅳ级者占1/3。体外循环时间最短30分,最长192分,其中>100分26例。主动脉阻断时间最短20分,最长139分。其中>30分者35例。心脏复跳成功率99.45％。自动复跳117例(65％),电击除颤复跳63例(35％)。     

Cardiac contractility modulation device-new hope for refractory heart failure patients

A large number of patients with advanced heart failure (HF) are refractory to optimal standard medical therapy. This has given rise to development and testing of a host of new device based therapies. One recent and potentially broadly applicable treatment under investigation is cardiac contractility modulation (CCM) electrical signals. The CCM signals are relatively high-voltage electrical impulses applied to the myocardium during the absolute refractory period. These signals do not initiate a new contraction or modify activation sequence as is the case with other therapies such as cardiac resynchronization therapy. Rather, CCM signals are intended to enhance systolic function of the failing myocardium. A device similar to a pacemaker is typically implanted in the right pectoral region. Three standard pacemaker leads are placed intravenously. One right arterial lead is used to sense atrial activity, and two right ventricular leads are used to sense ventricular activity and deliver the CCM pulse trains. The electrodes of the ventricular leads are placed on the right ventricular septum at least 2cm apart. In contrast to a Pacemaker and defibrillator the system is designed to modulate the strength of contraction of the heart muscle rather than the rhythm. Application of CCM signals to the failing heart is associated with improved gene expression which ultimately causes LV global, cellular and biochemical remodeling as a result improved LV systolic function.     

Reliability of the Parabola Approximation Method in Heart Rate Variability Analysis Using Low-Sampling-Rate Photoplethysmography

Photoplethysmographic signals are useful for heart rate variability analysis in practical ambulatory applications. While reducing the sampling rate of signals is an important consideration for modern wearable devices that enable 24/7 continuous monitoring, there have not been many studies that have investigated how to compensate the low timing resolution of low-sampling-rate signals for accurate heart rate variability analysis. In this study, we utilized the parabola approximation method and measured it against the conventional cubic spline interpolation method for the time, frequency, and nonlinear domain variables of heart rate variability. For each parameter, the intra-class correlation, standard error of measurement, Bland-Altman 95% limits of agreement and root mean squared relative error were presented. Also, elapsed time taken to compute each interpolation algorithm was investigated. The results indicated that parabola approximation is a simple, fast, and accurate algorithm-based method for compensating the low timing resolution of pulse beat intervals. In addition, the method showed comparable performance with the conventional cubic spline interpolation method. Even though the absolute value of the heart rate variability variables calculated using a signal sampled at 20 Hz were not exactly matched with those calculated using a reference signal sampled at 250 Hz, the parabola approximation method remains a good interpolation method for assessing trends in HRV measurements for low-power wearable applications.     

Automated Diagnosis of Heart Sounds Using Rule-Based Classification Tree

In order to assist the diagnosis procedure of heart sound signals, this paper presents a new automated method for classifying the heart status using a rule-based classification tree into normal and three abnormal cases; namely the aortic valve stenosis, aortic insufficient, and ventricular septum defect. The developed method includes three main steps as follows. First, one cycle of the heart sound signals is automatically detected and segmented based on time properties of the heart signals. Second, the segmented cycle is preprocessed with the discrete wavelet transform and then largest Lyapunov exponents are calculated to generate the dynamical features of heart sound time series. Finally, a rule-based classification tree is fed by these Lyapunov exponents to give the final decision of the heart health status. The developed method has been tested successfully on twenty-two datasets of normal heart sounds and murmurs with success rate of 95.5%. The resulting error can be easily corrected by modifying the classification rules; consequently, the accuracy of automated heart sounds diagnosis is further improved.     

A WBAN-based Real-time Electroencephalogram Monitoring System: Design and Implementation

In this study, a flexible wireless body area network (WBAN) node platform has been designed and implemented based on the Zigbee technology. In order to provide wide range WBAN for health monitoring, a Zigbee/Internet Gateway (ZiGW) has also been developed rather than using a PDA or a host PC to connect different WBANs by using the Internet as the communication infrastructure. The proposed body sensor node platform promises a cost-effective, flexible platform for developing physical sensor node in real-time health monitoring. The ZiGW can provide an effective method to connect WBAN with the Internet. In this work, we present the implementation of an Electroencephalogram (EEG) monitoring system using the proposed methods. In this proposed system, real-time EEG signals can be remotely monitored by physicians via Internet, and the collected EEG data is stored in the online EEG database which can be shared with physicians or researchers for further analysis.     

A Biomedical System Based on Artificial Neural Network and Principal Component Analysis for Diagnosis of the Heart Valve Diseases

Listening via stethoscope is a primary method, being used by physicians for distinguishing normally and abnormal cardiac systems. Listening to the voices, coming from the cardiac valves via stethoscope, upon the flow of the blood running in the heart, physicians examine whether there is any abnormality with regard to the heart. However, listening via stethoscope has got a number of limitations, for interpreting different heart sounds depends on hearing ability, experience, and respective skill of the physician. Such limitations may be reduced by developing biomedical based decision support systems. In this study, a biomedical-based decision support system was developed for the classification of heart sound signals, obtained from 120 subjects with normal, pulmonary and mitral stenosis heart valve diseases via stethoscope. Developed system was mainly comprised of three stages, namely as being feature extraction, dimension reduction, and classification. At feature extraction stage, applying Discrete Fourier Transform (DFT) and Burg autoregressive (AR) spectrum analysis method, features, representing heart sounds in frequency domain, were obtained. Obtained features were reduced in lower dimensions via Principal Component Analysis (PCA), being used as a dimension reduction technique. Heart sounds were classified by having the features applied as input to Artificial Neural Network (ANN). Classification results have shown that, dimension reduction, being conducted via PCA, has got positive effects on the classification of the heart sounds.     

人体动脉脉搏波传输检测系统的设计与应用

作者根据人体动脉脉搏波传输的生物力学原理,设计和研制了包括心功能常用指标在内的一台雏型脉搏波传输检测系统.对设计过程中出现的基本问题,如传感器的定位、消除人为误差及多道传感器频响的一致性等进行探讨,并介绍了传感器的加压系统、静态和动态特性的补偿及多路微弱信号放大和数据存储.通过对正常和某些患心血管疾病的人体测试,表明该仪器设计合理,提供具有临床意义的参数较多.该系统具有一定的开拓性,而且它对脉搏波时域和频域分析得到的一些参数,有助于某些心脏病发生和发展的诊断和预测.     

超声技术在临床麻醉中的应用进展

随着便携式超声设备和高分辨率探头的使用,以及超声技术的改进,超声技术在临床麻醉中的应用日益广泛。经食道超声心动图在心脏手术中的应用在心血管麻醉史中具有重要的意义。通过食道超声,麻醉医生可以实时了解患者心脏的前后负荷以及心室的充盈状态,从而可以对心脏功能做出快速而有效的评估。此外,经食道超声可以评估心脏手术效果,甚至在一定程度上影响手术决策。超声引导下进行动静脉穿刺置管和外周神经阻滞,不仅成功率高,而且并发症少。     

Performance Evaluation of Time-Frequency Distributions for ECG Signal Analysis

The non-stationary and multi-frequency nature of biomedical signal activities makes the use of time-frequency distributions (TFDs) for analysis inevitable. Time-frequency analysis provides simultaneous interpretations in both time and frequency domain enabling comprehensive explanation, presentation and interpretation of electrocardiogram (ECG) signals. The diversity of TFDs and specific properties for each type show the need to determine the best TFD for ECG analysis. In this study, a performance evaluation of five TFDs in term of ECG abnormality detection is presented. The detection criteria based on extracted features from most important ECG signal components (QRS) to detect normal and abnormal cases. This is achieved by estimating its energy concentration magnitude using the TFDs. The TFDs analyse ECG signals in one-minute interval instead of conventional time domain approach that analyses based on beat or frame containing several beats. The MIT-BIH normal sinus rhythm ECG database total records of 18 long-term ECG sampled at 128 Hz have been analysed. The tested TFDs include Dual-Tree Wavelet Transform, Spectrogram, Pseudo Wigner-Ville, Choi-Williams, and Born-Jordan. Each record is divided into one-minute slots, which is not considered previously, and analysed. The sample periods (slots) are randomly selected ten minutes interval for each record. This result with 99.44% detection accuracy for 15,735 ECG beats shows that Choi-Williams distribution is most reliable to be used for heart problem detection especially in automated systems that provide continuous monitoring for long time duration.     

先天性心脏病心音听诊筛查的人工智能技术应用现状

近年来,电子听诊器结合人工智能技术实现了心音的数字化采集和先天性心脏病的智能识别,为心音听诊提供了客观依据,提高了先天性心脏病诊断的准确率。现阶段基于人工智能技术的智能听诊技术主要侧重于人工智能算法的研究,国内外学者也针对心音音频数据的特点设计总结了多种有效算法,其中梅尔频率倒谱系数（MFCC）是最常用且有效的心音特征,被广泛应用于智能听诊技术中。然而,当前心音智能听诊技术均基于筛选的特定数据集实现,并且尚未在实际临床环境中基于大样本进行实验验证,因此各个算法的实际临床应用表现尚待进一步验证。心音数据匮乏,特别是高质量、标准化、带疾病标注且公开的心音数据库的缺失,进一步制约了心音智能诊断分析技术的发展和听诊筛查的应用。因此,相关医疗单位应当组织有关专家共同建立先天性心脏病心音听诊筛查的专家共识和标准化心音听诊筛查流程,并以此建立权威心音数据库。本文就现阶段基于人工智能的听诊算法和硬件设备在先天性心脏病听诊筛查中的研究及应用进行综述,提出人工智能心音听诊筛查技术在临床应用中有待解决的问题。     

基于径向基函数神经网络的心脏早搏分类诊断研究

早搏是常见的心律失常,根据发生部位分为窦性早搏、房性早搏、交界性早搏和室性早搏。心肌细胞特有的电生理特性和心律失常中常有的一些心电现象,使一部分早搏的心电图失去其固有的特征,临床上通过心电图对其分类诊断存在一定的不确定性。基于Matlab平台,采用径向基神经网络方法,对所搜集的82个早搏分类确诊病例进行研究,建立了心脏早搏分类的辅助诊断模型,其准确率达到96％。为临床心脏早搏分类诊断提供了一种新的方法。     

复苏困难的重症瓣膜病人心肌保护方法

目的 总结9例复苏困难的重症瓣膜病患者成功复苏经验，尝试心肌保护新方法。方法对经多次除颤不能复苏的重症瓣膜病患者，采用再次阻断升主动脉，行4：1半钾温冷血停搏液诱停加纯温血持续灌注，待心脏复苏后再开放升主动脉的方法。结果9例复苏困难的重症瓣膜病患者均自动复苏。结论再次阻断升主动脉，行4：1半钾温冷血停搏液诱停加纯温血(不含停搏液)持续灌注，待心脏复苏后再开放升主动脉的方法为复苏困难的心脏直视手术患者成功复苏提供了新方法。     

新生大鼠心肌细胞体外培养及其与心肌成纤维细胞形态比较

目的探索一种快速分离和培养新生大鼠心肌细胞的方法,比较心肌细胞和成纤维细胞的形态差异。方法胰蛋白酶消化法分离新生大鼠心肌细胞进行体外培养,差速贴壁法纯化心肌细胞,倒置显微镜跟踪观察心肌细胞的生长及搏动情况,台盼兰负染法检测心肌细胞活力,通过HE染色观察心肌细胞和成纤维细胞的形态学差异。结果接种24 h后细胞全部贴壁,部分细胞开始自发性搏动,48 h后,细胞体积增大,并伸出伪足,形状以三角形和梭形为主,3 d后细胞聚集成片并同步搏动且细胞活力均在90%以上。心肌细胞为梭形或三角形,胞质致密,多为单核细胞;心肌成纤维细胞呈泡状,双核和三核细胞较多,培养过程中不发生搏动。结论胰蛋白酶消化法培养新生大鼠心肌细胞快速有效,差速贴壁法纯化的心肌细胞可用于实验研究。     

冠心病患者的心率变异性分析

目的：心率变异性（ＨＲＶ）分析是反映支配心脏的自主神经活动的无创性指标。本研究目的是研究冠心病人ＨＲＶ的改变。方法：通过ＨＲＶ时域分析方法,研究了２５例正常人,４０例冠心病病人的心率变异特征。结果：冠心病患者ＨＲＶ的窦性心博间标准差（ＳＤＮＮ）和ＨＲＶ三角指数（ＨＲＶ指数）明显低于正常人,两组之ＨＲＶ均有明显的昼夜节律,结论：冠心病人的ＨＲＶ降低,提示支配心脏的自主神经功能不平衡。     

胺碘酮联合美托洛尔治疗冠心病合并心律失常临床研究

目的：观察胺碘酮联合美托洛尔治疗冠心病合并心律失常的临床效果。方法：将2015年6月～2016年4月在我院就诊的冠心病合并心律失常患者121例,随机分为对照组60例和治疗组61例,对照组患者口服胺碘酮片200mg/次,3次/d;联合治疗组给予胺碘酮片和美托洛尔片,起始剂量为口服12.5mg/次,2次/d,随后对两组临床资料进行回顾性分析。结果：治疗组总有效率为90.1%,高于对照组的70.0%,差异有统计学意义;治疗后两组患者心率[（71.7±8.7）、（63.6±7.3）次/min]和QTd[（49.7±15.3）、（42.5±15.6）ms]均明显减少,与治疗前相比差异有统计学意义,且治疗组水平低于对照组,差异有统计学意义;治疗组患者的室性早搏次数[（1832.3±532.6）次]、房性早搏[（359.3±177.6）次]和交界性早搏次数[（433.1±163.2）次]与对照组比较,差异均有统计学意义。结论：美托洛尔联合胺碘酮治疗冠心病并发心律失常效果优于单药治疗,建议临床推广。     

A Novel Handheld Device for Use in Remote Patient Monitoring of Heart Failure Patients—Design and Preliminary Validation on Healthy Subjects

Remote patient monitoring (RPM) holds great promise for reducing the burden of congestive heart failure (CHF). Improved sensor technology and effective predictive algorithms can anticipate sudden decompensation events. Enhanced telemonitoring systems would promote patient independence and facilitate communication between patients and their physicians. We report the development of a novel hand-held device, called Blue Box, capable of collecting and wirelessly transmitting key cardiac parameters derived from three integrated biosensors: 2 lead electrocardiogram (ECG), photoplethysmography and bioelectrical impedance (bioimpedance). Blue Box measurements include time intervals between consecutive ECG R-waves (RR interval), time duration of the ECG complex formed by the Q, R and S waves (QRS duration), bioimpedance, heart rate and systolic time intervals. In this study, we recruited 24 healthy subjects to collect several parameters measured by Blue Box and assess their value in correlating with cardiac output measured with Echo-Doppler. Linear correlation between the heart rate measured with Blue Box and cardiac output from Echo-Doppler had a group average correlation coefficient of 0.80. We found that systolic time intervals did not improve the model significantly. However, STIs did inversely correlate with increasing workloads.     

悬尾实验实时检测分析处理系统的研制及初步应用

目的建立悬尾实验实时监测分析处理系统方法结合计算机、机械工程和实验动物行为学,研制悬尾实验实时监测分析处理系统。应用动物实验进行运动阈值确认和数据准确性验证,并用抗抑郁阳性药丙咪嗪和帕罗西汀进行不动时间和"功"指标的验证。结果悬尾测试箱由8个悬尾箱组成。采用四周封闭、顶面开放的形式。通过旋转固定手柄,调节横梁高度,实现多样本同时悬挂。8个传感器固定于横梁两侧,分别对应8个悬尾箱。传感器采集的信号经过增益调整、滤波处理送至采集传输盒。采集传输盒采集输出信号,数字化后送往PC机。动物的运动时间、不动时间及功值可实时显示。运动阈值为26.8时,人机相关系数最大,为0.94。应用本系统,与对照组比较,帕罗西汀和丙咪嗪可显著缩短小鼠悬尾不动时间(P0.01),帕罗西汀还可显著增加小鼠"功"值(P0.01),即增强小鼠悬尾的挣扎强度。结论悬尾实验实时监测分析处理系统实现了悬尾实验的自动化,客观性强,重复性好,可推广应用于抗抑郁药物的筛选和抑郁症病理机制的研究。     

Classification of EEG Recordings by Using Fast Independent Component Analysis and Artificial Neural Network

Since there is no definite decisive factor evaluated by the experts, visual analysis of EEG signals in time domain may be inadequate. Routine clinical diagnosis requests to analysis of EEG signals. Therefore, a number of automation and computer techniques have been used for this aim. In this study we aim at designing a MLPNN classifier based on the Fast ICA that accurately identifies whether the associated subject is normal or epileptic. By analyzing a data set consisting of 100 normal and 100 epileptic EEG time series, we have found that the MLPNN classifier based on the Fast ICA achieved and sensitivity rate of 98%, and specificity rate of 90.5%. The results demonstrate that the testing performance of the neural network diagnostic system is found to be satisfactory and we think that this system can be used in clinical studies. Since the time series analysis of EEG signals is unsatisfactory and requires specialist clinicians to evaluate, this application brings objectivity to the evaluation of EEG signals.