Estimating oxygen consumption from heart rate using adaptive neuro-fuzzy inference system and analytical approaches

In new approaches based on adaptive neuro-fuzzy systems (ANFIS) and analytical method, heart rate (HR) measurements were used to estimate oxygen consumption (VO₂). Thirty-five participants performed Meyer and Flenghi's step-test (eight of which performed regeneration release work), during which heart rate and oxygen consumption were measured. Two individualized models and a General ANFIS model that does not require individual calibration were developed. Results indicated the superior precision achieved with individualized ANFIS modelling (RMSE = 1.0 and 2.8 ml/kg min in laboratory and field, respectively). The analytical model outperformed the traditional linear calibration and Flex-HR methods with field data. The General ANFIS model's estimates of VO₂ were not significantly different from actual field VO₂ measurements (RMSE = 3.5 ml/kg min). With its ease of use and low implementation cost, the General ANFIS model shows potential to replace any of the traditional individualized methods for VO₂ estimation from HR data collected in the field.     

gHRV: Heart rate variability analysis made easy

In this paper, the gHRV software tool is presented. It is a simple, free and portable tool developed in python for analysing heart rate variability. It includes a graphical user interface and it can import files in multiple formats, analyse time intervals in the signal, test statistical significance and export the results.     

Classification of cardiac sound signals using constrained tunable-Q wavelet transform

The features extracted from the cardiac sound signals are commonly used for detection and identification of heart valve disorders. In this paper, we present a new method for classification of cardiac sound signals using constrained tunable-Q wavelet transform (TQWT). The proposed method begins with a constrained TQWT based segmentation of cardiac sound signals into heart beat cycles. The features obtained from heart beat cycles of separately reconstructed heart sounds and murmur can better represent the various types of cardiac sound signals than that from containing both. Therefore, heart sounds and murmur have been separated using constrained TQWT. Then the proposed novel raw feature set has been created by the parameters that have been optimized while constraining the output of TQWT together with that of extracted by using time-domain representation and Fourier–Bessel (FB) expansion of separated heart sounds and murmur. However, the adaptively selected features have been used to obtain the final feature set for subsequent classification of cardiac sound signals using least squares support vector machine (LS-SVM) with various kernel functions. The performance of the proposed method has been validated with publicly available datasets and the results have been compared with the existing short-time Fourier transform (STFT) based method. The proposed method shows higher percentage classification accuracy of 94.01 as compared to 93.53 of STFT based method. In comparison with STFT based method, it is noteworthy that the proposed method uses well defined and lower dimensionality of feature vector that can reduce the computational complexity.     

基于SIFT算法的三维心脏表面模型重建

心脏三维模型对于心脏疾病的诊断和治疗有很重要的作用,为满足心肌缺血可视化的显示需求,提出了一种基于SIFT图像匹配算法重建三维心脏表面模型的新方法。该方法用一部标定好的数码相机围绕心脏模型拍摄一组照片,用SIFT算法完成图像间同名点的匹配,通过这些同名点求出相机的外参数,进而实现三维心脏模型的重建。实验结果表明,该方法成功地重建了三维心脏表面模型,并且成本低、效率高,重建的模型能满足心肌缺血可视化的显示需要。     

狗心脏中I_(to)相关钾离子通道表达分析

目的探讨钾离子通道Kv4·3和Kv1·4在正常与心衰的心肌组织中的表达差别,为进一步阐明心衰的发病机理奠定基础。方法建立狗的心衰模型,用Western blot方法对正常与心衰的狗心肌组织中Kv4·3、Kv1·4及辅助亚基Mink的表达进行分析。结果在心衰时,Kv4·3表达有明显下降;Kv1·4表达有所上升,在心肌组织的不同部位,即外层和内层Kv1·4的表达明显不同;同时Mink的表达在心肌外层也有明显下降。结论心衰时,心肌组织中形成Ito的钾离子通道Kv4·3和Kv1·4以及辅助亚基Mink的蛋白表达变化之间可能存在着一些相关性。     

基于AB-CNN-BiLSTM心衰死亡率预测模型

为了更有效地分配医疗资源并辅助医生诊疗,需要对ICU患者的死亡率做出更快、更准确的预测,提出一种融合注意力机制的CNN-BiLSTM ICU患者心衰死亡率预测模型。对MIMICIII数据集提供的入院48小时的数据进行一系列预处理操作;通过卷积神经网络模型(CNN)及双向长短期记忆神经网络模型(BiLSTM)考虑不同参数之间的空间联系,同时关注数据时间维度上的变化;引入注意力机制赋予特征权重。实验表明,该预测模型能够有效地对ICU患者心衰死亡率进行预测,提高死亡率预测的准确性。     

基于深度卷积神经网络的心音分类算法

针对现有心音分类算法普适性差、依赖于对基本心音的精确分割、分类模型结构单一等问题,提出采用大量未经过精确分割的心音二维特征图训练深度卷积神经网络(CNN)的方法;首先采用滑动窗口方法和梅尔频率系数对心音信号进行预处理,得到大量未经过精确分割的心音特征图;然后利用深度CNN模型对心音特征图进行训练和测试;根据卷积层间连接方式的不同,设计了 3种深度CNN模型:基于单一连接的卷积神经网络、基于跳跃连接的卷积神经网络、基于密集连接的卷积神经网络;实验结果表明,基于密集连接的卷积神经网络比其他两种网络具备更大的潜力;与其他心音分类算法相比,该算法不依赖于对基本心音的精确分割,且在分类准确率、敏感性和特异性方面均有提升.     

Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images

Segmenting the heart in medical images is a challenging and important task for many applications. In particular, segmenting the heart in CT images is very useful for cardiology and oncological applications such as radiotherapy. Although the majority of methods in the literature are designed for ventricle segmentation, there is a real interest in segmenting the heart as a whole in this modality. In this paper, we address this problem and propose an automatic and robust method, based on anatomical knowledge about the heart, in particular its position with respect to the lungs. This knowledge is represented in a fuzzy formalism and it is used both to define a region of interest and to drive the evolution of a deformable model in order to segment the heart inside this region. The proposed method has been applied on non-contrast CT images and the obtained results have been compared to manual segmentations of the heart, showing the good accuracy and high robustness of our approach.     

A NEW BATO COMPLEX^99mTcCl（4—MCDO）3MeB FOR MYOCARDIAL IMAGING

A new nertral complex,^99mTcCl(4-MCDO)3MEB,((Bis[4-methyl-1,2-cyclohex-anedionedioximato(1-)-O]-[4-methyl-1,2-cyclohexanedione-dioximato(2-)-O] methyl-borato(2-)-N,N‘,N″,N‘‘‘,N‘‘‘‘0-chlorotechnetium,generally called BATO(Boronic acid adducts of technitium dioximes),has been synthesized and evaluated for potential use in myocardial perfusion imaging,It has intrinsic affinity for the blood clearance.The uptake of heart.lung and blood in mice at 2min separately are 1.12,2.48 and 6.66%ID.The complex formation is rapid,simple and highly yielded(≥93%).This process is easy to kit formation.     

Multi‐response optimization and empirical modeling of cardiopulmonary responses during manual lifting tasks

An experimental study involving five independent lifting variables (namely weight, lift frequency, horizontal distance from the object, operator, and vertical distance) and their interactions was conducted using the Taguchi L₂₇ design. Two responses—heart rate and oxygen uptake—were measured during each trial. To optimize both responses simultaneously, the signal‐to‐noise ratio was normalized to obtain a multi‐response signal‐to‐noise (MRSN) ratio for each trial. The MRSN ratio was analyzed using analysis of variance to identify the optimal condition in which both responses were optimized at the same time. Lift frequency was found to be the most significant factor, and operator and horizontal distance were found to be least significant. The empirical modeling equation was developed and can be used to predict the response values during experimental trials involving similar work conditions. © 2010 Wiley Periodicals, Inc.