Long-term heart rate variability as a predictor of patient age

Patients age has been estimated in healthy population by means of the heart rate variability (HRV) parameters to assess the potentiality of HRV indexes as a biomarker of age. A long-term analysis of HRV has been performed, computing linear time and frequency domain parameters as well as non-linear metrics, in a dataset of 113 healthy subjects (age range 20-85 years old). The principal component analysis has been used to capture age-related influence on HRV and then three different models have been applied to predict subjects age: a robust linear regressor (RLR), a feedforward neural network (FFNN) and a radial basis function neural network (RBFNN). A good prediction of patient age has been obtained (using all principal components, the Pearson correlation coefficient between predicted and real age: RLR=0.793; FFNN=0.872; RBFNN=0.829), even if an overestimation in younger subjects and an underestimation in older ones may be observed. The important and complementary contribution of non-linear indexes to aging related HRV modifications has also been underlined.     

A novel approach for phonocardiographic signals processing to make possible fetal heart rate evaluations

In this paper, a non-invasive, portable and inexpensive antenatal care system is developed using fetal phonocardiography. The fPCG technique has the potential to provide low-cost and long-term diagnostics to the under-served population. The fPCG signal contains valuable diagnostic information regarding fetal health during antenatal period. The fPCG signals are acquired from the maternal abdominal surface using a wireless data acquisition and recording system. The diagnostic parameters e.g., baseline, variability, acceleration and deceleration of the fetal heart rate are derived from the fPCG signal. A model based on adaptive neuro-fuzzy inference system is developed for the evaluation of fetal health status. To study the performance of the developed system, experiments were carried out with real fPCG signals under the supervision of medical experts. Its performance is found to be in close proximity with the widely accepted Doppler ultrasound based fetal monitor results. The overall performance shows that the developed system has a long-term monitoring capability with very high performance to cost ratio. The system can be used as first screening tool by the medical practitioners.     

Linear and nonlinear analysis of normal and CAD-affected heart rate signals

Coronary artery disease (CAD) is one of the dangerous cardiac disease, often may lead to sudden cardiac death. It is difficult to diagnose CAD by manual inspection of electrocardiogram (ECG) signals. To automate this detection task, in this study, we extracted the heart rate (HR) from the ECG signals and used them as base signal for further analysis. We then analyzed the HR signals of both normal and CAD subjects using (i) time domain, (ii) frequency domain and (iii) nonlinear techniques. The following are the nonlinear methods that were used in this work: Poincare plots, Recurrence Quantification Analysis (RQA) parameters, Shannon entropy, Approximate Entropy (ApEn), Sample Entropy (SampEn), Higher Order Spectra (HOS) methods, Detrended Fluctuation Analysis (DFA), Empirical Mode Decomposition (EMD), Cumulants, and Correlation Dimension. As a result of the analysis, we present unique recurrence, Poincare and HOS plots for normal and CAD subjects. We have also observed significant variations in the range of these features with respect to normal and CAD classes, and have presented the same in this paper. We found that the RQA parameters were higher for CAD subjects indicating more rhythm. Since the activity of CAD subjects is less, similar signal patterns repeat more frequently compared to the normal subjects. The entropy based parameters, ApEn and SampEn, are lower for CAD subjects indicating lower entropy (less activity due to impairment) for CAD. Almost all HOS parameters showed higher values for the CAD group, indicating the presence of higher frequency content in the CAD signals. Thus, our study provides a deep insight into how such nonlinear features could be exploited to effectively and reliably detect the presence of CAD.     

Spectral and cross-spectral analysis of heart rate and arterial blood pressure variability signals

A parametric method for autoregressive (AR) auto- and cross-spectral analysis is presented for the contemporaneous processing of heart rate and arterial blood pressure variability signals. In particular, the introduced bivariate spectral analysis (phase and coherence spectra) provides quantitative and objective means which are useful to measure the role played by the neural controlling systems (sympathetic and parasympathetic systems) on the cardiovascular signals under different pathophysiological conditions. Algorithmic aspects, connected to the way of processing discrete numerical series synchronized to single cardiac beats, are particularly stressed. Important applications are foreseen both in physiological studies and in clinical practice as an aid to the detection of various relevant cardiovascular pathologies such as hypertension and diabetes.     

Spectral analysis of heart rate variability as an indicator of driver fatigue

The effect of distance driven on three physiological variables taken to be indicators of fatigue was investigated on a 340 km highway circuit with eight inexperienced drivers as subjects. The physiological variables used were spectral values of heart rate variability in the 0.05-0.15Hz region (0.1 Hz HRV) supplemented by standard deviation of heart rate mean (S.D. HRV) and heart rate mean (HR). The analysis showed a significant relationship between 0.1 Hz HRV and distance driven while S.D. HRV and HR showed no direct relationship. The reason for this differential effect on the three physiological variables was discussed, and it was concluded that 0.1 Hz HRV seems to be a sensitive indicator of driver fatigue.     

数据挖掘在胎儿心率上的规则预测研究

瞬时胎心率是监测胎儿健康状态的一种重要方式。当前,监控胎儿心率是重要而复杂的任务,正确的自动化分类和规则提取是非常必要的。医疗诊断自动化系统,不仅加强医疗保健,同时也可以降低成本。设计了一个有效挖掘规则,并根据给定的参数来预测胎儿的风险水平。采用C4.5、Classification and Regression Tree(CART)、随机森林分类器来进行系统比较。该系统的性能评价由分类精度、产生规则数量构成。实验结果表明,基于随机森林分类器的系统具有高精度(99.4%)的预测胎儿健康状态的潜力,同时,产生的规则数量精简且可供于医生决策。     

基于超声传感器的胎心率检测优化算法

获得胎心率信号的方法有多种,而以超声传感器检测法应用最广.然而如何在嵌入式实时系统中快速、准确地从含有噪声干扰的超声多普勒回波信号中提取胎心率信号成为一个难点.针对该难点,提出了一种基于经验模态分解(EMD)与提升小波变换相结合的去噪方法,再将去噪后的胎心信号采用希尔伯特变换提取信号包络,最后通过自相关运算得出胎心率.实验结果表明:采用这种方法能够有效提取到胎心率信号,使得胎心率计算耗时大大减少,同时,计算的准确性得到提高.     

Feature selection using swarm-based relative reduct technique for fetal heart rate

Fetal heart rate helps in diagnosing the well-being and also the distress of fetal. Cardiotocograph (CTG) monitors the fetal heart activity to estimate the fetal tachogram based on the evaluation of ultrasound pulses reflected from the fetal heart. It consists in a simultaneous recording and analysis of fetal heart rate signal, uterine contraction activity and fetal movements. Generally CTG comprises more number of features. Feature selection also called as attribute selection is a process of selecting a subset of highly relevant features which is responsible for future analysis. In general, medical datasets require more number of features to predict an activity. This paper aims at identifying the relevant and ignores the redundant features, consequently reducing the number of features to assess the fetal heart rate. The features are selected by using unsupervised particle swarm optimization (PSO)-based relative reduct (US-PSO-RR) and compared with unsupervised relative reduct and principal component analysis. The proposed method is then tested by applying various classification algorithms such as single decision tree, multilayer perceptron neural network, probabilistic neural network and random forest for maximum number of classes and clustering accuracies like root mean square error, mean absolute error, Davies–Bouldin index and Xie–Beni index for minimum number of classes. Empirical results show that the US-PSO-RR feature selection technique outperforms the existing methods by producing sensitivity of 72.72 %, specificity of 97.66 %, F-measure of 74.19 % which is remarkable, and clustering results demonstrate error rate produced by US-PSO-RR is less as well.     

Estimation of heart rate signals for mental stress assessment using neuro fuzzy technique

This paper presents the evaluation of mental stress assesment using heart-rate variability (HRV). The activity of the autonomic nervous system (ANS) is studied by means of time-frequency analysis (TFA) of the heart-rate variability signal. Spectral decomposition of the heart-rate variability before smoking and after smoking was obtained. Mental stress is accompanied by dynamic changes in ANS activity. HRV analysis is a popular tool for assessing the activities of autonomic nervous system. The approach consists of (1) monitoring of heart rate signals, (2) signal processing using wavelet transform (WT) (different wavelets), (3) neuro fuzzy evaluation techniques to provide robustness in HRV analysis, (4) monitoring the function of ANS under different stress conditions. Our experiment involves 20 physically fit persons under different times (before smoking and after smoking). Nero fuzzy technique have been used to model the experimental data.     

基于体震信号的心率检测装置的设计与实现

设计并实现了一种基于体震信号的心率检测装置。心脏泵血所引起的人体和与其接触物体的震动,经过压力传感器转换为电信号,通过放大模块等信号处理电路,再由串行接口输出到PC机中进行预处理,最后,利用一种改进的心率检测算法得到心率。同步采集一路心电信号作为时间基准测量心率,并检验装置的准确性,实验结果表明:两者测得的心率基本相同。     

一种基于PCA的MPSK信号码元速率估计方法

针对MPSK信号的码元速率估计问题, 研究了有限数据条件下循环谱的谱线特征受到背景色噪声干扰的现象, 提出了一种基于主分量分析(PCA)的循环谱特征码元速率估计方法。PCA变换抑制了信号循环谱中的背景色噪声, 提高了估计精度, 减小了估计方差。仿真表明, 该方法在有限数据条件下具有良好的估计性能, 适用于不同成形滤波系数的MPSK信号。     

Application of empirical mode decomposition (EMD) for automated identification of congestive heart failure using heart rate signals

Electrocardiogram is widely used to diagnose the congestive heart failure (CHF). It is the primary noninvasive diagnostic tool that can guide in the management and follow-up of patients with CHF. Heart rate variability (HRV) signals which are nonlinear in nature possess the hidden signatures of various cardiac diseases. Therefore, this paper proposes a nonlinear methodology, empirical mode decomposition (EMD), for an automated identification and classification of normal and CHF using HRV signals. In this work, HRV signals are subjected to EMD to obtain intrinsic mode functions (IMFs). From these IMFs, thirteen nonlinear features such as approximate entropy \( (E_{\text{ap}}^{x} ) \), sample entropy \( (E_{\text{s}}^{x} ) \), Tsallis entropy \( (E_{\text{ts}}^{x} ) \), fuzzy entropy \( (E_{\text{f}}^{x} ) \), Kolmogorov Sinai entropy \( (E_{\text{ks}}^{x} ) \), modified multiscale entropy \( (E_{{{\text{mms}}_{y} }}^{x} ) \), permutation entropy \( (E_{\text{p}}^{x} ) \), Renyi entropy \( (E_{\text{r}}^{x} ) \), Shannon entropy \( (E_{\text{sh}}^{x} ) \), wavelet entropy \( (E_{\text{w}}^{x} ) \), signal activity \( (S_{\text{a}}^{x} ) \), Hjorth mobility \( (H_{\text{m}}^{x} ) \), and Hjorth complexity \( (H_{\text{c}}^{x} ) \) are extracted. Then, different ranking methods are used to rank these extracted features, and later, probabilistic neural network and support vector machine are used for differentiating the highly ranked nonlinear features into normal and CHF classes. We have obtained an accuracy, sensitivity, and specificity of 97.64, 97.01, and 98.24 %, respectively, in identifying the CHF. The proposed automated technique is able to identify the person having CHF alarming (alerting) the clinicians to respond quickly with proper treatment action. Thus, this method may act as a valuable tool for increasing the survival rate of many cardiac patients.

     

Computerized screening of children congenital heart diseases

In this paper, we propose a method for automated screening of congenital heart diseases in children through heart sound analysis techniques. Our method relies on categorizing the pathological murmurs based on the heart sections initiating them. We show that these pathelogical murmur categories can be identified by examining the heart sound energy over specific frequency bands, which we call, Arash-Bands. To specify the Arash-Band for a category, we evaluate the energy of the heart sound over all possible frequency bands. The Arash-Band is the frequency band that provides the lowest error in clustering the instances of that category against the normal ones. The energy content of the Arash-Bands for different categories constitue a feature vector that is suitable for classification using a neural network. In order to train, and to evaluate the performance of the proposed method, we use a training data-bank, as well as a test data-bank, collectively consisting of ninety samples (normal and abnormal). Our results show that in more than 94% of cases, our method correctly identifies children with congenital heart diseases. This percentage improves to 100%, when we use the Jack-Knife validation method over all the 90 samples.     

Autonomic function assessment using analysis of heart rate variability

Analysis of changes in heart rate can be useful in determining the state of various body systems. In particular the analysis of heart rate variability (HRV) is used in the assessment of autonomic function. This paper uses the discrete harmonic wavelet transform for a time-frequency analysis of HRV data to show changes in spectral power over time. Signals representing patient heart rate are presented, and methods for spectral and time-frequency analysis are described. Three sets of patient data are then analysed using these methods. The results show the potential of time-frequency analysis in the assessment of medical disorders, such as the sleep apnoea syndrome, where transient alterations in autonomic function occur.     

Robust heart rate variability analysis by generalized entropy minimization

Typical heart rate variability (HRV) times series are cluttered with outliers generated by measurement errors, artifacts and ectopic beats. Robust estimation is an important tool in HRV analysis, since it allows clinicians to detect arrhythmia and other anomalous patterns by reducing the impact of outliers. A robust estimator for a flexible class of time series models is proposed and its empirical performance in the context of HRV data analysis is studied. The methodology entails the minimization of a pseudo-likelihood criterion function based on a generalized measure of information. The resulting estimating functions are typically re-descending, which enable reliable detection of anomalous HRV patterns and stable estimates in the presence of outliers. The infinitesimal robustness and the stability properties of the new method are illustrated through numerical simulations and two case studies from the Massachusetts Institute of Technology and Boston’s Beth Israel Hospital data, an important benchmark data set in HRV analysis.