Exploiting correlation of ECG with certain EMD functions for discrimination of ventricular fibrillation

Ventricular fibrillation (VF) is a life-threatening cardiac arrhythmia. A high impulse current is required in this stage to save lives. In this paper, an empirical mode decomposition (EMD) based algorithm is presented to separate VF from other arrhythmias. The characteristics of the VF signal has high degree of similarity with the intrinsic mode functions (IMFs) of the EMD decomposition in comparison to other ECG pathologies. This high correlation between the VF signal and its certain IMFs is exploited to separate VF from other cardiac pathologies. Reliable databases are used to verify effectiveness of our algorithm and the results demonstrate superiority of our proposed technique compared to other well-known techniques of VF discrimination.     

An optimized design of seizure detection system using joint feature extraction of multichannel EEG signals

The detection of seizure onset and events using electroencephalogram (EEG) signals are important tasks in epilepsy research. The literature available on seizure detection has discussed the implementation of advanced signal processing algorithms using tools accessed over the cloud. However, seizure monitoring application needs near sensor processing due to privacy and latency issues. In this paper, a real time seizure detection system has been implemented using an embedded system. The proposed system is based on ensemble empirical mode decomposition (EEMD) and tunable-Q wavelet transform (TQWT) algorithms. The analysis and classification of non-stationary EEG signals require the wavelet transform with high Q-factor. However, direct use of TQWT increases the computational complexity of feature extraction from multivariate EEG signals. In this paper, the first step is to process the signal by using EEMD to obtain 8 intrinsic mode functions (IMFs). The Kraskov (KraEn), sample (SampEn), and permutation (PermEn) entropy features of IMFs are extracted and based on optimum values, and 4 IMFs are decomposed using TQWT. Secondly, centered correntropy (CenCorrEn) features of the 1^st and 16^th sub-band of TQWT have been used as classifier inputs. The performance of multilayer perceptron neural networks (MLPNN), least squares support vector machine (LSSVM), and random forest (RF) classifiers has been tested on the multichannel EEG data recorded from a local hospital. The RF classifier has produced the highest accuracy of 96.2% in classifying the signals. The proposed scheme has been employed in developing an embedded seizure detection system to assist neurologists in making seizure diagnostic decisions.     

基于Hilbert-Huang变换的医学超声信号去噪

目的:探求一种基于Hilbert-Huang变换的医学超声信号去噪方法。方法:提出了一种基于Hilbert-Huang变换的医学超声信号去噪方法。首先对含噪超声信号进行经验模式分解,得到各阶IMF分量,然后对高频的IMF分量用阈值方法进行处理,把经过阈值处理的高频的IMF分量和低频IMF分量进行叠加,得到重构的去噪信号。结果:仿真实验表明,基于Hilbert-Huang变换的医学超声信号去噪方法可以有效地降噪。结论:Hilbert-Huang变换的医学超声信号去噪方法在自适应性和先验性方面优于基于小波的去噪方法。     

A Method for the Automated Detection of Venous Gas Bubbles in Humans Using Empirical Mode Decomposition

Doppler ultrasound signals are widely used to grade the quantity of circulating venous bubbles in divers. Current techniques rely on trained observers, making the grading process both time-consuming and subjective. The automated detection of bubbles, however, is confounded by the presence of other signals, primarily those arising from blood motion. Empirical Mode Decomposition was used here to calculate the intrinsic mode functions (IMFs) of a number of Doppler ultrasound signals from recreational divers, post-decompression. The IMFs provide a basis set for signal decomposition, each IMF corresponding to a different timescale in the signal. Each signal was found to comprise approximately 20 IMFs: the precise number being dependent upon the nature of the signal. A method is presented to detect bubbles using the IMF; features are first identified in the individual heart cycles, these having been previously determined using a robust peak detection method, by examining deviations from the ensemble averaged IMF. Bubbles are then identified as features appearing in more than one IMF, with significant energy in the original signal. This method has been applied to a subset of the available database and appears to perform with good sensitivity even when the signal has variable signal strength.     

Empirical mode decomposition: a novel technique for the study of tremor time series

Tremor is a clinical feature characterized by oscillations of a part of the body. The detection and study of tremor is an important step in investigations seeking to explain underlying control strategies of the central nervous system under natural (or physiological) and pathological conditions. It is well established that tremorous activity is composed of deterministic and stochastic components. For this reason, the use of digital signal processing techniques (DSP) which take into account the nonlinearity and nonstationarity of such signals may bring new information into the signal analysis which is often obscured by traditional linear techniques (e.g. Fourier analysis). In this context, this paper introduces the application of the empirical mode decomposition (EMD) and Hilbert spectrum (HS), which are relatively new DSP techniques for the analysis of nonlinear and nonstationary time-series, for the study of tremor. Our results, obtained from the analysis of experimental signals collected from 31 patients with different neurological conditions, showed that the EMD could automatically decompose acquired signals into basic components, called intrinsic mode functions (IMFs), representing tremorous and voluntary activity. The identification of a physical meaning for IMFs in the context of tremor analysis suggests an alternative and new way of detecting tremorous activity. These results may be relevant for those applications requiring automatic detection of tremor. Furthermore, the energy of IMFs was visualized as a function of time and frequency by means of the HS. This analysis showed that the variation of energy of tremorous and voluntary activity could be distinguished and characterized on the HS. Such results may be relevant for those applications aiming to identify neurological disorders. In general, both the HS and EMD demonstrated to be very useful to perform objective analysis of any kind of tremor and can therefore be potentially used to perform functional assessment.     

Single-Trial Analysis of Cortical Oscillatory Activities During Voluntary Movements Using Empirical Mode Decomposition (EMD)-Based Spatiotemporal Approach

This study presents a method based on empirical mode decomposition (EMD) and a spatial template-based matching approach to extract sensorimotor oscillatory activities from multi-channel magnetoencephalographic (MEG) measurements during right index finger lifting. The longitudinal gradiometer of the sensor unit which presents most prominent SEF was selected on which each single-trial recording was decomposed into a set of intrinsic mode functions (IMFs). The correlation between each IMF of the selected channel and raw data on other channels were created and represented as a spatial map. The sensorimotor-related IMFs with corresponding correlational spatial map exhibiting large values on primary sensorimotor area (SMI) were selected via spatial-template matching process. Trial-specific alpha and beta bands were determined in sensorimotor-related oscillatory activities using a two-spectrum comparison between the spectra obtained from baseline period (−4 to −3 s) and movement-onset period (−0.5 to 0.5 s). Sensorimotor-related oscillatory activities were filtered within the trial-specific frequency bands to resolve task-related oscillatory activities. Results demonstrated that the optimal phase and amplitude information were preserved not only for alpha suppression (event-related desynchronization) and beta rebound (event-related synchronization) but also for profound analysis of subtle dynamics across trials. The retention of high SNR in the extracted oscillatory activities allow various methods of source estimation that can be applied to study the intricate brain dynamics of motor control mechanisms. The present study enables the possibility of investigating cortical pathophysiology of movement disorder on a trial-by-trial basis which also permits an effective alternative for participants or patients who can not endure lengthy procedures or are incapable of sustaining long experiments.     

言语诱发脑干反应中频率跟随反应部分的瞬时能量谱分析

言语诱发脑干反应(s-ABR),为言语(speech,或语音)刺激诱导出的脑干诱发电位,包含四个部分:起始反应(OR)、过渡反应、频率跟随反应(FFR)及终止反应。其中FFR部分是准周期波,为周期性事件引出。目前研究FFR一般是采取对s-ABR进行基于快速傅立叶变换的频谱分析,并直接在原始时域中观察其准周期特性。由于噪声因素的影响,单个个体s-ABR的这些特性通常十分模糊,不易在原始时域中直接观察。本文提出了一种观察FFR的新方法,将s-ABR经验模态分解为有限层本征模态函数(IMF),分析各层IMF的瞬时能量(IE)谱,发现FFR部分主要突显在第二层IMF的IE谱中。本研究表明此方法能够更好地表征FFR,有利于s-ABR基础和临床的进一步研究。     

基于HHT人步行状态髋关节角度信号的分析方法

目的 提出一种基于希尔伯特-黄变换（Hilbert-Huang transform,HHT）分析人步行状态髋关节角度信号的方法 ,并验证其可行性。方法 首先,利用加速度传感器与陀螺仪组成的髋关节角度测量平台,测量健康人步行状态髋关节角度。其次,对此信号进行集合经验模态分解（ensemble empirical mode decomposition, EEMD）,得到各本征模态函数（intrinsic mode functions, IMF）,再对不同尺度的模态函数进行分析与组合。最后,对原信号进行Hilbert谱分析。结果 得到反映运动模式的特征信号以及髋关节旋转轨迹所表示的步态特征。Hilbert谱显示出主运动模式内的波内频率调制现象与步频特征。结论 此方法 适用于步态疾病患者的康复与治疗,可以有效地将髋关节角度信号不同频率尺度的特征信号进行分解,实现中心修正与滤波,达到自适应分析患者步态信号的目的 。     

Assessment of Chaotic Parameters in Nonstationary Electrocardiograms by Use of Empirical Mode Decomposition

This study addressed the issue of assessing chaotic parameters from nonstationary electrocardiogram (ECG) signals. The empirical mode decomposition (EMD) was proposed as a method to extract intrinsic mode functions (IMFs) from ECG signals. Chaos analysis methods were then applied to the stationary IMFs without violating the underlying assumption of stationarity. Eight ECG data sets representing normal and various abnormal rhythms were obtained from the American Heart Associate Ventricular Arrhythmia database. The chaotic parameters including Lyapunov exponent, entropy, and correlation dimension were computed. The results consistently showed that the 10th IMF (IMF-10) was stationary and preserved sufficient nonlinearity of the ECG signals. Each IMF-10 from the data sets (n = 8) gave a positive dominate Lyapunov exponent (0.29-0.64, p < 0.0001), a positive entropy (0.039-0.061, p < 0.0001), and a noninteger correlation dimension (1.1-1.9). These were evidences of a chaotic dynamic system. We therefore concluded that the original ECG signals must also have chaotic properties. The chaotic parameters did not show significant differences among the eight data sets representing normal sinus rhythm and various abnormalities. This study has demonstrated an effective way to characterize nonlinearities in nonstationary ECG signals by combining the empirical mode decomposition and the chaos analysis methods.     

Classification of low-density EEG for epileptic seizures by energy and fractal features based on EMD

We are here to present a new method for the classification of epileptic seizures from electroencephalogram (EEG) signals. It consists of applying empirical mode decomposition (EMD) to extract the most relevant intrinsic mode functions (IMFs) and subsequent computation of the Teager and instantaneous energy, Higuchi and Petrosian fractal dimension, and detrended fluctuation analysis (DFA) for each IMF. We validated the method using a public dataset of 24 subjects with EEG signals from 22 channels and showed that it is possible to classify the epileptic seizures, even with segments of six seconds and a smaller number of channels (e.g., an accuracy of 0.93 using five channels). We were able to create a general machine-learning-based model to detect epileptic seizures of new subjects using epileptic-seizure data from various subjects, after reducing the number of instances, based on the k-means algorithm.     

窦性和房性心律失常的DFA分析

研究证据表明许多自然系统和生物系统没有固定的特征尺度,而是展现自相似特性。本文利用消除趋势波动分析(DFA)方法,分析窦性心律、房性心律失常的ECG信号的自相似特性,以实现这两种心律失常的检测。并利用DFA方法对MIT-BIH标准数据库中的正常窦性心律、房性期前收缩(也称为房性早搏)、窦性心动过缓信号进行了分析和检测,得到这三种信号的尺度指数,据此区分出窦性心律、房性心律失常和正常窦性心律,此结果表明DFA方法能够检测窦性和房性心律失常。     

超声多普勒管壁搏动和血流信号的分离研究

本研究提出利用经验模式分解(EMD)算法分解混叠有管壁成分的超声多普勒血流信号来实现管壁搏动和血流信号的分离。该方法首先将混叠有管壁搏动的超声多普勒血流信号分解为少量有限的分量,即内模函数(IMFs),然后根据管壁搏动信号与血流信号的功率比变化曲线,用比值法自动确定并去除低频管壁博动成分。在仿真实验中用提出的方法处理模拟的多普勒信号,对于靠近管腔内壁的血流信号其在频域功率谱上的相对误差为50%,在时域幅度的相对误差为45%,与高通滤波器方法的相对误差95%相比,准确性得到提高。基于个人计算机用C语言编程实现提出的算法,对实际采集的人体颈动脉多普勒信号可实现实时分离处理。结果表明:基于经验模式分解的滤波方法能有效客观地滤除管壁搏动信号,更准确地保留低频血流信号成分。     

基于多区域分析的非接触式热红外视频心率检测方法

本研究提出了一种基于面部多区域分析的非接触式热红外视频心率检测方法.首先,确定面部3个感兴趣区域(region of interests,ROIs),构建像素均值时间序列.其次,对3个ROIs采用独立成分分析和多变量经验分解算法,分别提取包含心率信息的独立分量和本征模态函数,通过功率谱分析确定最佳独立分量和最佳本征模态...     

Preservation of quadrature Doppler signals from bidirectional slow blood flow close to the vessel wall using an adaptive decomposition algorithm

A novel approach based on the phasing-filter (PF) technique and the empirical mode decomposition (EMD) algorithm is proposed to preserve quadrature Doppler signal components from bidirectional slow blood flow close to the vessel wall. Bidirectional mixed Doppler ultrasound signals, which were echoed from the forward and reverse moving blood and vessel wall, were initially separated to avoid the phase distortion of quadrature Doppler signals (which is induced from direct decomposition by the nonlinear EMD processing). Separated unidirectional mixed Doppler signals were decomposed into intrinsic mode functions (IMFs) using the EMD algorithm and the relevant IMFs that contribute to blood flow components were identified and summed to give the blood flow signals, whereby only the components from the bidirectional slow blood flow close to the vessel wall were retained independently. The complex quadrature Doppler blood flow signal was reconstructed from a combination of the extracted unidirectional Doppler blood flow signals. The proposed approach was applied to simulated and clinical Doppler signals. It is concluded from the experimental results that this approach is practical for the preservation of quadrature Doppler signal components from the bidirectional slow blood flow close to the vessel wall, and may provide more diagnostic information for the diagnosis and treatment of vascular diseases.     

基于经验模式分解和独立分量分析的单导少次EP信号提取

EP信号的单导少次提取一直是生物医学信号处理领域倍受关注的问题。本研究利用经验模式分解（EMD）,把单导脑电信号（EP＋EEG）分解成多个基本模式分量（IMF）之和,进而选取合适的基本模式分量或者它们的组合,构成1导或多导参考信号,再利用独立分量分析（ICA）成功提取出了期望的EP信号,从而克服了ICA需要多导观测信号的要求。仿真实验证明了本方法的有效性。     

On the influence of time-series length in EMD to extract frequency content: Simulations and models in biomedical signals

In this paper, fractional Gaussian noise (fGn) was used to simulate a homogeneously spreading broadband signal without any dominant frequency band, and to perform a simulation study about the influence of time-series length in the number of intrinsic mode functions (IMFs) obtained after empirical mode decomposition (EMD). In this context three models are presented. The first two models depend on the Hurst exponent H, and the last one is designed for small data lengths, in which the number of IMFs after EMD is obtained based on the regularity of the signal, and depends on an index measure of regularity. These models contribute to a better understanding of the EMD decomposition through the evaluation of its performance in fGn signals. Since an analytical formulation to evaluate the EMD performance is not available, using well-known signals allows for a better insight into the process.The last model presented is meant for application to real data. Its purpose is to predict, in function of the regularity signal, the time-series length that should be used when one wants to divide the spectrum into a pre-determined number of modes, corresponding to different frequency bands, using EMD. This is the case, e.g., in heart rate and blood pressure signals, used to assess sympathovagal balance in the central nervous system.     

基于脉搏波的呼吸频率计算方法

为了在可穿戴医疗领域中快速检测出人体的呼吸频率,提出一种基于光电容积脉搏波的呼吸频率计算方法。首 先,通过MIMIC Database数据集获取人体同时段的脉搏波信号与呼吸波信号;其次,通过对脉搏波信号运行经验模态分 解算法,从而获得脉搏波信号的有限个本征模态函数,再选取合适的本征模态函数重构呼吸波信号;最后,通过对重构的 呼吸波信号进行特征提取,计算出呼吸频率。结果表明:经过脉搏波分解得到的呼吸信号与原始呼吸信号的相对相干系 数在0.6以上,呼吸频率也十分接近,准确率高达0.9以上。说明通过光电容积脉搏波信号计算呼吸频率的可行性,这对于 可穿戴医疗领域、无创医疗诊断具有重要意义。     

基于经验模式分解与样本熵的癫痫预测方法

本研究提出一种基于经验模式分解（empirical mode decomposition，EMD）与样本熵的癫痫预测方法；该方法首先对原始信号进行了经验模态分解，将其分解为多个平稳的固有模态函数（intrinsic mode function，IMF）之和，再选取若干个包含主要癫痫预报信息的IMF分量，将其求和后，计算其样本熵（sample entropy，SampEn）。结果表明，癫痫发作前期样本熵呈减小趋势，基于EMD的样本熵其减小幅度显著增加，同时抑制了伪差对实验结果的影响。基于经验模式分解与样本熵的癫痫预测方法能够很好的对癫痫进行预测。     

一种基于经验模态分解的时频分布及其在EEG分析中的应用

Hilbert-Huang变换是一种新的分析非线性非平稳信号的时频方法,这种方法的关键部分是经验模态分解(EMD)方法,任何复杂的信号都可以通过EM D分解为有限数目并且具有一定物理意义的固有模态函数。我们结合该方法给出一种抑制Wigner-Ville分布交叉项的新方法,并将其应用于癫痫脑电信号(EEG)中,且得到了比较好的结果。     

基于经验模式分解的心电特征提取算法

本研究应用基于经验模式分解的心电特征提取方法,利用第一本征模函数(intrinsic mode function,IMF)分量对QRS波进行定位,并通过减少分解层数、筛选次数、处理区域等策略实现了快速算法。利用MIT-BIT心律失常数据库的数据进行算法测试,取得较高的检测率,检测速度也有明显提高。实验结果表明,经验模式分解算法在QRS波定位中具有相当的优越性,临床应用中取得了良好的检测效果。