A fixed point algorithm for extracting the atrial activity in the frequency domain

In this work we present a fixed point algorithm to extract the atrial rhythm in atrial tachyarrhythmias from the surface electrocardiogram (ECG). In the frequency domain the atrial signal is characterized by a concentration of power around a main peak in the bandwidth 3-10Hz. The proposed algorithm exploits this discriminative property of the atrial component in combination with the decoupling of the atrial and other components superposed in the ECG. It recovers only the interesting atrial rhythm in a simple, fast and reliable way using the information contained in all the leads and reducing the average computational time from 0.902s (FastICA) to 0.023s (the proposed method). The algorithm is applied successfully to synthetic and real data. In simulated ECGs, the correlation index obtained was 0.792. In real ECGs, the accuracy of the results was validated using spectral and temporal parameters. The average peak frequency and spectral concentration obtained were 5.354Hz and 59.4%, respectively, and the kurtosis was 0.065.     

基于匹配追踪的癫痫脑电信号的时频分析

目的 鉴于匹配追踪算法具有良好的参数化描述特性,应用匹配追踪算法研究癫痫脑电的时频分布特征.方法 通过仿真算例,将匹配追踪算法与短时傅里叶变换、Wigner-Ville分布结果进行比较,验证该方法的频率分辨率高及参数化表征的优越性;应用上述3种方法对癫痫脑电和正常脑电进行时频分析,研究癫痫异常放电在时频平面的表现.结果 仿真结果表明基于匹配追踪算法能得到较好的时频分布;对癫痫脑电和正常脑电进行时频分析,癫痫脑电和正常脑电在时频平面上存在明显的差异.结论 基于匹配追踪的时频分析方法,能够更好地揭示脑电类非平稳信号的特征.     

心音信号的时频分析

对比研究了几种时频分析方法(WVD、CWD、CKD)及其在心音信号时频分析方面的特征和差异，并分别对正常和病态的一个心动周期的心音时频特征进行了研究，结果显示时频分析方法对心音这种非平稳信号，有较高的时频分辨率，这对于揭示心音产生的生理机制有着积极的作用，在理论研究和临床诊断中有一定的实用价植。     

基于时频混合域局部统计的帕金森病语音障碍分析方法研究

针对帕金森病语音检测问题,本文提出了一种基于时频混合域局部统计的帕金森病语音障碍分析方法。该方法首先将语音信号从时域转化为时频混合域,即进行时频化表示。在时频化表示方法中将语音信号进行分帧处理,再将每帧的语音进行傅里叶变换,通过计算得到能量谱,并将能量谱通过映射关系映射到图像空间进行可视化;其次统计信号每个能量数据在时间轴上和频率轴上的差分值,根据差分值计算该能量的梯度统计特征,用梯度统计特征来表示其不同时域与频域的能量值的突变情况;最后利用KNN分类器对提取的梯度统计特征进行分类。本文在不同的帕金森病语音数据集上进行实验,发现本文所提取的梯度统计特征在分类时有更强的聚类性。与基于传统特征与深度学习特征的分类结果相比,本文所提取的梯度统计特征在分类准确率、特异性和灵敏性上均优于前二者。实验证明了本文所提出的梯度统计特征在帕金森病语音分类诊断中的可行性。     

Novel Time-frequency Analysis and Representation of EEG

INTRODUCTION　　 Electroencephalograpm ( EEG) signals are fundamental forthe clinical diagnosisof patients with neural system diseases especially epilepsy disease.The accurate di-agnosis of the disease types and choices of medicine and therapeutic plan need to pro-cess a large amountof EEG data.In order to reserve and extractthe mostof charac-teristic of EEG signals,it is important to build an accurate EEG model for furtherdiscrimination,compression,storage,and searching.Nowadays mul…     

Problems and limitations in thrombolysis of acute stroke patients at a tertiary care center

Aim:

(1) To evaluate the number of patients thrombolysed within 1 h of arrival to emergency room (ER) (2) To identify reasons for delay in thrombolysis of acute stroke patients.

Materials and Methods:

All patients admitted to ER with symptoms suggestive of stroke from January 2011 to November 2013 were studied. Retrospective data were collected to evaluate ER to needle (door to needle time [DTNt]) time and reasons for delay in thrombolysis. The parameters studied (1) onset of symptoms to ER time, (2) ER to imaging time (door to imaging time [DTIt]), (4) ER to needle time (door to needle) and (5) contraindications for thrombolysis.

Results:

A total of 695 patients with suspected stroke were admitted during study period. 547 (78%) patients were out of window period. 148 patients (21%, M = 104, F = 44) arrived within window period (<4.5 h.). 104 (70.27%) were contraindicated for thrombolysis. Majority were intracerebral bleeds. 44 (29.7%) were eligible for thrombolysis. 7 (15.9%) were thrombolysed within 1 h. The mean time for arrival of patients from onset of symptoms to hospital (symptom to door) 83 min (median - 47). The mean door to neuro-physician time (DTPt) was 32 min (median - 15 min). The mean DTIt was 58 min (median - 50 min). The mean DTNt 104 (median - 100 min).

Conclusion:

Reasons for delay in thrombolysis are: Absence of stroke education program for common people. Lack of priority for triage and imaging for stroke patients.     

Haemodynamic determinants of the mitral valve closure sound: A finite element study

Automatic acoustic classification and diagnosis of mitral valve disease remain outstanding biomedical problems. Although considerable attention has been given to the evolution of signal processing techniques, the mechanics of the first heart sound generation has been largely overlooked. In this study, the haemodynamic determinants of the first heart sound were examined in a computational model. Specifically, the relationship of the transvalvular pressure and its maximum derivative to the time-frequency content of the acoustic pressure was examined. To model the transient vibrations of the mitral valve apparatus bathed in a blood medium, a dynamic, non-linear, fluid-coupled finite element model of the mitral valve leaflets and chordae tendinae was constructed. It was found that the root mean squared (RMS) acoustic pressure varied linearly (r²=0.99) from 0.010 to 0.259 mm Hg, following an increase in maximum dP/dt from 415 to 12470 mm Hg s⁻¹. Over that same range, peak frequency varied non-linearly from 59.6 to 88.1 Hz. An increase in left-ventricular pressure at coaptation from 22.5 to 58.5 mm Hg resulted in a linear (r²=0.91) rise in RMS acoustic pressure from 0.017 to 1.41 mm Hg. This rise in transmitral pressure was accompanied by a non-linear rise in peak frequency from 63.5 to 74.1 Hz. The relationship between the transvalvular pressure and its derivative and the time-frequency content of the first heart sound has been examined comprehensively in a computational model for the first time. Results suggest that classification schemes should embed both of these variables for more accurate classification.     

细胞胞浆Ca2+浓度的时-频分析及ELF脉冲电磁波产生的生物学窗效应

本研究以实验为基础，对细胞胞浆Ca^2＋浓度进行时-频分析，进而研究ELF脉冲电磁波在细胞胞浆Ca^2＋浓度时-频域内产生的生物学效应和生物学窗效应。结果表明：细胞胞浆Ca^2＋浓度在时-频域内有其固有的频谱特征，其中包括连续谱和离散谱。有的参数的电磁波可在细胞胞浆Ca^2＋浓度的时-频域内产生生物学效应而有的参数的电磁波却不能。若能够产生生物学效应，则有两个特征：一是使胞浆Ca^2＋浓度在时-频域内的连续谱变窄，具体表现为连续谱中的高能谱分量被抑制；二是使离散谱的分布和离散谱的频率发生了变化。同时，能够在细胞胞浆Ca^2＋浓度的时-频域内产生生物学效应的电磁波脉冲频率和脉冲强度是离散的和间隔的，这意味着，ELF脉冲电磁波能够在细胞胞浆Ca^2＋浓度的时-频域内产生生物学窗效应。本研究使用的脉冲频率范围内有两个频率窗，它们是16Hz和45Hz；使用的脉冲强度范围内有一个强度窗，是53V／m。     

Boundary modelling and shape analysis methods for classification of mammographic masses

The problem of computer-aided classification of benign and malignant breast masses using shape features is addressed. The aim of the study is to look at the exceptions in shapes of masses such as circumscribed malignant tumours and spiculated benign masses which are difficult to classify correctly using common shape analysis methods. The proposed methods of shape analysis treat the object's boundary in terms of local details. The boundaries of masses analysed using the proposed methods were manually drawn on mammographic images by an expert radiologist (JELD). A boundary segmentation method is used to separate major portions of the boundary and to label them as concave or convex segments. To analyse the shape information localised in each segment, features are computed through an iterative procedure for polygonal modelling of the mass boundaries. Features are based on the concavity fraction of a mass boundary and the degree of narrowness of spicules as characterised by a spiculation index. Two features comprising spiculation index (SI) and fractional concavity (f_cc) developed in the present study when used in combination with the global shape feature of compactness resulted in a benign/malignant classification accuracy of 82%, with an area (A_z) of 0.79 under the receiver operating characteristics (ROC) curve with a database of the boundaries of 28 benign masses and 26 malignant tumours. SI alone resulted in a classification accuracy of 80% with A_z of 0.82. The combination of all the three features achieved 91% accuracy of circumscribed versus spiculated classification of masses based on shape.     

Single trial time-frequency domain analysis of error processing in post-traumatic stress disorder

Error processing studies in psychology and psychiatry are relatively common. Event-related potentials (ERPs) are often used as measures of error processing, two such response-locked ERPs being the error-related negativity (ERN) and the error-related positivity (Pe). The ERN and Pe occur following committed error in reaction time tasks as low frequency (4-8Hz) electroencephalographic (EEG) oscillations registered at the midline fronto-central sites. We created an alternative method for analyzing error processing using time-frequency analysis in the form of a wavelet transform. A study was conducted in which subjects with PTSD and healthy control completed a forced-choice task. Single trial EEG data from errors in the task were processed using a continuous wavelet transform. Coefficients from the transform that corresponded to the theta range were averaged to isolate a theta waveform in the time-frequency domain. Measures called the time-frequency ERN and Pe were obtained from these waveforms for five different channels and then averaged to obtain a single time-frequency ERN and Pe for each error trial. A comparison of the amplitude and latency for the time-frequency ERN and Pe between the PTSD and control group was performed. A significant group effect was found on the amplitude of both measures. These results indicate that the developed single trial time-frequency error analysis method is suitable for examining error processing in PTSD and possibly other psychiatric disorders.     

Optimized Overcomplete Signal Representation and its Applications to Time-frequency Analysis of Electrogastrogram

The electrogastrogram (EGG) is a surface measurement of gastric myoelectrical activity. The normal frequency of gastric myoelectrical activity in humans is 3 cycles/min. Abnormal frequencies in gastric myoelectrical activity have been found to be associated with functional disorders of the stomach. The aim of this article was, therefore, to develop new time-frequency analysis methods for the detection of gastric dysrhythmia from the EGG. A concept of overcomplete signal representation was used. Two algorithms were proposed for the optimization of the overcomplete signal representation. One was a fast algorithm of matching pursuit and the other was based on an evolutionary program. Computer simulations were performed to compare the performance of the proposed methods in comparison with existing time-frequency analysis methods. It was found that the proposed algorithms provide higher frequency resolution than the short time Fourier transform and Wigner-Ville distribution methods. The practical application of the developed methods to the EGG is also presented. It was concluded that these methods are well suited for the time-frequency analysis of the EGG and may also be applicable to the time-frequency analysis of other biomedical signals. © 1998 Biomedical Engineering Society. PAC98: 8780+s, 0705Kf     

Extraction subject-specific motor imagery time-frequency patterns for single trial EEG classification

We introduce a new adaptive time-frequency plane feature extraction strategy for the segmentation and classification of electroencephalogram (EEG) corresponding to left and right hand motor imagery of a brain-computer interface task. The proposed algorithm adaptively segments the time axis by dividing the EEG data into non-uniform time segments over a dyadic tree. This is followed by grouping the expansion coefficients in the frequency axis in each segment. The most discriminative features are selected from the segmented time-frequency plane and fed to a linear discriminant for classification. The proposed algorithm achieved an average classification accuracy of 84.3% on six subjects by selecting the most discriminant subspaces for each one. For comparison, classification results based on an autoregressive model are also presented where the mean accuracy of the same subjects turned out to be 79.5%. Interestingly the subjects and two hemispheres of each subject are represented by distinct segmentations and features. This indicates that the proposed method can handle inter-subject variability when constructing brain-computer interfaces.     

Diffusion‐weighted imaging features of breast tumours and the surrounding stroma reflect intrinsic heterogeneous characteristics of molecular subtypes in breast cancer

Breast cancer heterogeneity is the main obstacle preventing the identification of patients with breast cancer with poor prognoses and treatment responses; however, such heterogeneity has not been well characterized. The purpose of this retrospective study was to reveal heterogeneous patterns in the apparent diffusion coefficient (ADC) signals in tumours and the surrounding stroma to predict molecular subtypes of breast cancer. A dataset of 126 patients with breast cancer, who underwent preoperative diffusion‐weighted imaging (DWI) on a 3.0‐T image system, was collected. Breast images were segmented into regions comprising the tumour and surrounding stromal shells in which features that reflect heterogeneous ADC signal distribution were extracted. For each region, imaging features were computed, including the mean, minimum, variance, interquartile range (IQR), range, skewness, kurtosis and entropy of ADC values. Univariate and stepwise multivariate logistic regression modelling was performed to identify the magnetic resonance imaging features that optimally discriminate luminal A, luminal B, human epidermal growth factor 2 (HER2)‐enriched and basal‐like molecular subtypes. The performance of the predictive models was evaluated using the area under the receiver operating characteristic curve (AUC). Univariate logistic regression analysis showed that the skewness in the tumour boundary achieved an AUC of 0.718 for discrimination between luminal A and non‐luminal A tumours, whereas the IQR of the ADC value in the tumour boundary had an AUC of 0.703 for classification of the HER2‐enriched subtype. Imaging features in the tumour boundary and the proximal peritumoral stroma corresponded to a higher overall prediction performance than those in other regions. A multivariate logistic regression model combining features in all the regions achieved an overall AUC of 0.800 for the classification of the four tumour subtypes. These findings suggest that features in the tumour boundary and stroma around the tumour may be further assessed as potential predictors of molecular subtypes of breast cancer.     

SSSEP提升下肢MI-BCI系统性能及其多维脑电特征分析

运动想象脑-机接口(MI-BCI)可解码用户运动意图,为无法自主运动患者提供一种额外交互控制通道,辅助或改善其生活方式。针对现有下肢MI-BCI分类性能较低等关键问题,引入了体感电刺激(ES)用于下肢MI-BCI构建混合范式(MI+ES),并与传统单一范式(MI)对比。共20名年轻健康右利手受试参与实验,5名参与最优诱发频率验证试验,15名参与正式实验。随后采集了参与正式实验的15名受试不同条件下脑电(EEG)数据,应用傅里叶变换(FFT)和事件相关谱扰动(ERSP)算法提取EEG频域响应、时频特征等,并计算alpha(8～14 Hz)、低 beta(15～24 Hz)和高 beta(25～35 Hz)等多频段能量变化。此外,分别探索了MI/(MI+ES)条件、共空间模式(CSP)/基于多频率成分的共空间模式(FBCSP)特征提取方法对下肢MI-BCI系统分类性能的影响。结果表明, 引入体感电刺激策略可诱发明显的SSSEP特征,MI+ES条件分类准确率较单一MI条件有显著性提升(P<0.001),且应用FBCSP方法的系统分类准确率显著优于经典CSP方法(P<0.01):CSP特征提取方法下MI+ES条件的平均分类准确率为70.2%,其中受试S15的分类准确率达84.2%;FBCSP方法下的平均分类准确率为71.7%,受试S15的分类结果达到90%。初步证实了受试在体感电刺激条件下可诱发出明显的SSSEP特征,而且其融合MI可有效提升下肢MI-BCI分类性能,可支撑下肢MI-BCI系统的实用化进程,也为外周神经相关体感刺激调控方法的优化设计提供了新的技术思路。     

Limited prognostic value of cellular DNA content to classical and morphometrical parameters in invasive ductal breast cancer

This retrospective study evaluates several prognostic factors in 63 patients with invasive ductal breast cancer. Special attention is paid to the additional prognostic value of cellular DNA content to the previously developed and evaluated quantitative features mitotic activity index (MAI) and multivariate morphometric prognostic index (MPI). Follow-up was monitored for at least 50 months (median survival, 78 months) and only patients who died of distant metastases were included. The results show that the MAI is the strongest prognostic factor of all single features (Mantel-Cox, P = 0.008). Although patients with a diploid or tetraploid tumor tended to have a better prognosis than those with an aneuploid cancer, the DNA index as a single parameter was a weak prognosticator in the univariate survival analysis (Mantel-Cox, P = 0.24). Within the diploid and tetraploid tumors the MAI could distinguish patients with a favorable and unfavorable prognosis prediction (chi-square, P = 0.01). For aneuploid tumors this was not possible. Analysis of combined features revealed that the MPI has a high prognostic value (Mantel-Cox, P = 0.0015), thus confirming other studies. A linear combination of the nuclear DNA index, MAI, nodal status, and mean nuclear area showed only a slight improvement in prognosis prediction compared with the MPI (Mantel-Cox, P = 0.0005); with this rule, the classification of the patients was more in agreement with the actual outcome in 4% of the cases. The gain was in the poor prognosis group. These results suggest that the additional prognostic value of nuclear DNA content is restricted when compared with the morphometric prognostic factors. Further studies on a larger number of patients are required to confirm these findings.     

Time-frequency and time-varying analysis for assessing the dynamic responses of cardiovascular control

Time-frequency or time-variant methods have been extensively applied in the study of the heart-rate variability (HRV) signal. In fact, the frequency content of HRV signal has a strong correlation with the control system assessing heart rate. In particular, the power related to the low-frequency (LF) and high-frequency (HF) components have been demonstrated to correlate to the action of sympathetic and parasympathetic branches of the autonomic nervous system. However, the analysis is restricted to stationary conditions, unless time-frequency methods are employed for detecting dynamic changes that may occur during physiological and pathological conditions.This article reviews the most diffused tools for time-frequency analysis, starting from linear decomposition of the signal (including short-time Fourier transform and wavelet and wavelet packet decomposition), to quadratic time-frequency distributions (including Wigner-Ville transform and Cohen's class of distributions), and finally to adaptive or time-variant autoregressive (AR) models, in both the mono- and bivariate forms. In the past few years, these approaches have been applied in several studies related to cardiovascular responses during nonstationary pathophysiological events. Among them, we will recall and discuss myocardial ischemia (spontaneous or induced), drug infusion, rest-tilt maneuver and syncope, neurophysiological, and sleep investigations.     

Automated neural network detection of wavelet preprocessed electrocardiogram late potentials

The aim of the study is to investigate the potential of a feedforward neural network for detecting wavelet preprocessed late potentials. The terminal parts of a simulated QRS complex are processed with a continuous wavelet transform, which leads to a time-frequency represenation of the QRS complex. Then, diagnostic feature vectors are obtained by subdividing the representations into several regions and by processing the sum of the decomposition coefficients belonging to each region. The neural network is trained with these feature vectors. Simulated ECGs with varying signalto-noise ratios are used to train and test the classifier. Results show that correct classification ranges from 79% (high-level noise) to 99% (no noise). The study shows the potential of neural networks for the classification of late potentials that have been preprocessed by a wavelet transform. However, clinical use of this method still requires further investigation.     

High-resolution esophageal long-term ECG allows detailed atrial wave morphology analysis in case of atrial ectopic beats

Detection of arrhythmic atrial beats in surface ECGs can be challenging when they are masked by the R or T wave, or do not affect the RR-interval. Here, we present a solution using a high-resolution esophageal long-term ECG that offers a detailed view on the atrial electrical activity. The recorded ECG shows atrial ectopic beats with long coupling intervals, which can only be successfully classified using additional morphology criteria. Esophageal high-resolution ECGs provide this information, whereas surface long-term ECGs show poor atrial signal quality. This new method is a promising tool for the long-term rhythm monitoring with software-based automatic classification of atrial beats.     

Selection of Dynamic Features Based on Time–Frequency Representations for Heart Murmur Detection from Phonocardiographic Signals

This work discusses a method for the selection of dynamic features, based on the calculation of the spectral power through time applied to the detection of systolic murmurs from phonocardiographic recordings. To investigate the dynamic properties of the spectral power during murmurs, several quadratic energy distributions have been studied, namely Wigner-Ville, Choi-Williams, smoothed pseudo Wigner-Ville, exponential, and hyperbolic T-distribution. The classification performance has been compared with that using a Short Time Fourier Transform and Continuous Wavelet Transform representations. Furthermore, this work discusses a variety of nonparametric techniques to estimate the spectral power contours as dynamic features that characterize the heart sounds (HS): instantaneous energy, eigenvectors, instantaneous frequency, equivalent bandwidth, subband spectral centroids, and Mel cepstral coefficients. In this way, the aforementioned time–frequency representations and their dynamic features were evaluated by means of their ability to detect the presence of murmurs using a simple k-Nearest Neighbors classifier. Moreover, the relevancies of the proposed dynamic features have been evaluated using a time-varying principal component analysis. The work presented is carried out using a database containing 22 phonocardiographic recordings (16 normal and 6 records with murmurs), segmented to extract 402 representative individual beats (201 per class). The results suggest that the smoothing given by the quadratic energy distribution significantly improves the classification performance for the detection of murmurs in HS. Moreover, it is shown that the power dynamic features which give the best overall classification performance are the MFCC contours. As a result, the proposed method can be implemented as a simple diagnostic tool for primary health-care purposes with high accuracy (up to 98%) discriminating between normal and pathologic beats.     

The evaluation of EEG response to photic stimulation in normal and diseased subjects

In this paper, our aim is to determine two photic stimulation frequencies, which would represent normal and diseased subjects, separately. Following features were extracted for this aim; linear prediction coefficients (LPC), subband wavelet entropy (SWE), subband wavelet variance (SWV), and relative power (RP). After extracting related features, analysis of variance (ANOVA) statistical test was used for the statistical evaluation of these features. According to the obtained results, wavelet transform-based entropy gave the best results to determine the representing stimulation frequencies. As a result, 29 Hz stimulation frequency was determined as the most representative frequency for normal subjects, whereas 8 Hz stimulation frequency was determined as the most representative frequency for diseased subjects.