Algorithm for the classification of multi-modulating signals on the electrocardiogram

This article discusses the algorithm to measure electrocardiogram (ECG) and respiration simultaneously and to have the diagnostic potentiality for sleep apnoea from ECG recordings. The algorithm is composed by the combination with the three particular scale transform of a(j)(t), u(j)(t), o(j)(a(j)) and the statistical Fourier transform (SFT). Time and magnitude scale transforms of a(j)(t), u(j)(t) change the source into the periodic signal and tau(j) = o(j)(a(j)) confines its harmonics into a few instantaneous components at tau(j) being a common instant on two scales between t and tau(j). As a result, the multi-modulating source is decomposed by the SFT and is reconstructed into ECG, respiration and the other signals by inverse transform. The algorithm is expected to get the partial ventilation and the heart rate variability from scale transforms among a(j)(t), a(j+1)(t) and u(j+1)(t) joining with each modulation. The algorithm has a high potentiality of the clinical checkup for the diagnosis of sleep apnoea from ECG recordings.     

Automatic detection of the electrocardiogram T-wave end

Various methods for automatic electrocardiogram T-wave detection and Q-T interval assessment have been developed. Most of them use threshold level corrsing. Comparisons with observer detection were performed due to the lack of objective measurement methods. This study followed the same approach. Observer assessments were performed on 43 various T-wave shapes recorded: (i) with 100 mms⁻¹ equivalent paper speed and 0.5mVcm⁻¹ sensitivity; and (ii) with 160 mms⁻¹ paper speed and vertical scaling ranging from 0.07 to 0.02 m Vcm⁻¹, depending on the T-wave amplitude. An automatic detection algorithm was developed by adequate selection of the T-end search interval, improved T-wave peak detection and computation of the angle between two 10ms long adjacent segments along the search interval. The algorithm avoids the use of baseline crossin direct signal differentiation. It performs well in cases of biphasic and/or complex T-wave shapes. Mean differences with respect to observer data are 13.5 ms for the higher gain/speed records and 14.7 ms for the lower gain/speed records. The algorithm was tested with 254 various T-wave shapes. Comparisons with two other algorithms are presented. The lack of a ‘gold standard’ for the T-end detection, especially if small waves occur around it, impeded adequate interobserver assessment and evaluation of automatic methods. It is speculated that a simultaneous presentation of normal and high-gain records might turn more attention to this problem. Automatic detection methods are in fact faced with ‘high-gain’ data, as high-resolution analogue-to-digital conversion, is already widely used.     

Automatic detection of the electrocardiogram T-wave end

Various methods for automatic electrocardiogram T-wave detection and Q-T interval assessment have been developed. Most of them use threshold level crossing. Comparisons with observer detection were performed due to the lack of objective measurement methods. This study followed the same approach. Observer assessments were performed on 43 various T-wave shapes recorded: (i) with 100 mm s-1 equivalent paper speed and 0.5 mV cm-1 sensitivity; and (ii) with 160 mm s-1 paper speed and vertical scaling ranging from 0.07 to 0.02 mV cm-1, depending on the T-wave amplitude. An automatic detection algorithm was developed by adequate selection of the T-end search interval, improved T-wave peak detection and computation of the angle between two 10 ms long adjacent segments along the search interval. The algorithm avoids the use of baseline crossing and direct signal differentiation. It performs well in cases of biphasic and/or complex T-wave shapes. Mean differences with respect to observer data are 13.5 ms for the higher gain/speed records and 14.7 ms for the lower gain/speed records. The algorithm was tested with 254 various T-wave shapes. Comparisons with two other algorithms are presented. The lack of a 'gold standard' for the T-end detection, especially if small waves occur around it, impeded adequate interobserver assessment and evaluation of automatic methods. It is speculated that a simultaneous presentation of normal and high-gain records might turn more attention to this problem. Automatic detection methods are in fact faced with 'high-gain' data, as high-resolution analogue-to-digital conversion is already widely used.     

An improved algorithm for the automatic detection and characterization of slow eye movements

Slow eye movements (SEMs) are typical of drowsy wakefulness and light sleep. SEMs still lack of systematic physical characterization. We present a new algorithm, which substantially improves our previous one, for the automatic detection of SEMs from the electro-oculogram (EOG) and extraction of SEMs physical parameters. The algorithm utilizes discrete wavelet decomposition of the EOG to implement a Bayes classifier that identifies intervals of slow ocular activity; each slow activity interval is segmented into single SEMs via a template matching method. Parameters of amplitude, duration, velocity are automatically extracted from each detected SEM. The algorithm was trained and validated on sleep onsets and offsets of 20 EOG recordings visually inspected by an expert. Performances were assessed in terms of correctly identified slow activity epochs (sensitivity: 85.12%; specificity: 82.81%), correctly segmented single SEMs (89.08%), and time misalignment (0.49 s) between the automatically and visually identified SEMs. The algorithm proved reliable even in whole sleep (sensitivity: 83.40%; specificity: 72.08% in identifying slow activity epochs; correctly segmented SEMs: 93.24%; time misalignment: 0.49 s). The algorithm, being able to objectively characterize single SEMs, may be a valuable tool to improve knowledge of normal and pathological sleep.     

A computer system for quantitative analysis of gastrointestinal signals

A technique for detecting and quantifying non stationary repetitive slow waves in noisy signals is described. It is based on an improvement of the signal-to-noise ratio by means of correlation functions combined to spectral analysis.

A digital computer is used to perform automatically the different steps of this analysis. Applied to mechanical and electrical activities simultaneously recorded in the rat colon, the computer aided analysis presented in this paper allows an accurate investigation of the dynamics of the physiological phenomena.     

一种基于二维云模型理论心电信号ST段的检测方法

背景：目前对于异常ST段的形态特征的评价体系,尚无精确数学化模型分类及识别标准,这阻碍了对某些心血管疾病进行计算机自动识别程序算法的进展。 目的：找到一种符合医学诊断逻辑思维的心电信号ST段分析方法,为心电图ST段变化的实时分析提供新的思路。 方法：针对采集的心电数据模糊性和随机性较大的特点,提出了一种基于两维云模型的心电信号ST段的检测方法,其对二维正态云发生器隶属度进行判断,并进行心电信号ST段的形态判定。 结果与结论：利用Matlab对本算法进行仿真,并通过标准心电数据库(欧共体CSE数据库)中数据进行算法准确性验证,统计结果表明该算法ST段的识别率较高,对于大数据量的心电信号处理快捷有效,为ST段的准确分析提供了新的方法。     

Parametric and non-parametric estimation of speech formants: application to infant cry

The present paper addresses the issue of correctly estimating the peaks in the speech envelope (formants) occurring in newborn infant cry. Clinical studies have shown that the analysis of such spectral characteristics is a helpful noninvasive diagnostic tool. In fact it can be applied to explore brain function at very early stages of child development, for a timely diagnosis of neonatal disease and malformation. The paper focuses on the performance comparison between some classical parametric and non-parametric estimation techniques particularly well suited for the present application, specifically the LP, ARX and cepstrum approaches. It is shown that, if the model order is correctly chosen, parametric methods are in general more reliable and robust against noise, but exhibit a less uniform behaviour than cepstrum. The methods are compared also in terms of tracking capability, since the signals under study are nonstationary. Both simulated and real signals are used in order to outline the relevant features of the proposed approaches.     

Optical anisotropy of collagen fibers of rat calcaneal tendons: An approach to spatially resolved supramolecular organization

Optical anisotropic characteristics investigated by polarization microscopy have been valuable for the study of the oriented organization of collagen fibers in tendons. However, topographic differences in supramolecular organization of collagen fibers along extensive areas in tendons have not yet been described. Here, the statistical variability of the oriented organization of collagen fibers along extensive areas (10(5)-10(6)microm(2)) of 7-microm thick unstained sections of rat calcaneal tendons were studied by assessing their birefringence with polarization microscopy and image analysis, and the periodic frequency distribution in their birefringent images with the fast Fourier transform (FFT). Various levels of birefringence intensity were determined principally by image analysis procedures, and periodicity in the birefringent images was revealed by FFT line profile and spectrum image patterns. Present results support the idea of a helical distribution for collagen bundles along the tendon long axis, and of a statistical architecture for the rat calcaneal tendons in terms of variability in the oriented distribution of their collagen fibers.     

Regression analysis of nonlinear Arrhenius plots: An empirical model and a computer program

The rates of most physical, chemical and biological processes vary with temperature and numerous instances have been reported in which Arrhenius plots of the experimental data appear to consist of two straight line segments joined by a relatively sharp break. An empirical model, based on a general hyperbola, is shown to be applicable to such systems; a nonlinear regression program is described which facilitates fitting of this function to experimental data which show either broad or sharp transitions. The program yields best estimates and standard errors of the temperature and ordinate value at the transition, and the two activation energies.     

Non-invasive evaluation of abdominal aortic properties: lumped circuit model and estimation of its parameters

To non-invasively determine abdominal aortic properties, a five-element lumped circuit model was adopted. The model consists of resistance due to blood viscosity (R1), inertia of blood flow, compliances of the vessel (C1, C2), resistance of the peripheral arteries (R2) and the impedance of the femoral arteries (termination). Patterns of the central velocity of the upper abdominal aorta and the femoral artery are measured by pulsed Doppler echocardiography, and confours of flow volume rates are calculated. The pressure pattern of the lower limb is recorded by a pulse wave rransducer and corrected according to sphygmomanometer values. Contours are transformed into respective Fourier transform components. The current transfer function is described theoretically and calculated from the acquired Fourier components. Values of every element are evaluated by the nonlinear least squares method. In 94 subjects (17–92 years), the values of each element are estimated. R2 values are greater in the elderly group, than in the young group and r₁ (R1/cm) increased with age. This model demonstrates that vessel compliance (c₁+c₂ (C1+C2/cm)) decreases with age, and it is suggested that this may be a useful marker of arteriosclerosis.     

Pattern identification of biomedical images with time series: Contrasting THz pulse imaging with DCE-MRIs

ObjectiveWe provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities.MethodsBoth time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed.ValidationExamples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed.ResultsIdentifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis.ConclusionThe proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community.