Quantitative assessment of the use of continuous wavelet transform in the analysis of the fundamental frequency disturbance of the synthetic voice

The aim of this work is to investigate quantitatively the capability of the Continuous Wavelet Transform (CWT) as a tool to estimate (calculate) Jitter and Shimmer, assessing the error between these indices calculated in each Wavelet decomposition and the ones for the original signal, for several dilatation levels. Two synthetic vowels /a/ were generated with the fundamental frequencies of 120 Hz for male and 220 Hz for female, by an autoregressive 22 coefficient all-pole model, and Jitter and Shimmer were introduced to the signal using five different percentage variations. The signals were decomposed by CWT in eight levels of dilatation (1, 2, 4, 8, 16, 32, 64 and 128), using the Mexican Hat, Meyer and Morlet real bases. Jitter and Shimmer were calculated for the original signals and all eight levels of decompositions and then the errors between the indices in the decompositions and the original signals were calculated. It can be concluded that CWT can be used as a tool for pre-processing the signal to measure Shimmer preferentially, and Jitter, instead of using the original signal to do that. The Mexican Hat base provided the lowest errors for Shimmer analysis, where the best dilatation level was 8 (error below 0.1%). In addition, the errors associated with Shimmer index, in general, are lower than the ones associated with Jitter index.     

Improving storage efficiency of vector quantization codebook for physiological quasi-periodic signals

A quasi-periodic signal is a periodic signal with period and amplitude variations. The electrocardiogram (ECG) and several physiological signals can be treated as quasi-periodic. Vector quantization (VQ) is a valuable and universal tool for signal compression. However, the periodicity of a quasi-periodic signal causes data redundancy in the VQ codebook, where many codevectors are highly correlated. This paper explores the codebook (CB) redundancy in order to increase storage efficiency for physiological quasi-periodic signals. A quantitative CB redundancy measure and two redundancy reducing algorithms are proposed. Both algorithms use a mixed CB structure containing one and two-dimensional CBs. The first algorithm is applied to a CB directly, and the second one uses an LBG-like training algorithm to obtain a storage-efficient CB from a set of training vectors. With the MIT/BIH ECG database, the experimental results show that both algorithms can reduce the CB redundancy effectively with essentially no loss of signal quality. For comparison, the mean-shape VQ (MSVQ) proposed by Cárdenas-Barrera and Lorenzo-Ginori for ECG compression is implemented and the resulting average percent of the root-mean-square difference (PRD) is 10.78%. By using the first algorithm, the CB storage space is reduced by 40% and the resulting average PRD is 10.87%. The second algorithm can reduce the CB storage space by 75% and the average PRD is 10.27%, which is even better than the original MSVQ.     

一种基于自适应的新滤波技术

在心电信号的采集过程中,不可避免地会混入肌电噪声和各种干扰信号,为节获得含有较小噪声的ＥＣＧ信号,便于分析,需要对采集到的ＥＣＧ信号作消噪处理。     

An optimized method for tremor detection and temporal tracking through repeated second order moment calculations on the surface EMG signal

In this study, the problem of detecting and tracking tremor from the surface myoelectric signal is addressed. A method based on the calculation of a Second Order Moment Function (SOMF) inside a window W sliding over the sEMG signal is here presented. An analytical formulation of the detector allows the extraction of the optimal parameters characterizing the algorithm. Performance of the optimized method is assessed on a set of synthetic tremor sEMG signals in terms of sensitivity, precision and accuracy through the use of a properly defined cost function able to explain the overall detector performance. The obtained results are compared to those emerging from the application of optimized versions of traditional detection techniques. Once tested on a database of synthetic tremor sEMG data, a quantitative assessment of the SOMF algorithm performance is carried out on experimental tremor sEMG signals recorded from two patients affected by Essential Tremor and from two patients affected by Parkinson's Disease. The SOMF algorithm outperforms the traditional techniques both in detecting (sensitivity and positive predictive value >99% for SNR higher than 3dB) and in estimating timings of muscular tremor bursts (bias and standard deviation on the estimation of the onset and offset time instants lower than 8ms). Its independence from the SNR level and its low computational cost make it suitable for real-time implementation and clinical use.     

Spot measurement of heart rate based on morphology of PhotoPlethysmoGraphic (PPG) signals

Due to increasing health consciousness among people, it is imperative to have low-cost health care devices to measure the vital parameters like heart rate and arterial oxygen saturation (SpO₂). In this paper, an efficient heart rate monitoring algorithm based on the morphology of photoplethysmography (PPG) signals to measure the spot heart rate (HR) and its real-time implementation is proposed. The algorithm does pre-processing and detects the onsets and systolic peaks of the PPG signal to estimate the heart rate of the subject. Since the algorithm is based on the morphology of the signal, it works well when the subject is not moving, which is a typical test case. So, this algorithm is developed mainly to measure the heart rate at on-demand applications. Real-time experimental results indicate the heart rate accuracy of 99.5%, mean absolute percentage error (MAPE) of 1.65%, mean absolute error (MAE) of 1.18 BPM and reference closeness factor (RCF) of 0.988. The results further show that the average response time of the algorithm to give the spot HR is 6.85?s, so that the users need not wait longer to see their HR. The hardware implementation results show that the algorithm only requires 18 KBytes of total memory and runs at high speed with 0.85 MIPS. So, this algorithm can be targeted to low-cost embedded platforms.     

A Stochastic Nonlinear Autoregressive Algorithm Reflects Nonlinear Dynamics of Heart-Rate Fluctuations

Current methods for detecting nonlinear determinism in a time series require long and stationary data records, as most of them assume that the observed dynamics arise only from the internal, deterministic workings of the system, and the stochastic portion of the signal (the noise component) is assumed to be negligible. To explicitly account for the stochastic portion of the data we recently developed a method based on a stochastic nonlinear autoregressive (SNAR) algorithm. The method iteratively estimates nonlinear autoregressive models for both the deterministic and stochastic portions of the signal. Subsequently, the Lyapunov exponents (LE) are calculated for the estimated models in order to examine if nonlinear determinism is present in the deterministic portion of the fitted model. To determine if nonlinear dynamic analysis of heart-rate fluctuations can be used to assess arrhythmia susceptibility by predicting the outcome of invasive cardiac electrophysiologic study (EPS), we applied the SNAR algorithm to noninvasively measured resting sinus-rhythm heart-rate signals obtained from 16 patients. Our analysis revealed that a positive LE was highly correlated to a patient with a positive outcome of EPS. We found that the statistical accuracy of the SNAR algorithm in predicting the outcome of EPS was 88% (sensitivity=100%, specificity=75%, positive predictive value=80%, negative predictive value=100%, p=0.0019). Our results suggest that the SNAR algorithm may serve as a noninvasive probe for screening high-risk populations for malignant cardiac arrhythmias. © 2002 Biomedical Engineering Society. PAC2002: 8719Hh, 0545Tp, 8710+e     

Application of a modified two-point backward difference to sequential event detection in surface electromyography

Reliable detection of onset and termination of muscle contraction is an essential task in the analysis of surface electromyographic signals. An event detection method that can be used for sequential detection of both onset and termination of muscle contraction is described. The method builds on the techniques of envelope detection, two-point backward difference and threshold based decision making. Therefore, fast conventional digital signal processing techniques can be used in its implementation. Because the method is computationally efficient, it can be employed in both real-time and non-real-time applications. This text discusses the architecture of the method, considers the practical aspects of its implementation, analyses its computational complexity and evaluates its performance on the grounds of experimental results.     

Real time monitoring of muscular fatigue from dynamic surface myoelectric signals using a complex covariance approach.

A method aimed at the real-time monitoring of muscular fatigue was implemented and optimized. The method is based on an estimate of the complex covariance function in order to evaluate, in real time, the mean frequency of the myoelectric signal spectrum. Real-time implementation is guaranteed by a recursive computation of the complex covariance and then of the mean frequency. The results show good performance on both synthetic and experimental non-stationary myoelectric signals recorded during fatiguing dynamic protocols. Performance in the presence of noise is highly satisfactory on both deterministic signals and stochastic processes, even when there are strong non-stationarities. Moreover, the computational complexity is highly reduced with respect to that offered by traditional methods based on short time Fourier transform.     

早期高血压激光、电刺激疗法的信号自动识别研究

目的:针对早期高血压激光、电刺激治疗中需要多种模式刺激信号以解决患者个体化差异以及适应性的问题,提出了一种刺激信号的自动识别方法,为临床选择有效的治疗信号提供了方便、快捷的筛选手段。 方法:用已被临床试验证实有疗效的数字音频信号作为原始信号,提取该类信号的MFCC特征参数建立HMM模型库;将实验用的信号分组,有效信号识别后定出判别标准;对识别信号提取特征参数,利用维特比（Viterbi）算法计算识别信号与模型匹配程度,将识别信号与模板库中的信号自动匹配,根据匹配结果判断识别信号在模板库中所属的类别。 结果和结论:待识别信号采用HMM模型能有效地分类识别,并且可从大量的待识别信号中准确、快捷地分拣出识别信号与原始信号输出概率对数的相对值小的信号,待临床验证后用作早期高血压激光、电刺激疗法的信号源。     

On developing B-spline registration algorithms for multi-core processors

Spline-based deformable registration methods are quite popular within the medical-imaging community due to their flexibility and robustness. However, they require a large amount of computing time to obtain adequate results. This paper makes two contributions towards accelerating B-spline-based registration. First, we propose a grid-alignment scheme and associated data structures that greatly reduce the complexity of the registration algorithm. Based on this grid-alignment scheme, we then develop highly data parallel designs for B-spline registration within the stream-processing model, suitable for implementation on multi-core processors such as graphics processing units (GPUs). Particular attention is focused on an optimal method for performing analytic gradient computations in a data parallel fashion. CPU and GPU versions are validated for execution time and registration quality. Performance results on large images show that our GPU algorithm achieves a speedup of 15 times over the single-threaded CPU implementation whereas our multi-core CPU algorithm achieves a speedup of 8 times over the single-threaded implementation. The CPU and GPU versions achieve near-identical registration quality in terms of RMS differences between the generated vector fields.     

Continuous Wavelet Analysis as an Aid in the Representation and Interpretation of Electrogastrographic Signals

This paper presents an application of the continuous wavelet transform (CWT) in the analysis of electrogastrographic (EGG) signals. Due to the nonstationary nature of EGG signals, the CWT method, which uses multiresolution scaled windows, gives a better time-frequency resolution than the short-time Fourier transform, which uses a fixed window. Spike activity due to gastric contraction was investigated through experiments on dogs. During spike activity we observed an increase in magnitude of the slow wave and the appearance of a low frequency component with half the frequency of the slow wave. Studies of the EGG signals from the small intestine are also presented to investigate the hypothesis that its slow wave might be confounded with spike activity in the stomach due to the similarity of their frequency ranges. © 1998 Biomedical Engineering Society. PAC98: 0230-f, 8759Wc     

长序列心电信号最佳复杂度的确定

研究了长序列心电信号的最佳复杂度。先将原始序列符号化 ,再采用 L empel- Ziv算法来计算复杂度 ,探讨影响复杂度的各种因素 ,然后对三组不同信号即正常心电、心绞痛和心肌梗塞信号进行分析。结果表明 ,采用最佳阈值比用平均值为阈值得到的复杂度更能明显地分辨出正常和异常信号 ,原始序列符号化的阈值以及信号长度直接影响序列复杂度 ,因此 ,在实际信号的复杂度测量上 ,应采用最佳阈值和最佳信号长度。     

Bileaflet mechanical valve sound analysis using a continuous wavelet transform

Bileaflet mechanical valve closing sounds have splits, the duration of which is not constant in normally functioning valves. However, no reports have discussed the influences of valve malfunction on the split interval, neither have any studies discussed the fact that mechanical valve closing sound signals must be analyzed using a time-frequency analysis because they are nonstationary signals. The continuous wavelet transform (CWT), a time-frequency analyzing method using mother wavelets modified by scale numbers, was selected in this study for analyzing bileaflet valve closing sounds because it is easy to understand and has no limitations such as the cross-terms in the Wigner–Ville distribution or the tradeoff between time and frequency resolutions of the short-time Fourier transform. This study compares the properties of the mother wavelets of various CWTs and selects one that is suitable for detection of the clear split in bileaflet mechanical valve closing sound signals. This article also establishes a standard frequency analyzing system for bileaflet mechanical valve sounds. A preliminary study with chirp Doppler signals for comparing the frequency properties of the mother wavelets of various CWTs suggested that Ishikawa's modified Morlet CWT has better time and frequency resolution at the highest frequency scale. Morlet/power CWT analysis of normal in vivo bileaflet valve closing sounds of the ST. Jude Medical (SJM), ATS, and Carbomedics (CM) valves demonstrated clear splits of very short interval at the highest level of frequency. Detection of the disappearance of the split by using this analytical method may be the key to identifying bileaflet mechanical valve malfunction in outpatient departments.     

Fast Walsh transform data-compression algorithm: E.c.g. applications

The feasibility of using a fast Walsh transform algorithm to implement a real-time microprocessor-based e.c.g. data-compression system was studied. Using the mean square error between the original and reconstructed e.c.g. signals as a measure of the utility of the reconstructed signals, the limit to which an e.c.g. signal could be compressed and still yield an acceptable reconstruction was determined. The possibility of enhancing the quality of the reconstructed signals using linear filtering techniques was also investigated.     

基于连续小波变换的心电信号特征提取及其仪器实现

介绍了一种用于心电信号的记录和识别的虚拟式测量和分析仪器系统，目的是要构建一种基于PC的虚拟仪器．能够实现十二导联心电信号的同步记录、同步整体观察及测量12导联同一心动周期的波形，从而提高心电参数测量的准确性。同时，由于Mexican hat小波特有的时域特性，对QRS波群具有很好的定位特性和分析精度，因此在本仪器中利用连续小波变换，选用Mexicanhat作为小波基，对心电信号中的特征信息进行精确检测，并给出准确的心电信号特征描述参数。对临床实测心电信号的分析表明，即使在有严重噪声干扰的情况下，本方法也很容易实现对心电信号特征信息的精确描述，并且具有很高的实时性，从而在本仪器中获得了实际和有效的应用。     

一种基于改进经验模态分解的癫痫脑电识别新方法

在临床癫痫诊断过程中,为了提高癫痫脑电的识别率,能在癫痫发作前期就预测到癫痫疾病,其特征波的提取至关重要。针对这一问题,提出将平行延拓与镜像延拓相结合来改进EMD算法。首先,使用平行延拓的方法,在原始脑电信号的左、右端点处分别预测出一个极值;然后,使用基于镜像延拓的EMD方法,对信号进行镜像延拓,以避免经验模态分解过程中的端点效应;最后,采用支持向量机进行信号的分类识别。算法验证数据取自德国伯恩大学癫痫研究中心的脑电数据库,其中50例是正常脑电信号、50例是癫痫发作间期的脑电信号。实验研究表明：该方法对总测试脑电信号的识别率达到94%。其中,正常脑电信号和癫痫脑电信号的独立识别率均为94%,比传统EMD算法处理后的脑电识别率提高了5%,可见该方法可以有效地预测癫痫脑电。     

Model-based spectral estimation of Doppler signals using parallel genetic algorithms

Conventional spectral analysis methods use a fast Fourier transform (FFT) on consecutive or overlapping windowed data segments. For Doppler ultrasound signals, this approach suffers from an inadequate frequency resolution due to the time segment duration and the non-stationarity characteristics of the signals. Parametric or model-based estimators can give significant improvements in the time-frequency resolution at the expense of a higher computational complexity. This work describes an approach which implements in real-time a parametric spectral estimator method using genetic algorithms (GAs) in order to find the optimum set of parameters for the adaptive filter that minimises the error function. The aim is to reduce the computational complexity of the conventional algorithm by using the simplicity associated to GAs and exploiting its parallel characteristics. This will allow the implementation of higher order filters, increasing the spectrum resolution, and opening a greater scope for using more complex methods.     

Human heart beat analysis using a modified algorithm of detrended fluctuation analysis based on empirical mode decomposition

How to quantify the complexity of a physiological signal is a crucial issue for verifying the underlying mechanism of a physiological system. The original algorithm of detrended fluctuation analysis (DFA) quantifies the complexity of signals using the DFA scaling exponent. However, the DFA scaling exponent is suitable only for an integrated time series but not the original signal. Moreover, the method of least squares line is a simple detrending operation. Thus, the analysis results of the original DFA are not sufficient to verify the underlying mechanism of physiological signals. In this study, we apply an innovative timescale-adaptive algorithm of empirical mode decomposition (EMD) as the detrending operation for the modified DFA algorithm. We also propose a two-parameter scale of randomness for DFA to replace the DFA scaling exponent. Finally, we apply this modified algorithm to the database of human heartbeat interval from Physiobank, and it performs well in identifying characteristics of heartbeat interval caused by the effects of aging and of illness.     

基于小波变换和似然无偏估计的运动心电信号伪差消除法

介绍了一种基于小波变换并结合似然无偏估计来消除运动心电信号中基线漂移和肌电噪声的新方法 ,且提出了评价心电消噪算法有效性的两个指标。该方法利用小波变换多分辨率分析的特性 ,将原始运动心电信号进行多尺度分解及单支重构 ,根据运动心电信号的自身特征 ,结合似然无偏估计针对不同的心电细节成分进行阈值消噪处理。研究结果表明 ,该方法能有效消除运动心电信号中的干扰成分 ,为进一步研究运动心电信号的特征识别分析提供了新途径。     

Model-based evaluation of the short-circuited tripolar cuff configuration

Recordings of neural information for use as feedback in functional electrical stimulation are often contaminated with interfering signals from muscles and from stimulus pulses. The cuff electrode used for the neural recording can be optimized to improve the S/I ratio. In this work, we evaluate a model of both the nerve signal and the interfering signals recorded by a cuff, and subsequently use this model to study the signal to interference ratio of different cuff designs and to evaluate a recently introduced short-circuited tripolar cuff configuration. The results of the model showed good agreement with results from measurements in rabbits and confirmed the superior performance of the short-circuited tripolar configuration as compared with the traditionally used tripolar configuration.