Optimization of processing parameters for the analysis and detection of embolic signals.

The fast Fourier transform (FFT), which is employed by all commercially available ultrasonic systems, provides a time-frequency representation of Doppler ultrasonic signals obtained from blood flow. The FFT assumes that the signal is stationary within the analysis window. However, the presence of short duration embolic signals invalidates this assumption. For optimal detection of embolic signals if FFT is used for signal processing, it is important that the FFT parameters such as window size, window type, and required overlap ratio should be optimized. The effect of varying window type, window size and window overlap ratio were investigated for both simulated embolic signals, and recorded from patients with carotid artery stenosis. An optimal compromise is the use of a Hamming or Hanning window with a FFT size of 64 (8.9 ms) or 128 (17.9 ms). A high overlap ratio should also be employed in order not to miss embolic events occurring at the edges of analysis windows. The degree of overlap required will depend on the FFT size. The minimum overlap should be 65% for a 64-point window and 80% for a 128-point window.     

Online automated detection of cerebral embolic signals using a wavelet-based system

Transcranial Doppler ultrasound (US) can be used to detect emboli in the cerebral circulation. We have implemented and evaluated the first online wavelet-based automatic embolic signal-detection system, based on a fast discrete wavelet transform algorithm using the Daubechies 8th order wavelet. It was evaluated using a group of middle cerebral artery recordings from 10 carotid stenosis patients, and a 1-h compilation tape from patients with particularly small embolic signals, and compared with the most sensitive commercially available software package (FS-1), which is based on a frequency-filtering approach using the Fourier transform. An optimal combination of a sensitivity of 78.4% with a specificity of 77.5% was obtained. Its overall performance was slightly below that of FS-1 (sensitivity 86.4% with specificity 85.2%), although it was superior to FS-1 for embolic signals of short duration or low energy (sensitivity 75.2% with specificity 50.5%, compared to a sensitivity of 55.6% and specificity of 55.0% for FS-1). The study has demonstrated that the fast wavelet transform can be computed online using a standard personal computer (PC), and used in a practical system to detect embolic signals. It may be particularly good for detecting short-duration low-energy signals, although a frequency filtering-based approach currently offers a higher sensitivity on an unselected data set.     

Time-frequency analysis of arterial pressure oscillations in anesthetized dogs: effects of standardized hemorrhages

The purpose of this preliminary study was to investigate the advantages of the time-frequency analysis through the Continuous Wavelet Transform (CWT) compared to classical Fourier analysis using the Fast Fourier Transform (FFT) in arterial pressure signals from anesthetized mongrel dogs before and during standardized hemorrhages. Systemic arterial pressure pulsations were recorded using catheter-tip manometers. CWT and FFT were applied to arterial pressure pulsations to obtain module coefficients of this transformation and its associated contours during the evolution of progressive hemorrhages, in amounts of 15, 34, and 66% of the estimated total blood volume. This mathematical analysis enabled us to identify the evolution of the frequency components of aortic valve functions, heart dynamics, respiratory influences, and vasomotor activities. Furthermore, we isolated the modulating signal of amplitude modulation phenomenon present in the arterial pressure records, as described in previous papers, being the heart rate carrier frequency. The CWT is a very sensitive and reliable procedure to analyze (time-frequency) the oscillatory phenomena in two dimensions, and to provide more information than the FFT. This new analytical procedure may provide new insights in the study of shock pathophysiology.     

Use of wavelet and fast Fourier transforms in pharmacodynamics

Progress has been made in the development and application of mechanism-based pharmacodynamic models for describing the drug-specific and physiological factors influencing the time course of responses to the diverse actions of drugs. However, the biological variability in biosignals and the complexity of pharmacological systems often complicate or preclude the direct application of traditional structural and nonstructural models. Mathematical transforms may be used to provide measures of drug effects, identify structural and temporal patterns, and visualize multidimensional data from analyses of biomedical signals and images. Fast Fourier transform (FFT) and wavelet analyses are two methodologies that have proven to be useful in this context. FFT converts a signal from the time domain to the frequency domain, whereas wavelet transforms colocalize in both domains and may be utilized effectively for nonstationary signals. Nonstationary drug effects are common but have not been well analyzed and characterized by other methods. In this review, we discuss specific applications of these transforms in pharmacodynamics and their potential role in ascertaining the dynamics of spatiotemporal properties of complex pharmacological systems.     

Wavelet-based edge detection in ultrasound images

We introduce a new wavelet-based method for edge detection in ultrasound (US) images. Each beam that is analyzed is first transformed into the wavelet domain using the continuous wavelet transform (CWT). Because the CWT preserves both scale and time information, it is possible to separate the signal into a number of scales. The edge is localized by first determining the scale at which the power spectrum, based on the wavelet transform, has its maximum value. Next, at this scale we find the position of the peak for the squared CWT. This method does not depend on any threshold, after the range of scales have been determined. We suggest a range of scales for US images in general. Sample edge detections are demonstrated in US images of straight and jagged edges of simple structures submerged in water bath, and of an abdominal aorta aneurysm phantom.     

Adaptive,autoregressive spectral estimation for analysis of electrical signals of gastric origin

The electrical activity of the human stomach, which normally shows a frequency of about 0.05 Hz, may be studied non-invasively by either cutaneous electrogastrography (EGG) or surface magnetogastrography (MGG). Detection of changes in frequency with time may be useful to characterize gastric disorders. The fast Fourier transform (FFT) has been the most commonly used method for the automated spectral analysis of the signals obtained from the EGG or the MGG. We have used an autoregressive (AR) parametric spectrum estimator to analyse simulated signals of gastric electrical activity, and to evaluate the results of human studies using EGG and MGG. In comparison with the FFT, our results showed that the AR spectrum estimator provided more detailed qualitative information about frequency variations of short duration simulated signals than the FFT. In the human studies, the AR estimator was as good as the conventional FFT methods in detecting physiological changes in frequency and in identifying abnormal recordings. We conclude that the AR spectral estimator may provide a better qualitative analysis of frequency variations in small portions of the signal, and is as useful as the FFT to analyse human EGG or MGG studies.     

Linear constraint minimum variance beamformer functional magnetic resonance inverse imaging

Accurate estimation of the timing of neural activity is required to fully model the information flow among functionally specialized regions whose joint activity underlies perception, cognition and action. Attempts to detect the fine temporal structure of task-related activity would benefit from functional imaging methods allowing higher sampling rates. Spatial filtering techniques have been used in magnetoencephalography source imaging applications. In this work, we use the linear constraint minimal variance (LCMV) beamformer localization method to reconstruct single-shot volumetric functional magnetic resonance imaging (fMRI) data using signals acquired simultaneously from all channels of a high density radio-frequency (RF) coil array. The LCMV beamformer method generalizes the existing volumetric magnetic resonance inverse imaging (InI) technique, achieving higher detection sensitivity while maintaining whole-brain spatial coverage and 100 ms temporal resolution. In this paper, we begin by introducing the LCMV reconstruction formulation and then quantitatively assess its performance using both simulated and empirical data. To demonstrate the sensitivity and inter-subject reliability of volumetric LCMV InI, we employ an event-related design to probe the spatial and temporal properties of task-related hemodynamic signal modulations in primary visual cortex. Compared to minimum-norm estimate (MNE) reconstructions, LCMV offers better localization accuracy and superior detection sensitivity. Robust results from both single subject and group analyses demonstrate the excellent sensitivity and specificity of volumetric InI in detecting the spatial and temporal structure of task-related brain activity.     

Evaluation of low back muscle surface EMG signals using wavelets

OBJECTIVE: To compare the ability of observers to correctly detect the reaction time of erector spinae response to unexpected load by inspecting nonprocessed electromyographic signals versus inspection of wavelet transformed electromyographic signals and versus automatic detection on the same wavelet transformed signals. BACKGROUND: Traditionally, electromyographic signal analysis is performed using Fourier transform based methods. However, muscle response to transients such as unexpected load, have limitations when using these methods of electromyographic processing. DESIGN: A comparison was made of the three methods using the same signals attained during sudden loading of the trunk. METHODS: 11 chronic low back pain patients and eleven normal subjects were investigated in sudden loading. Surface electromyographic signals were obtained from the erector spine muscle at L3. The ability of observers to detect reaction time of erector spinae muscle responses of nonprocessed electromyographic signals versus inspection of wavelet transformed electromyographic signals versus an automatic peak detection program was determined. RESULTS: The results have shown that the spine muscle reaction time was easier and more accurately determined in the wavelet domain rather than in its original signal representation. CONCLUSION: Wavelet transform methods improved the analysis of electromyographic signals in the time domain by facilitating the determination of the time of muscle activity. RELEVANCE: Wavelet transform could be a valuable tool for electromyographic analysis in resolving the psychophysical problem of perception involved in the analysis of nonprocessed signals. In clinical environments, where the speed and the accuracy of the analysis of electromyographic signal is critical, the wavelet based signal processing could be very important.     

Robust removal of short-duration artifacts in long neonatal EEG recordings using wavelet-enhanced ICA and adaptive combining of tentative reconstructions

The goal of this paper is to describe a robust artifact removal (RAR) method, an automatic sequential procedure which is capable of removing short-duration, high-amplitude artifacts from long-term neonatal EEG recordings. Such artifacts are mainly caused by movement activity, and have an adverse effect on the automatic processing of long-term sleep recordings. The artifacts are removed sequentially in short-term signals using independent component analysis (ICA) transformation and wavelet denoising. In order to gain robustness of the RAR method, the whole EEG recording is processed multiple times. The resulting tentative reconstructions are then combined. We show results in a data set of signals from ten healthy newborns. Those results prove, both qualitatively and quantitatively, that the RAR method is capable of automatically rejecting the mentioned artifacts without changes in overall signal properties such as the spectrum. The method is shown to perform better than either the wavelet-enhanced ICA or the simple artifact rejection method without the combination procedure.     

Characteristics of Doppler embolic signals observed following carotid endarterectomy

Postoperative Doppler embolic signals following carotid endarterectomy (CEA) are associated with an increased risk of stroke, but the characteristics of these signals are rarely reported. In this study, we survey signals from 1485 emboli, assumed to consist predominantly of thrombus. Data were obtained by monitoring the middle cerebral arteries of 100 consecutive CEA patients during postoperative recovery. The distribution of embolic signal frequencies, intensities and durations revealed that embolic signals do not occur randomly in the sonogram. In particular, we find that the signals possess a characteristic distribution of velocities reflecting the preferred path of the embolus through the artery (at approximately 75% of the distance between the centre of the artery and the artery wall). Embolic signals were more likely to be observed at cardiac cycle positions between 35% and 80% from the start of systole than elsewhere. After eliminating other considerations, we hypothesized that this peak in the distribution of signals in the sonogram arose due to the localization of emboli trajectories and a strong tendency for emboli to detach from the carotid bifurcation during systole.     

The 'when' and 'where' of perceiving signals of threat versus non-threat

We tested the proposal that signals of potential threat are given precedence over positive and neutral signals, reflected in earlier and more pronounced changes in neural activity. The temporal sequence ('when') and source localization ('where') of event-related potentials (ERPs) elicited by fearful and happy facial expressions, compared to neutral control expressions, were examined for 219 healthy subjects. We scored ERPs over occipito-temporal sites (N80, 50-120 ms; P120, 80-180 ms; N170, 120-220 ms; P230, 180-290 ms; N250, 230-350 ms) and their polarity-reversed counterparts over medial sites (P80, 40-120 ms; N120, 80-150 ms; VPP, 120-220 ms; N200, 150-280 ms; P300, 280-450 ms). In addition to scoring peak amplitude and latency, the anatomical sources of activity were determined using low resolution brain electromagnetic tomography (LORETA). Fearful faces were distinguished by persistent increases in positivity, associated with a dynamical shift from temporo-frontal (first 120 ms) to more distributed cortical sources (120-220 ms) and back (220-450 ms). By contrast, expressions of happiness produced a discrete enhancement of negativity, later in the time course (230-350 ms) and localized to the fusiform region of the temporal cortex. In common, fear and happiness modulated the face-related N170, and produced generally greater right hemisphere activity. These findings support the proposal that fear signals are given precedence in the neural processing systems, such that processing of positive signals may be suppressed until vigilance for potential danger is completed. While fear may be processed via parallel pathways (one initiated prior to structural encoding), neural systems supporting positively valenced input may be more localized and rely on structural encoding.     

基于离散小波变换的fMRI数据特征提取

目的 设计一种灵敏度高且处理速度快的fMRI数据小波分析方法.方法 先用离散小波变换和频谱分析确定有用信号存在的小波分解尺度,也即特征尺度;再对实验数据进行离散小波分解,重构时将非特征尺度里的小波系数设置为0;再以相关分析对小波重构信号进行激活检测.结果 对视觉数据的分析结果显示,新方法的灵敏度与基于平稳小波变换、SPM2方法相当,而优于基于提升小波变换的方法;新方法的处理速度与基于提升小波变换的方法相当,但较平稳小波变换方法有较大提高.结论 本文为fMRI数据提供了一种更为灵敏且快速的小波分析方法,更为实用.     

Wavelet transforms and the ECG: a review

The wavelet transform has emerged over recent years as a powerful time-frequency analysis and signal coding tool favoured for the interrogation of complex nonstationary signals. Its application to biosignal processing has been at the forefront of these developments where it has been found particularly useful in the study of these, often problematic, signals: none more so than the ECG. In this review, the emerging role of the wavelet transform in the interrogation of the ECG is discussed in detail, where both the continuous and the discrete transform are considered in turn.     

Frequency filtering improves ultrasonic embolic signal detection.

Problems in detection of Doppler cerebral embolic signals primarily occur for embolic signals of low relative intensity. A characteristic feature of embolic signals is that the intensity increase is maximal over a narrow frequency band. Therefore, frequency filtering of the data might improve embolic signal relative intensity and detectability. We implemented an off-line finite impulse response filter in software running on a commercially available transcranial Doppler system, using the time-domain audio data as input. The range of the filter was chosen by placing a box around the embolic signal on the spectral display. One hundred consecutive embolic signals from patients with carotid stenosis were analyzed; all had been recorded by a bigate system and the signal was analyzed in both proximal and distal channels. There was a highly significant increase in embolic signal relative intensity following frequency filtering; mean (SD) proximal channel prefiltering 12.75 (4.83) dB, postfiltering 16.36 (4.93) dB; distal channel prefiltering 13.42 (4.98) dB, postfiltering 16.60 (5.11) dB, for both p < 0.001. Despite all embolic signals being audible and visible in at least one channel on the frequency spectral display, in 17 cases, the amplitude increase associated with the embolic signal could not be clearly seen in time-domain data of one or both channels prior to filtering. Following frequency filtering, this was reduced to 5. Incorporation of such a frequency-filtering approach to an online system is likely to improve the sensitivity of online detection for embolic signals of low relative intensity.     

Noninvasive measurement of neuronal activity with near-infrared optical imaging

Diffuse optical imaging (DOI) alone offers the possibility of simultaneously and noninvasively measuring neuronal and vascular signals in the brain with temporal resolution of up to 1 ms. However, while optical measurement of hemodynamic signals is well established, optical measurement of neuronal activation (the so-called fast signal) is just emerging and requires further optimization and validation. In this work, we present preliminary studies in which we measured the fast signal in 10 healthy volunteers during finger-tapping, tactile stimulation, and electrical median nerve stimulation. We used an instrument (CW4) with 8 source (690 and 830 nm) and 16 detector positions-more optodes than the instruments in previously reported studies. This allowed us to record the ipsilateral and contralateral sensorimotor cortex simultaneously, while at the same time measuring the evoked hemodynamic response. We used an acquisition time of 25 ms per image; after averaging approximately 1000 events, the signal-to-noise ratio was approximately 10(4). Since the expected relative intensity changes due to the fast signal (approximately 10(-3)) are smaller than the relative intensity changes due to physiological effects (approximately 10(-1)), we enhanced the suppression of competing signals such as the heartbeat-associated intensity changes, and established five criteria with which to assess the robustness of the fast signal. We detected the fast signal in 43% of the measurements during finger-tapping, 60% of those during tactile stimulation, and 23% of those during electrical median nerve stimulation. The relative changes in intensity associated with the fast signal were approximately 0.07% and the latency of the signal was approximately 100 ms.     

Quantification of Embolic Showers Using Radio-Frequency Based TCD Analysis

During cardiac surgery and cardiology interventions, microemboli may be generated and disperse in the systemic circulation. The amount of microemboli that ends up in cerebral blood vessels is associated with postoperative neurologic complications. During cardiac surgery a large amount of cerebral microemboli can occur at once and create so-called “cerebral embolic showers.” To correlate postoperative neurologic outcome to cerebral embolic load, a quantitative evaluation of these embolic showers is necessary. The standard monitoring technology to visualize cerebral microemboli is transcranial Doppler (TCD). Although the conventional TCD systems are equipped with software claiming to detect microembolic signals, none of the existing TCD systems is capable of an accurate estimation of the number of cerebral microemboli in embolic showers. In this study, an algorithm with a high temporal resolution, based on the radiofrequency (RF) signal of a TCD system, has been designed to quantify these showers. Evaluation by three independent observers of a training set demonstrates that the proposed method has a sensitivity of at least one order of magnitude better than the automatic detection algorithm on the existing Doppler device used. RF-based emboli detection can possibly become a standard addition to conventional Doppler methods, considering that accurate estimation of the embolic load supports quantification of neurologic risk during various surgical procedures. (E-mail: l.sauren@ctc.unimaas.nl)     

The matching pursuit: a new method of characterizing microembolic signals?

Detection of clinically silent circulating microemboli within cerebral arteries by transcranial Doppler ultrasound (US) is now being widely investigated in the hope of identifying patients at increased risk for stroke. However, the widespread application of embolus detection is still limited in clinical practice because current transcranial Doppler systems have not the required sensitivity and specificity to analyze microembolic signals, particularly to distinguish between gaseous, or solid brain emboli and artefacts. In this work, we proposed to investigate the potential of a new approach for the analysis of microembolic signals via the so-called matching pursuit, which is closely related to wavelet transform and is not subject to the same limitations as the fast Fourier transform. Our preliminary results clearly indicate that matching pursuit is well suited to this task.     

Denoising of arterial and venous Doppler signals using discrete wavelet transform: Effect on clinical parameters

In this paper, the effects of a wavelet transform based denoising strategy on clinical Doppler parameters are analyzed. The study scheme included: (a) Acquisition of arterial and venous Doppler signals by sampling the audio output of an ultrasound scanner from 20 healthy volunteers, (b) Noise reduction via decomposition of the signals through discrete wavelet transform, (c) Spectral analysis of noisy and noise-free signals with short time Fourier transform, (d) Curve fitting to spectrograms, (e) Calculation of clinical Doppler parameters, (f) Statistical comparison of parameters obtained from noisy and noise-free signals. The decomposition level was selected as the highest level at which the maximum power spectral density and its corresponding frequency were preserved. In all subjects, noise-free spectrograms had smoother trace with less ripples. In both arterial and venous spectrograms, denoising resulted in a significant decrease in the maximum (systolic) and mean frequency, with no statistical difference in the minimum (diastolic) frequency. In arterial signals, this leads to a significant decrease in the calculated parameters such as Systolic/Diastolic Velocity Ratio, Resistivity Index, Pulsatility Index and Acceleration Time. Acceleration Index did not change significantly. Despite a successful denoising, the effects of wavelet decomposition on high frequency components in the Doppler signal should be challenged by comparison with reference data, or, through clinical investigations.     

Event-related single-shot volumetric functional magnetic resonance inverse imaging of visual processing

Developments in multi-channel radio-frequency (RF) coil array technology have enabled functional magnetic resonance imaging (fMRI) with higher degrees of spatial and temporal resolution. While modest improvement in temporal acceleration has been achieved by increasing the number of RF coils, the maximum attainable acceleration in parallel MRI acquisition is intrinsically limited only by the amount of independent spatial information in the combined array channels. Since the geometric configuration of a large-n MRI head coil array is similar to that used in EEG electrode or MEG SQUID sensor arrays, the source localization algorithms used in MEG or EEG source imaging can be extended to also process MRI coil array data, resulting in greatly improved temporal resolution by minimizing k-space traversal during signal acquisition. Using a novel approach, we acquire multi-channel MRI head coil array data and then apply inverse reconstruction methods to obtain volumetric fMRI estimates of blood oxygenation level dependent (BOLD) contrast at unprecedented whole-brain acquisition rates of 100 ms. We call this combination of techniques magnetic resonance Inverse Imaging (InI), a method that provides estimates of dynamic spatially-resolved signal change that can be used to construct statistical maps of task-related brain activity. We demonstrate the sensitivity and inter-subject reliability of volumetric InI using an event-related design to probe the hemodynamic signal modulations in primary visual cortex. Robust results from both single subject and group analyses demonstrate the sensitivity and feasibility of using volumetric InI in high temporal resolution investigations of human brain function.     

A real-time autoregressive spectrum analyzer for Doppler ultrasound signals

A system based on a digital signal processor and a microcomputer has been programmed to estimate the maximum entropy autoregressive (AR) power spectrum of ultrasonic Doppler shift signals and display the results in the form of a sonogram in real-time on a computer screen. The system, which is based on a TMS 320C25 digital signal processor chip, calculates spectra with 128 frequency components from 64 samples of the Doppler signal. The samples are collected at a programmable rate of up to 40.96 kHz, and the computation of each spectrum takes typically 3.2 ms. The feasibility of on-line AR spectral estimation makes this type of analysis an attractive alternative to the more conventional fast Fourier transform approach to the analysis of Doppler ultrasound signals.