Researchers and clinicians routinely rely on interference electromyograms (EMGs) to estimate muscle forces and command signals in the neuromuscular system (e.g., amplitude, timing, and frequency content). The amplitude cancellation intrinsic to interference EMG, however, raises important questions about how to optimize these estimates. For example, what should the length of the epoch (time window) be to average an EMG signal to reliably estimate muscle forces and command signals? Shorter epochs are most practical, and significant reductions in epoch have been reported with high-pass filtering and whitening. Given that this processing attenuates power at frequencies of interest (< 250 Hz), however, it is unclear how it improves the extraction of physiologically-relevant information. We examined the influence of amplitude cancellation and high-pass filtering on the epoch necessary to accurately estimate the "true" average EMG amplitude calculated from a 28 s EMG trace (EMG(ref)) during simulated constant isometric conditions. Monte Carlo iterations of a motor-unit model simulating 28 s of surface EMG produced 245 simulations under 2 conditions: with and without amplitude cancellation. For each simulation, we calculated the epoch necessary to generate average full-wave rectified EMG amplitudes that settled within 5% of EMG(ref.) For the no-cancellation EMG, the necessary epochs were short (e.g., < 100 ms). For the more realistic interference EMG (i.e., cancellation condition), epochs shortened dramatically after using high-pass filter cutoffs above 250 Hz, producing epochs short enough to be practical (i.e., < 500 ms). We conclude that the need to use long epochs to accurately estimate EMG amplitude is likely the result of unavoidable amplitude cancellation, which helps to clarify why high-pass filtering (> 250 Hz) improves EMG estimates.
In this paper, a new adaptive bolus-chasing control scheme is proposed to synchronize the bolus peak in a patient's vascular system and the imaging aperture of a computed tomography (CT) scanner. The proposed control scheme is theoretically evaluated and experimentally tested on a modified Siemens SOMATOM Volume Zoom CT scanner. The first set of experimental results are reported on bolus-chasing CT angiography using realistic bolus dynamics, real-time CT imaging and adaptive table control with physical vasculature phantoms. The data demonstrate that the proposed control approach tracks the bolus propagation well, and clearly outperforms the constant-speed scheme that is the current clinical standard.
Synchronization of the contrast bolus peak and CT imaging aperture is a crucial issue for computed tomography angiography (CTA). It affects the CTA image quality and the amount of contrast dose. A whole-body CTA procedure means to scan from the abdominal aorta to pedal arteries. In this context, the synchronization is much more difficult with the asymmetric arterial flow in lower extremities than in the case of symmetric arterial flow. In this paper, we propose an adaptive optimal controller to chase the contrast bolus peak while it propagates in the aorta and lower extremities with symmetric flow. In the case of asymmetric flow after the contrast bolus splitting into two lower limbs, we propose a dynamic programming approach to cover the lower limbs optimally. Simulation and experimental results show that the proposed methods outperform the current constant-speed method substantially.
The present paper describes the design of two stimulators (bench-top and portable) which can be used for animal studies in cochlear implants. The bench-top stimulator is controlled by a high-speed digital output board manufactured by National Instruments and is electrically isolated. The portable stimulator is controlled by a personal digital assistant (PDA) and is based on a custom interface board that communicates with the signal processor in the PDA through the secure digital IO (SDIO) slot. Both stimulators can provide 8 charge-balanced, bipolar channels of pulsatile and analog-like electrical stimulation, delivered simultaneously, interleaved or using a combination of both modes. Flexibility is provided into the construction of arbitrary, but charge-balanced, pulse shapes, which can be either symmetric or asymmetric.
BACKGROUND: Examination of spontaneously occurring phasic muscle activity from the human polysomnogram may have considerable clinical importance for patient care, yet most attempts to quantify the detection of such activity have relied upon laborious and intensive visual analyses. We describe in this study innovative signal processing approaches to this issue. METHODS: We examined multiple features of surface electromyographic signals based on 16,200 individual 1-second intervals of low impedance sleep recordings. We validated which of those features most closely mirrored the careful judgments of trained human observers in making discriminations of the presence of short-lived (100-500 msec) phasic activity, and also examined which features provided maximal differences across 1-second intervals and which features were least susceptible to residual levels of amplifier noise. RESULTS: Our data suggested particularly promising and novel features (e.g., Non-linear energy, 95(th) percentile of Spectral Edge Frequency) for developing automated systems for quantifying muscle activity during human sleep. CONCLUSIONS: The EMG signals recorded from surface electrodes during sleep can be processed with techniques that reflect the visually based analyses of the human scorer but also offer potential for discerning far more subtle effects, Future studies will explore both the clinical utility of these techniques and their relative susceptibility to and/or independence from signal artifacts.
Effective transverse relaxation rate (T(2)*)-weighted echo-planar imaging (EPI) is extensively used for functional magnetic resonance imaging (fMRI), because of its high speed and good sensitivity to the blood oxygenation level-dependent (BOLD) signal. Nevertheless, its use is limited in areas with severe static magnetic field inhomogeneities that cause frequency shifts and T(2)* relaxation-related distortions of the MR signal along the time-domain (k-space) trajectory, resulting in disperse time-domain signals and generating susceptibility-induced signal losses. Echo planar images are commonly smoothed with k-space spatial low-pass filters to improve the signal-to-noise ratio (SNR) and reduce reconstruction artifacts. Here, we show that when such filters are applied to the dispersed echo-signals (not perfectly centered in k-space), part of the image information from the object is removed, thereby enhancing signal-loss artifacts in the images. To avoid this artifact, the dispersed echo signal has to be refocused before k-space filtering.
This paper presents a novel fully automatic food intake detection methodology, an important step toward objective monitoring of ingestive behavior. The aim of such monitoring is to improve our understanding of eating behaviors associated with obesity and eating disorders. The proposed methodology consists of two stages. First, acoustic detection of swallowing instances based on mel-scale Fourier spectrum features and classification using support vector machines is performed. Principal component analysis and a smoothing algorithm are used to improve swallowing detection accuracy. Second, the frequency of swallowing is used as a predictor for detection of food intake episodes. The proposed methodology was tested on data collected from 12 subjects with various degrees of adiposity. Average accuracies of >80% and >75% were obtained for intra-subject and inter-subject models correspondingly with a temporal resolution of 30s. Results obtained on 44.1 hours of data with a total of 7305 swallows show that detection accuracies are comparable for obese and lean subjects. They also suggest feasibility of food intake detection based on swallowing sounds and potential of the proposed methodology for automatic monitoring of ingestive behavior. Based on a wearable non-invasive acoustic sensor the proposed methodology may potentially be used in free-living conditions.
A small, hermetic, wirelessy-controlled retinal prosthesis has been developed for pre-clinical studies in Yucatan minipigs. The device was attached conformally to the outside of the eye in the socket and received both power and data wirelessly from external sources. Based on the received image data, the prosthesis drove a subretinal thin-film polyimide array of sputtered iridium oxide stimulating electrodes. The implanted device included a hermetic titanium case containing a 15-channel stimulator and receiver chip and discrete circuit components. Feedthroughs in the hermetic case connected the chip to secondary power- and data-receiving coils, which coupled to corresponding external power and data coils driven by power amplifiers. Power was delivered by a 125 KHz carrier, and data were delivered by amplitude shift keying of a 15.5 MHz carrier at 100 Kbps. Stimulation pulse strength, duration and frequency were programmed wirelessly from an external computer system. The final assembly was tested in vitro in physiological saline and in vivo in two minipigs for up to five and a half months by measuring stimulus artifacts generated by the implant's current drivers.
In this contribution we investigate the applicability of different methods from the field of independent component analysis (ICA) for the examination of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data from breast cancer research. DCE-MRI has evolved in recent years as a powerful complement to X-ray based mammography for breast cancer diagnosis and monitoring. In DCE-MRI the time related development of the signal intensity after the administration of a contrast agent can provide valuable information about tissue states and characteristics. To this end, techniques related to ICA, offer promising options for data integration and feature extraction at voxel level. In order to evaluate the applicability of ICA, topographic ICA and tree-dependent component analysis (TCA), these methods are applied to twelve clinical cases from breast cancer research with a histopathologically confirmed diagnosis. For ICA these experiments are complemented by a reliability analysis of the estimated components. The outcome of all algorithms is quantitatively evaluated by means of receiver operating characteristics (ROC) statistics whereas the results for specific data sets are discussed exemplarily in terms of reification, score-plots and score images.
Future generations of upper limb prosthesis will have dexterous hand with individual fingers and will be controlled directly by neural signals. Neurons from the primary motor (M1) cortex code for finger movements and provide the source for neural control of dexterous prosthesis. Each neuron's activation can be quantified by the change in firing rate before and after finger movement, and the quantified value is then represented by the neural activity over each trial for the intended movement. Since this neural activity varies with the intended movement, we define the relative importance of each neuron independent of specific intended movements. The relative importance of each neuron is determined by the inter-movement variance of the neural activities for respective intended movements. Neurons are ranked by the relative importance and then a subpopulation of rank-ordered neurons is selected for the neural decoding. The use of the proposed neuron selection method in individual finger movements improved decoding accuracy by 21.5% in the case of decoding with only 5 neurons and by 9.2% in the case of decoding with only 10 neurons. With only 15 highly-ranked neurons, a decoding accuracy of 99.5% was achieved. The performance improvement is still maintained when combined movements of two fingers were included though the decoding accuracy fell to 95.7%. Since the proposed neuron selection method can achieve the targeting accuracy of decoding algorithms with less number of input neurons, it can be significant for developing brain-machine interfaces for direct neural control of hand prostheses.
Physiological changes in dynamic PET images can be quantitatively estimated by kinetic modeling technique. The process of PET quantification usually requires an input function in the form of a plasma-time activity curve (PTAC), which is generally obtained by invasive arterial blood sampling. However, invasive arterial blood sampling poses many challenges especially for small animal studies, due to the subjects' limited blood volume and small blood vessels. A simple non-invasive quantification method based on Patlak graphical analysis (PGA) has been recently proposed to use a reference region to derive the relative influx rate for a target region without invasive blood sampling, and evaluated by using the simulation data of human brain FDG-PET studies. In this study, the non-invasive Patlak (nPGA) method was extended to whole-body dynamic small animal FDG-PET studies. The performance of nPGA was systematically investigated by using experimental mouse studies and computer simulations. The mouse studies showed high linearity of relative influx rates between the nPGA and PGA for most pairs of reference and target regions, when an appropriate underlying kinetic model was used. The simulation results demonstrated that the accuracy of the nPGA method was comparable to that of the PGA method, with a higher reliability for most pairs of reference and target regions. The results proved that the nPGA method could provide a non-invasive and indirect way for quantifying the FDG kinetics of tumor in small animal studies.
Electrical muscle stimulation demonstrates potential for preventing muscle atrophy and for restoring functional movement after spinal cord injury (SCI). Control systems used to optimize delivery of electrical stimulation protocols depend upon the algorithms generated using computational models of paralyzed muscle force output. The Hill-Huxley-type model, while being highly accurate, is also very complex, making it difficult for real-time implementation. In this paper, we propose a Wiener-Hammerstein system to model the paralyzed skeletal muscle under electrical stimulus conditions. The proposed model has substantial advantages in identification algorithm analysis and implementation including computational complexity and convergence, which enable it to be used in real-time model implementation. Experimental data sets from the soleus muscles of fourteen subjects with SCI were collected and tested. The simulation results show that the proposed model outperforms the Hill-Huxley-type model not only in peak force prediction, but also in fitting performance for force output of each individual stimulation train.
A novel three-stage algorithm for detection of fixations and smooth pursuit movements in high-speed eye-tracking data is proposed. In the first stage, a segmentation based on the directionality of the data is performed. In the second stage, four spatial features are computed from the data in each segment. Finally, data are classified into fixations and smooth pursuit movements based on a combination of the spatial features and the properties of neighboring segments. The algorithm is evaluated under the assumption that the intersaccadic intervals represent fixations in data recorded when viewing images, and mainly smooth pursuit movements in data recorded when viewing moving dots. The results show that the algorithm is able to detect 94.3% of the fixations for image stimuli, compared to a previous algorithm with 80.4% detected fixations. For moving dot stimuli the proposed algorithm detects 86.7% smooth pursuit movements compared to 68.0% for the previous algorithm.
We present objective measurements of source-to-ear responses obtained in a previously established experimental paradigm of human echolocation. We identify and analyze the binaural localization cues encoded in those responses and we discuss their significance with respect to the previously reported performance in this specific experimental paradigm. The cues encoded in both the echo (lagging) and the direct transmission and echo (leading and lagging) parts of the responses are examined and their significance in view of the "precedence effect" is discussed. The variation and salience of the binaural cues pertaining to extensions of the previous experimental paradigm are examined and interpreted. This work allows us to formulate more detailed hypotheses and to design more informative subjective listening tests in order to further investigate the viability of using the acquired sensory modality of human echolocation in real-world applications.
We describe a novel approach for the design of
near-perfect-reconstruction mixed FIR-/allpass-based quadrature mirror
filter banks. The design is carried out in the polyphase domain, where
FIR filters, obtained via simple closed-form expressions, are employed
for compensating the nonlinear phase introduced by the allpass filters.
Starting from a generalized two-band structure, we introduce three
different types of analysis-synthesis banks based on the same design
principle. In all systems the remaining linear and phase distortions can
be made arbitrarily small at the expense of additional system delay.
Simultaneously, aliasing can be minimized, or completely canceled if
further delay can be tolerated
Due to the non-stationary, multicomponent nature of biomedical signals, the use of time-frequency analysis can be inevitable for these signals. The choice of the proper time-frequency distribution (TFD) that can reveal the exact multicomponent structure of biological signals is vital in many applications, including the diagnosis of medical abnormalities. In this paper, the instantaneous frequency (IF) estimation using four well-known TFDs is applied for analyzing biological signals. These TFDs are: the Wigner–Ville distribution (WVD), the Choi–Williams distribution (CWD), the Exponential T-distribution (ETD) and the Hyperbolic T-distribution (HTD). Their performance over normal and abnormal biological signals as well as over multicomponent frequency modulation (FM) signals in additive Gaussian noise was compared. Moreover, the feasibility of utilizing the wavelet transform (WT) in IF estimation is also studied. The biological signals considered in this work are the surface electromyogram (SEMG) with the presence of ECG noise and abnormal cardiac signals. The abnormal cardiac signals were taken from a patient with malignant ventricular arrhythmia, and a patient with supraventricular arrhythmia. Simulation results showed that the HTD has a superior performance, in terms of resolution and cross-terms reduction, as compared to other time-frequency distributions.
Decomposition of acceleration was investigated as an alternative to commonly used direct spectral analysis of measured acceleration or angular velocity for tremor quantification. An orientation estimation algorithm was devised to decompose the measured acceleration into the gravitational artifact and the inertial acceleration caused by sensor movement in an inertial reference frame. Resulting signals, beside the measured acceleration and angular velocity, were used to assess tremor amplitude and frequency by spectral peak detection. The algorithm was tested on experimental data from a clinical study including patients with essential tremor. The testing comprised of the classification of measurements to come from a patient or a healthy control and of the regression of the visual assessment of tremor amplitude. Small improvements in performance measures were achieved by using the decomposed acceleration. The regression accuracy was comparable to the accuracy achieved in other works. The influence of sensor calibration and connections of results to an analytic approach were analyzed briefly.
An adaptive approach is presented to investigate, on a beat-to-beat basis, the response to heart rate variations of the QT interval and the T wave amplitude (Ta). The relationship between each repolarization index and the RR interval is modeled using a time-variant system composed of a linear filter followed by a memoryless nonlinearity approximated by a Taylor expansion. The linear portion describes the influence of previous RR intervals on the repolarization index and the nonlinear portion expresses how the index evolves as a function of the averaged RR measurement at the output of the linear filter. For the identification of the unknown system, two procedures that simultaneously estimate all of the system parameters are proposed. The first procedure converts the total input–output relationship into one being linear in its parameters and uses a Kalman-based technique to estimate these parameters. The second procedure uses the Unscented Kalman Filter to solve the nonlinear identification directly. Those procedures were tested on artificially generated data and showed very good agreement between estimated and theoretical parameter values. The application to electrocardiographic recordings showed that both repolarization indices lag behind the RR interval, being the effect more noticeable for the QT interval and more strongly manifested in episodes of sustained changes in heart rate, with QT lags after large RR variations of nearly 1 min in mean over recordings. The time variant relationship was found to be adequately modeled by a first-order Taylor expansion, while the relationship was better modeled using a second-order nonlinearity.
In this paper we propose a new technique that adaptively extracts subject specific motor imagery related EEG patterns in the space–time–frequency plane for single trial classification. The proposed approach requires no prior knowledge of reactive frequency bands, their temporal behavior or cortical locations. For a given electrode array, it finds all these parameters by constructing electrode adaptive time–frequency segmentations that are optimized for discrimination. This is accomplished first by segmenting the EEG along the time axis with Local Cosine Packets. Next the most discriminant frequency subbands are selected in each time segment with a frequency axis clustering algorithm to achieve time and frequency band adaptation individually. Finally the subject adapted features are sorted according to their discrimination power to reduce dimensionality and the top subset is used for final classification. We provide experimental results for 5 subjects of the BCI competition 2005 dataset IVa to show the superior performance of the proposed method. In particular, we demonstrate that by using a linear support vector machine as a classifier, the classification accuracy of the proposed algorithm varied between 90.5% and 99.7% and the average classification accuracy was 96%.
Tremor is the root cause for human imprecision during microsurgery. Accurate filtering of physiological tremor is extremely important for compensation in robotics assisted microsurgical instruments/procedures. A study on several surgeons tremor is conducted and the characteristics of the tremor are analyzed. A double adaptive bandlimited multiple Fourier linear combiner is designed to estimate the modulated signals with multiple frequency components for filtering and compensation of tremor in real-time. A separation procedure to separate the intended motion/drift from the tremor portion is developed. The proposed methods are compared with the existing weighted-frequency Fourier linear combiner (WFLC) algorithm on the tremor data of surgeons/subjects. Critical validation of the algorithm is performed, experiments are conducted for 1-degree of freedom (DOF) cancellation of tremor. Our experiments showed that our newly developed algorithm has a tremor compensation of at least 65% compared to 46% for the WFLC algorithm.
The relative slow scanning speed of a galvanometer commonly used in a confocal laser scanning microscopy system can dramatically limit the system performance in scanning speed and image quality, if the data collection is simply synchronized with the galvanometric scanning. Several algorithms for the optimization of the galvanometric CLSM system performance are discussed in this work, with various hardware controlling techniques for the image distortion correction such as pixel delay and interlace line switching; increasing signal-to-noise ratio with data binning; or enhancing the imaging speed with region of interest imaging. Moreover, the pixel number can be effectively increased with Acquire-On-Fly scan, which can be used for the imaging of a large field-of-view with a high resolution.
In this paper, a methodology for using pulse photoplethysmography (PPG) signal to automatically detect sleep apnea is proposed. The hypothesis is that decreases in the amplitude fluctuations of PPG (DAP), are originated by discharges of the sympathetic branch of autonomic nervous system, related to arousals caused by apnea. To test this hypothesis, an automatic system to detect DAP events is proposed.The detector was evaluated using real signals, and tested on a clinical experiment. The overall data set used in the studies includes the polysomnographic records of 26 children which were further subdivided depending on the evaluation of interest. For real signals, the sensitivity and positive predictive value of the DAP detector were 76% and 73%, respectively. An apnea detector has been developed to analyze the relationship between apneas and DAP, indicating that DAP events increase by about 15% when an apnea occurs compared to when apneas do not occur. A clinical study evaluating the diagnostic power of DAP in sleep apnea in children was carried out. The DAP per hour ratio rDAP was statistically significant (p=0.033) in classifying children as either normal rDAP=13.5±6.35 (mean ± S.D.) or pathologic rDAP=21.1±8.93.These results indicate a correlation between apneic events and DAP events, which suggests that DAP events could provide relevant information in sleep studies. Therefore, PPG signals might be useful in the diagnosis of OSAS.