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.
Factorial phase analysis (FaPI) represents an alternative method to Fourier phase analysis (FoPI) in the evaluation and detection of abnormalities on cardiac contraction patterns, but it has limitations in representing the sequence in abnormal contraction patterns. In this work we propose a modified factorial phase image (FaPIm) that incorporates more complete information regarding the ventricular contraction sequence. In particular, we analyze and evaluate the contribution of the third eigenimage, in the presence of ventricular dyssynchrony, which has not been sufficiently explored in the literature. We have validated the proposed FaPIm using two Equilibrium Radionuclide Angiography (ERNA) sets of images obtained with a dynamic cardiac phantom and with a numerically simulated phantom. Also, we have tested the proposed representation for a control group of 23 normal subjects and for a sample of 15 patients with Complete Left Bundle Branch Block (LBBB). Whereas FoPI allows us to obtain an image that synthesizes ventricular contraction with the smallest dispersion around the mean values, FaPI and FaPIm show that external areas surrounding ventricular cavities present more dephasing than the rest of the ventricular region and contain more detailed information about the progression of contraction. Also, in the presence of an abnormal contraction pattern, the magnitude of the third eigenvalue was greater than the corresponding eigenvalue obtained for normal simulations. The dispersion plots obtained for a normal contraction pattern show that left ventricle (LV) and right ventricle (RV) information overlap. Therefore, when there is a dyssynchrony between LV and RV contraction it becomes necessary to incorporate the information corresponding to the third factor to achieve a clear separation between regions.
The objective of this study was to investigate the surface electromyographic signals using moving approximate entropy from 20 healthy participants’ wrist muscles (flexor carpi ulnaris and flexor carpi radialis). The participants were required to voluntary performed wrist flexion/extension, co-contraction and isometric contraction. A moving data window of 200 values was applied to the data and a moving approximate entropy series was obtained from the analysis. The results demonstrate that there are distinct drops of the approximate entropy values at the start and end of a contraction, and high (less regularity) approximate entropy in the middle. Mean values of approximate entropy of 0.54 and 0.55 were found for the start of a contraction compared to 0.79 and 0.77 during the middle, for the flexor and extensor, respectively. At the end, there are values of 0.46 and 0.5, respectively.
Cervical spinal cord injury (SCI) leads to tetraplegia, with paralysis and loss of sensation in the upper and lower limbs. The associated sedentary lifestyle results in an increased risk of cardiovascular disease. To address this, we require the design of exercise modalities aimed specifically at tetraplegia and methods to assess their efficacy.This paper describes methods for arm-crank ergometry (ACE) assisted by Functional Electrical Stimulation (FES) applied to the biceps and triceps. The instrumented ergometer enables work-rate control during exercise, implemented here for incremental exercise testing during FES-ACE. Detailed protocols for the tests are given.Experimental data collected during exercise tests with tetraplegic volunteers are provided to illustrate the feasibility of the proposed approach to testing and data analysis. Incremental tests enabled calculation of peak power output and peak oxygen uptake.We propose that the high-precision exercise testing protocols described here are appropriate to assess the efficacy of the novel exercise modality, FES-ACE, in tetraplegia.
We report the design of and results obtained by using a field programmable gate array (FPGA) to digitally process optical Doppler tomography signals. The processor fits into the analog signal path in an existing optical coherence tomography setup. We demonstrate both Doppler frequency and envelope extraction using the Hilbert transform, all in a single FPGA. An FPGA implementation has certain advantages over general purpose digital signal processor (DSP) due to the fact that the processing elements operate in parallel as opposed to the DSP, which is primarily a sequential processor.
High central arterial blood pressure can be sustained by the capacity of living arteries to respond to hemodynamic stimuli by changing their structural and/or functional characteristics. These adaptations are considered to occur in a time-dependency, in which different patterns of vascular geometry are identified at all stages. This paper proposes a three-section transmission-line model of the brachial-radial arterial segment and a rational procedure to analyze its transfer function that can be used to interpret the longitudinal remodeling process of medium-sized arteries. The three sections of the model correspond to different arterial segments of the forearm. The model processed pressure signals collected noninvasively from normotensive and hypertensive volunteers at brachial and radial arteries. Aiming to explain possible hypertrophic inward remodeling, geometrical model parameters obtained from normotensive individuals were modified in order to generate high-pressure pulses observed in the hypertensive subjects. The resulting transfer functions for the hypertrophy adaptation exhibit properties related to the pathophysiology of the remodeling process, mainly the reduced amplification of the higher harmonics of the pulse waveform. The results suggest the model can be used to assess noninvasively the hypertension-induced adaptations related to geometrical characteristics of the medium-size arteries.
Arterial blood pressure waveforms contain rich pathophysiological information; hence receive much attention in cardiovascular health monitoring. To assist computerized analysis, an automatic delineator was proposed for the fiducial points of arterial blood pressure waveforms, namely their onsets, systolic peaks and dicrotic notches. The presented delineator characterizes arterial blood pressure waveforms in a beat-by-beat manner. It firstly seeks the pairs of inflection and zero-crossing points, and then utilizes combinatorial amplitude and interval criteria to select the onset and systolic peak. Once a new beat is settled, the delineator seeks the derivative backward to locate the dicrotic notch in the preceding beat. In a nutshell, the delineator is based on the combinatorial analysis of arterial blood pressure waveforms and their derivatives. Three open databases, with an additional subset database, were utilized for delineator validation and performance evaluation. In terms of beat detection, the delineator achieved an average error rate 1.14%, sensitivity 99.43% and positive predictivity 99.45%. As to dicrotic notch detection, it performed well with an error rate 6.83%, sensitivity 96.53% and positive predictivity 96.64%.
We present a new non-rigid registration algorithm estimating the displacement field generated by articulated bodies. Indeed the bony structures between different patient images may rigidly move while other tissues may deform in a more complex way. Our algorithm tracks the displacement induced in the column by a movement of the patient between two acquisitions. The volumetric deformation field in the whole body is then inferred from those displacements using a linear elastic biomechanical finite element model. We demonstrate in this paper that this method provides accurate results on 3D sets of computed tomography (CT), MR and positron emission tomography (PET) images and that the results of the registration algorithm show significant decreases in the mean, min and max errors.
ST segment changes provide a sensitive marker in the diagnosis of myocardial ischemia in Holter recordings. However, not only do the mechanisms of ischemia result in ST segment deviation, but also heart rate related episodes, body position changes or conduction changes among others, which are considered artifactual events when ischemia is the target. In order to distinguish between them, the very similar signatures of ST modifications has led us to look for other ECG indices such as heart rate-based indices, correlation between the absolute ST segment deviation and heart rate series, the interval between the Tapex and the Tend, T wave amplitude, the signal-to-noise ratio and changes in the upward/downward slopes of the QRS complex. A discrimination analysis between the three types of events: ischemia, heart rate related episodes and sudden step ST changes (body position changes and conduction changes) has been performed on the Long-Term ST Database, reaching an accuracy of 82.3%. If we focus on distinguishing between different ST signatures, transient episodes (ischemic and heart rate related) and sudden step ST changes, it results in a sensitivity of 76.8% and a specificity of 98.3%. When classifying ischemia from heart rate related episodes, both with a very similar ST level pattern, a sensitivity of 84.5% and a specificity of 86.6% are reached. Finally, for separating ischemia from any other ST event, a sensitivity of 74.2% and a specificity of 93.2% are obtained.
Treadmill training is used for gait rehabilitation in various neurological conditions. Robot-assisted treadmill training automates repetition of the gait cycle and can reduce the load on therapists. Here we investigate the use of robot-assisted treadmill technology in cardiopulmonary rehabilitation and assessment.Using a new approach to exercise work rate estimation and volitional control, we propose cardiopulmonary assessment protocols for robot-assisted gait exercise, designed for estimation of cardiopulmonary performance parameters. Feasibility was explored in three subjects with incomplete spinal cord injury using the Lokomat system.Estimation and visual feedback of exercise work rate allowed all subjects to accurately follow specified work rate profiles in real time by means of volitional control. We were able to estimate the main cardiopulmonary performance parameters from constant work rate and incremental tests. “Passive” walking elicited a substantial metabolic response: on average, oxygen uptake () was a factor of 1.8 higher than during rest. The magnitude of peak above rest, obtained from incremental tests, was a factor of 4–6 higher than the increment in for passive walking, thus emphasising the importance of the subjects’ active participation in the exercise.Visual feedback and volitional control of estimated exercise work rate facilitates the imposition of work rate profiles for estimation of cardiopulmonary performance parameters in robot-assisted gait. This new approach could be used to guide a patient’s training regime during a cardiopulmonary rehabilitation programme, and for periodic assessment of cardiopulmonary status.
BackgroundRobotics-assisted treadmill exercise (RATE) is a new mode of exercise available to people with an incomplete spinal cord injury (SCI) that allows them to utilise their lower limb muscles during stepping. Pilot data suggest that RATE elicits a non-linear oxygen uptake () response corresponding to a linear increase in work rate. However, a linear response during an incremental exercise test (IET) may be important to enable accurate estimation of key cardiopulmonary performance parameters.AimThis study aims to characterise the linearity of the response elicited by a linearly increasing work rate during robotics-assisted treadmill exercise in subjects with incomplete SCI.MethodsUtilising the Lokomat system, 10 subjects each performed two IETs on a robotics-assisted treadmill to the limit of their tolerance. By employing work rate estimation algorithms, subjects were asked to use cognitive feedback and volitional control of their contribution to the exercise to follow a linearly increasing target work rate that was displayed on screen. Pulmonary gas exchange and ventilatory measurements (including ) were continuously measured throughout the exercise using a breath-by-breath respiratory monitoring system. Linear and 3rd-order non-linear approximations with comparable R2 values were computed for each subject’s response to the linear increasing work rate.ResultsR2 values for the non-linear approximations were 9% higher on average (p=0.015) than the corresponding R2 values for the linear approximations.ConclusionThe response elicited by a linearly increasing work rate during robotics-assisted treadmill exercise in those with incomplete SCI is non-linear. To ensure the intensity of exercise increases linearly, a more appropriate IET may be implemented by employing feedback control of to track a linear target.
Recently, the research efforts in the context of electrocardiographical recording during atrial fibrillation (AF) has been directed to broaden the understandings on the electrophysiological and structural remodelling occurring during the arrhythmia and on characterizing the different types of AF. Following this line, both surface ECG and endocardial electrograms have been thoroughly studied and a series of linear and non-linear parameters were computed either directly on the electrograms or on the derived activation series.In this paper, we reviewed some signal processing methods used to characterize surface ECG and endocardial electrograms during AF, focusing on spectral and non-linear analysis. In particular, parametric and non-parametric methods for spectral analysis of the residual ECG, i.e. atrial waves obtained from surface ECG after removing ventricular activity, and endocardial recordings are described. The different purposes of spectral analysis (exploring autonomic functions, analysis of spontaneous AF behaviour and predicting therapeutic effects) are illustrated with some examples. In addition, we described some more recent non-linear methods applied to AF, assessing the organization of atrial signals as well as ventricular response in AF. In particular, methods derived from embedding time series and based on entropy computation are illustrated and exemplified.
Skin cancer is the most common form of cancer and represents 50% of all new cancers detected each year. However, if detected at an early stage, simple and economic treatments can cure most skin cancers. Accurate skin lesion segmentation is critical in automated skin cancer early detection and diagnosis systems. In this paper, we present an evolution strategy (ES) based segmentation algorithm to identify the lesion area within an ellipse. The method is applied to a set of 51 crosspolarization and 60 transillumination images segmented manually by a dermatologist, which are used as ground truth. Unlike most segmentation procedures, the proposed ES-based method can detect the lesion automatically without setting parameters and initial values by trial and error. Compared to results from our previous work, the ES-based method gives comparable accuracy for easily segmented images and much better results for images with either higher noise level, less prominent edges, or very small size lesions.
One way for breast cancer diagnosis is provided by taking radiographic (X-ray) images (termed mammograms) for suspect patients, images further used by physicians to identify potential abnormal areas thorough visual inspection. When digital mammograms are available, computer-aided based diagnostic may help the physician in having a more accurate decision. This implies automatic abnormal areas detection using segmentation, followed by tumor classification. This work aims at describing an approach to deal with the classification of digital mammograms. Patches around tumors are manually extracted to segment the abnormal areas from the remaining of the image, considered as background. The mammogram images are filtered using Gabor wavelets and directional features are extracted at different orientation and frequencies. Principal Component Analysis is employed to reduce the dimension of filtered and unfiltered high-dimensional data. Proximal Support Vector Machines are used to final classify the data. Superior mammogram image classification performance is attained when Gabor features are extracted instead of using original mammogram images. The robustness of Gabor features for digital mammogram images distorted by Poisson noise with different intensity levels is also addressed.
This paper describes some methodological concerns to be considered when designing systems for automatic detection of voice pathology, in order to enable comparisons to be made with previous or future experiments.The proposed methodology is built around the Massachusetts Eye & Ear Infirmary (MEEI) Voice Disorders Database, which to the present date is the only commercially available one. Discussion about key points on this database is included.Any experiment should have a cross-validation strategy, and results should supply, along with the final confusion matrix, confidence intervals for all measures. Detector performance curves such as detector error trade off (DET) and receiver operating characteristic (ROC) plots are also considered.An example of the methodology is provided, with an experiment based on short-term parameters and multi-layer perceptrons.
Automatic analysis of biomedical time series such as electroencephalogram
(EEG) and electrocardiographic (ECG) signals has attracted great interest in
the community of biomedical engineering due to its important applications in
medicine. In this work, a simple yet effective bag-of-words representation that
is able to capture both local and global structure similarity information is
proposed for biomedical time series representation. In particular, similar to
the bag-of-words model used in text document domain, the proposed method treats
a time series as a text document and extracts local segments from the time
series as words. The biomedical time series is then represented as a histogram
of codewords, each entry of which is the count of a codeword appeared in the
time series. Although the temporal order of the local segments is ignored, the
bag-of-words representation is able to capture high-level structural
information because both local and global structural information are well
utilized. The performance of the bag-of-words model is validated on three
datasets extracted from real EEG and ECG signals. The experimental results
demonstrate that the proposed method is not only insensitive to parameters of
the bag-of-words model such as local segment length and codebook size, but also
robust to noise.
A novel transcellular micro-impedance biosensor, referred to as the electric cell-substrate impedance sensor or ECIS, has become increasingly applied to the study and quantification of endothelial cell physiology. In principle, frequency dependent impedance measurements obtained from this sensor can be used to estimate the cell–cell and cell–matrix impedance components of endothelial cell barrier function based on simple geometric models. Few studies, however, have examined the numerical optimization of these barrier function parameters and established their error bounds. This study, therefore, illustrates the implementation of a multi-response Levenberg–Marquardt algorithm that includes instrumental noise estimates and applies it to frequency dependent porcine pulmonary artery endothelial cell impedance measurements. The stability of cell–cell, cell–matrix and membrane impedance parameter estimates based on this approach is carefully examined, and several forms of parameter instability and refinement illustrated. Including frequency dependent noise variance estimates in the numerical optimization reduced the parameter value dependence on the frequency range of measured impedances. The increased stability provided by a multi-response non-linear fit over one-dimensional algorithms indicated that both real and imaginary data should be used in the parameter optimization. Error estimates based on single fits and Monte Carlo simulations showed that the model barrier parameters were often highly correlated with each other. Independently resolving the different parameters can, therefore, present a challenge to the experimentalist and demand the use of non-linear multivariate statistical methods when comparing different sets of parameters.
Several pathologies related to the atrial electrical activity can be detected in the electrocardiogram P-wave. A protocol for analyzing P-wave morphology changes has been developed in this article. By using this protocol a study on the beat-to-beat P-wave morphology changes of 89 ECG signals is performed. An algorithm based on the embedding space techniques has been used to extract the P-wave information of the ECG. The P-waves obtained in several of these ECGs exhibit significant alternate morphology changes. The morphologies have been classified by using the K-means clustering algorithm. The mechanism behind the P-wave morphology change process and its possible pathophysiological importance remains to be clarified.
This paper presents an asynchronous brain switch using one Laplacian electroencephalographic (EEG) derivation. The brain switch is operated through foot motor imagery (MI) and is based on the classification of event-related desynchronization (ERD) during a motor task or event-related synchronization (ERS) after the termination of the task (also known as the beta rebound). The methods described in this work are suitable for operating a brain–computer interface (BCI) as an attractive control alternative for healthy users. A general description of ERD/ERS is obtained with several band power features and a rigid paradigm timing. Two support vector machines (SVMs) are trained in a novel fashion by using the patterns from motor execution (ME) and a priori information about the significance of ERD/ERS patterns. A maximum true positive rate (TPR) of 0.92 and a minimum of 0.43 was achieved (in 8 out of 9 subjects) during training of the classifiers; with a mean false positive rate (FPR) of 0.09 ±0.05.A simulation of an asynchronous BCI using MI data and the classifiers trained with ME data achieved a maximum TPR of 0.88, a minimum of 0.50 (in 6 out of 9 subjects) and an average FPR of 0.09 ±0.04. This work presents a step forward towards an easy-to-set-up and easy-to-use asynchronous BCI system for healthy users.
In this paper, we propose a novel method using wavelets as input to neural network self-organizing maps and support vector machine for classification of magnetic resonance (MR) images of the human brain. The proposed method classifies MR brain images as either normal or abnormal. We have tested the proposed approach using a dataset of 52 MR brain images. Good classification percentage of more than 94% was achieved using the neural network self-organizing maps (SOM) and 98% from support vector machine. We observed that the classification rate is high for a support vector machine classifier compared to self-organizing map-based approach.
Heart and respiration rate measurement using Doppler radar is a non-contact
and non-obstructive way for remote thorough-clothing monitoring of vital signs.
The modulated back-scattered radar signal in the presence of high noise and
interference is non-stationary with hidden periodicities, which cannot be
detected by ordinary Fourier analysis. In this paper we propose a
cyclostationary approach for such signals and show that by using non-linear
transformation and then Fourier analysis of the radar signal, the hidden
periodicities can be accurately obtained. Numerical results show that the vital
signs can be extracted as cyclic frequencies, independent of SNR and without
any filtering or phase unwrapping.
Normal male voicing is defined, and voicing recovery after radiotherapy for larynx cancer quantified, using spectral domain complexity analysis of electro-glottogram conductance variations measured across the larynx during vowel phonation. These variations directly correlate with vocal fold vibrations that drive voice production. Approximate entropy is shown to concisely quantify the collective spectral pattern of the sustained impedance waveform after normalisation with respect to the varying fundamental frequency and power. It reveals a double banded reference standard in normal males. Forty-eight male larynx cancer patients were studied in parallel with an unrestricted perceptual analysis before and 1 year after radiotherapy. Two-thirds of patients had spectral approximate entropy values close to normal approximate entropy reference standards after 1 year. A quarter of patients showed reduced approximate entropy, predominantly in the most aberrant perceptual categories. Collective spectral pattern complexity analysis of vowel phonation has the potential to be a reliable, single parameter measure of voicing quality in these cancer patients.
AimPeople with complete lower-limb paralysis resulting from spinal cord injury (SCI) can perform cycle ergometry by means of functional electrical stimulation. Here, we propose and evaluate new exercise testing methods for estimation of cardiopulmonary performance parameters during this form of exercise.MethodsWe utilised a customised ergometer incorporating feedback control of stimulated exercise workrate and cycling cadence. This allowed the imposition of arbitrary workrate profiles with high precision with the potential for improved sensitivity in exercise testing. New incremental exercise test (IET) and step exercise test (SET) protocols for determination of peak and steady-state performance parameters were assessed.ResultsThe IET protocol allowed reliable determination of the ventilatory threshold, peak workrate and oxygen uptake-workrate relationship, but gave unrepresentative peak oxygen uptake values and highly variable measures of oxygen uptake kinetics. The SET protocol gave reliable estimation of steady-state oxygen uptake and metabolic efficiency of constant load exercise, but high variability in the estimation of oxygen uptake kinetics.ConclusionThe feedback-controlled testbed and the new IET and SET protocols have the potential for estimation of cardiopulmonary performance parameters with improved sensitivity during stimulated cycle ergometry in subjects with SCI.
Tight glycemic control has been shown to reduce mortality by 29–45% in critical care. Targeted glycemic control in critical care patients can be achieved by frequent fitting and prediction of a patient's modelled insulin sensitivity index, SI. This parameter can vary significantly in the critically ill due to the evolution of their condition and drug therapy.A three-dimensional stochastic model of SI variability is constructed using 18 long-term retrospective critical care patients’ data. Given SI for an hour, the stochastic model returns the probability distribution of SI for the next hour. Consequently, the resulting glycemic distribution 1 h following a known insulin and/or nutrition intervention can be derived. Knowledge of this distribution enables more accurate predictions for glycemic control with pre-determined likelihood based on confidence intervals.Clinical control data from eight independent critical care glycemic control trials were re-evaluated using the stochastic model. The stochastic model successfully captures the identified SI variation trend, accounting for 84% of measurements over time within the 0.90 confidence band, and 45% with a 0.50 confidence. Incorporating the stochastic model into the numerical glucose–insulin dynamics model, a virtual cohort was generated, imitating typical glucose–insulin dynamics in a critically ill population. Control trial simulations on this virtual cohort showed that the 0.90 confidence intervals cover 88% of measurements, and the 0.5 confidence intervals cover 46%. These results indicate that the stochastic model provides first order estimate of insulin sensitivity, SI, variation and resulting glycemic variation in critical care.
Hyperglycaemia is prevalent in critical care, as patients experience stress-induced hyperglycaemia, even with no history of diabetes. Hyperglycaemia in critical care is not largely benign, as once thought, and it has a deleterious effect on outcome. Recent studies have shown that tight glucose regulation to average levels from 6.1–7.75 mmol/L can reduce mortality 25–43%, while also significantly reducing other negative clinical outcomes.However, clinical results are highly variable and there is little agreement on what levels of performance can be achieved and how to achieve them. A typical clinical solution is to use ad-hoc protocols based primarily on experience, where large amounts of insulin, up to 50 U/h, are titrated against glucose measurements variably taken every 1–4 h. When combined with the unpredictable and sudden metabolic changes that characterise this aspect of critical illness and/or clinical changes in nutritional support, this approach results in highly variable blood glucose levels. The overall result is sustained periods of hyper- or hypo- glycaemia, characterised by oscillations between these states, which can adversely affect clinical outcomes and mortality. The situation is exacerbated by exogenous nutritional support regimes with high dextrose content. Hence, there is an emerging, strong need for the more rigorous analysis and methods that model-based control methods bring to this type of problem.This paper reviews the state of the clinical and model-based control systems approach to the problem of managing hyperglycaemia in critical care, emphasising emerging methods and results. The overall goal is to present the fundamental problem and associated science and technologies involved. Thus, it is less a discussion of specific advantageous approaches than a presentation of the different factors that impact the problem and the different approaches taken to address them in the limited clinical engineering research done to date. These discussions are presented in the context of current and emerging clinical studies, both model-based and empirical protocol driven. Analogies to the Type 1 diabetes mellitus control problem are also noted where relevant and significant. Hence, it is more overview than specific analysis, where the overall conclusion is that there are many opportunities and unanswered questions remaining on which model-based control research can have significant clinical impact.
A new method is proposed for detecting fraudulent whiplash claims based on measurements of movement control of the neck. The method is noninvasive and inexpensive. The subjects track a slowly moving object on a computer screen with their head. The deviation between the measured and actual trajectory is quantified and used as input to an ensemble of support vector machine classifiers. The ensemble was trained on a group of 34 subjects with chronic whiplash disorder together with a group of 31 healthy subjects instructed to feign whiplash injury. The sensitivity of the proposed method was 86%, the specificity 84% and the area under curve (AUC) was 0.86. This suggests that the method can be of practical use for evaluating the validity of whiplash claims.