BACKGROUND:Intraoperative neurophysiologic monitoring (IOM) has been used clinically since the 1970s and is a reliable tool for detecting impending neurologic compromise. However, there are mixed data as to whether long-term neurologic outcomes are improved with its use. We investigated whether IOM used in conjunction with image guidance produces different patient outcomes than with image guidance alone. METHODS:We reviewed 163 consecutive cases between January 2015 and December 2018 and compared patients undergoing posterior lumbar instrumentation with image guidance using and not using multimodal IOM. Monitored and unmonitored surgeries were performed by the same surgeons, ruling out variability in intersurgeon technique. Surgical and neurologic complication rates were compared between these 2 cohorts. RESULTS:A total of 163 patients were selected (110 in the nonmonitored cohort vs. 53 in the IOM cohort). Nineteen signal changes were noted. Only 3 of the 19 patients with signal changes had associated neurologic deficits postoperatively (positive predictive value 15.7%). There were 5 neurologic deficits that were observed in the nonmonitored cohort and 8 deficits observed in the monitored cohort. Transient neurologic deficit was significantly higher in the monitored cohort per case (P < 0.0198) and per screw (P < 0.0238); however, there was no difference observed between the 2 cohorts when considering permanent neurologic morbidity per case (P < 0.441) and per screw (P < 0.459). CONCLUSIONS:The addition of IOM to cases using image guidance does not appear to decrease long-term postoperative neurologic morbidity and may have a reduced diagnostic role given availability of intraoperative image-guidance systems.

Our group has developed a novel dry electrode, the skin screw electrode, for EEG measurement. This electrode can be conveniently and rapidly installed on the human scalp requiring no electrolyte application and skin preparation. In this paper, we further evaluate the performance of this electrode by investigating its frequency-dependent electrical impedance at the skin-electrode interface. Comparing with the traditional disc electrode, we found that the two types of electrodes showed very different spectral properties. We also found that the impedance of the screw electrode decreases after coating with gold.

Witricity, a highly efficient wireless power transfer method using mid-range resonant coupling, was reported in recent literature. Based on this method, we present a wireless power transfer scheme using thin film resonant cells for medical applications. The thin film cells, consisting of a tape coil in the exterior layer and conductive strips in the interior layer separated by an insulation layer, are made light and flexible to provide comfort for users during wear. The wireless power transfer scheme presented displays great potential for delivering energy to implantable and worn devices, with range much larger than the current technology for powering implantable devices.

In this paper, we present a wireless power transfer and communication method for biomedical sensors and implants by taking advantage of recently developed witricity technology. New witricity resonator and system designs are described and evaluated by performing both in vitro and in vivo experiments. When compared to the current designs, these new witricity designs demonstrate much improved performances for wireless energy delivery to and data communication with biomedical sensors and implantable devices from outside the human body, providing a higher efficiency and a longer transmission distance.

Wavelet threshold denoising is a powerful method for suppressing noise in signals and images. However, this method often uses a coordinate-wise processing scheme, which ignores the structural properties in the wavelet coefficients. We propose a new wavelet denoising method using sparse representation which is a powerful mathematical tool recently developed. Instead of thresholding wavelet coefficients individually, we minimize the number of non-zero coefficients under certain conditions. The denoised signal is reconstructed by solving an optimization problem. It is shown that the solution to the optimization problem can be obtained uniquely and the estimates of the denoised wavelet coefficients are unbiased, i.e., the statistical means of the estimates are equal to the noise-free wavelet coefficients. It is also shown that at least a local optimal solution to the denoising problem can be found. Our experiments on test data indicate that this new denoising method is effective and efficient for a wide variety of signals including those with low signal-to-noise ratios.

This paper investigates wireless electricity (witricity) and its application to medical sensors and implantable devices. Several coupling scenarios of resonators are analyzed theoretically. In vitro experiments are conducted in open air and through an agar phantom of the human head. An in vivo animal experiment is also carried out. Our studies indicate that witricity is a suitable tool for providing wireless power to a variety of medical sensors and implanted devices.

A novel system consisting of a camera and a light emitting diode (LED) is presented for measuring food portion size. The LED is positioned at a fixed distance besides the camera with its optical axis parallel to the optical axis of the camera. The distance to and oblique angle of the object plane are calculated according to the deformation of the projected spotlight pattern. Experimental results show that satisfactory measurements of food portion size can be obtained with this simple system.

Image-based gait analysis as a means of biometric identification has attracted much research attention. Most of the existing methods focus on human posture identification and tracking. There have been few investigations on the relationship between the carried load by a person and the change of gait characteristics. Nevertheless, this relationship can be very useful in a number of applications, such as studying the postural effects of load on children and adolescence. In this paper, we investigate how to estimate carried weight from a sequence of images of a person walking normally. Observing that human tends to minimize energy expenditure during walking, we compute several angles of body leaning and determine the relationship among the carried weight, the leaning angles, and the location of the center of gravity. This method has been verified successfully by experiments.

Overweight and obesity have become an epidemic in many parts of the world threatening the health of over one billion people. In order to combat this epidemic effectively, it is desirable to develop new methods to monitor individual's food intake and provide quantitative information about the nutrients and calories consumed in people's daily life. We present an electronic photographic approach and associated image processing algorithms to estimate food portion size, which is then utilized to obtain the required information. Our experiments show that our approach is accurate, providing an effective tool for people to track their nutritional and energy intake.

Publisher Summary This chapter describes the technical advances that have occurred that allow the data to be acquired rapidly from multiple data types simultaneously, including modern stimulation, display, and filtering techniques. It focuses on the technology that supports remote viewing of the data and communication between remote neurophysiologists and local neurotechnologists. The chapter reviews that the commonly accepted principal goal of intraoperative monitoring is to prevent morbidity and at a certain level this is true; however, the more fundamental goal of intraoperative monitoring is to provide the surgical team with information that allows them to accomplish the desired operative objective with a surgical strategy as optimal as possible, while having a clear idea of what surgical morbidity is being induced along the way. The chapter discusses that in support of the intraoperative monitoring of these measures, a distributed computer system is developed, NeuroNet, specifically configured to support the considerations. This system provides both off-line and real-time signal processing, and data review capabilities, and it addresses many problems associated with the acquisition, processing, and display of multivariate neurophysiologic data in these complex cases.

This paper presents a numerical approach to prove the existence of inherent bimanual postural synergies while performing actions with two hands. Five subjects were tested in two different tasks. The first task was a well coordinated task where each subject screwed nut and bolt using one or both hands. In the second task, subjects were asked to perform several random postures with both hands. Joint angles were measured during the experiment by a pair of data gloves. Principal component analysis (PCA) was performed over the postures obtained during the tasks. In the first task, the number of postural synergies obtained for both hands together was less than the sum of the number of postural synergies for two hands. This is expected intuitively as first task was well coordinated. In the second task where there is no voluntary coordination involved, the number of postural synergies obtained for both hands together was still less than the sum of the number of postural synergies for two hands. This implies that there are innate bimanual synergies wired biomechanically to help brain in bimanual movements.

Wavelet threshold denoising is a powerful method for suppressing noise in signals and images. However, this method uses a coordinate-wise processing scheme, which ignores the structural properties in the wavelet coefficients. We propose a new denoising method using sparse representation which is a powerful mathematical tool developed only recently. Instead of thresholding wavelet coefficients individually, we minimize the number of coefficients in the sparse representation frame work under certain conditions. The denoised signal is reconstructed by solving an optimization problem. We show that, by using an iterative algorithm, the solution to the optimization problem can be obtained uniquely and the estimates are unbiased, i.e., the statistical means of the estimates are equal to the ideal wavelet coefficients. Our experiments on test signals show that this new denoising method is effective and efficient for a wide variety of signals including those with a low signal-to-noise ratio.

Wearable visual devices have many emerging applications in human health monitoring, such as the study of food intake and physical activities. However, these devices produce large amounts of data which must be processed efficiently and effectively. In this paper, we present a video- based health monitoring system for physical activity studies. We utilize an efficient signal extraction method to process and reduce the effects of noises on field-acquired image data. We further develop a maximum likelihood estimator to estimate the walking speed and classify activity patterns (walking or running) in real-time under both indoor and outdoor environments. We demonstrate the feasibility of the proposed method using three different test video sequences.

This chapter discusses that the objective of performing intraoperative neurophysiological monitoring during procedures involving decompression and instrumentation of the cervical, thoracic, and lumbosacral spine is to detect insults to the central and the peripheral nervous systems, and subsequent prevention of the iatrogenic neurological injury. It discusses the free-run and stimulus evoked EMG literature in the context of other modalities and how these EMG modalities are implemented, performed, and interpreted during decompressive and instrumented surgical procedures in the cervical, thoracic, and lumbosacral spine. The chapter reviews that the number of different monitoring modalities have been developed and successfully implemented including somatosensory evoked potentials (SEPs), dermatomal sensory evoked potentials (DSEPs), motor evoked potentials (MEPs), and free-run and stimulus evoked electromyography (EMG). Each of these modalities has its advantages and disadvantages as it relates, to which segment of the spinal cord it assays or whether it monitors cord versus single nerve root function.

We extend the signal space separation (SSS) method to decompose multichannel magnetoencephalographic (MEG) data into regions of interest inside the head. It has been shown that the SSS method can transform MEG data into a signal component generated by neurobiological sources and a noise component generated by external sources outside the head. In this paper, we show that the signal component obtained by the SSS method can be further decomposed by a simple operation into signals originating from deep and superficial sources within the brain. This is achieved by using a scheme that exploits the beamspace methodology that relies on a linear transformation that maximizes the power of the source space of interest. The efficiency and accuracy of the algorithm are demonstrated by experiments utilizing both simulated and real MEG data.

Various formulations of polymeric hydrogels were synthesized and evaluated with the goal of developing a novel skin-surface biopotential electrode. Materials explored within the study included poly(2-hydroxyethyl methacrylate) (polyHEMA) in pure form or impregnated with the conducting polymers polypyrrole or poly(3,4-ethylenedioxythiophene) (PEDOT), as well as polyacrylate. The drying dynamics, ionic conductivity, and impedance of the prototype materials were characterized when applied to porcine and human skin, in order to determine their utility for a minimum preparation and low specific impedance surface electrode. The addition of a fraction of PEDOT or polypyrrole within a polyHEMA gel was found to decrease hydrogel impedance when tested within PBS, as well as on non-abraded porcine skin. A polyacrylate component added to polyHEMA had no significant effect on hydrogel impedance in PBS, but significantly reduced impedance on the porcine skin surface. Although having much lower specific impedance (impedance normalized by the contact area) than the commercial skin surface electrode (3M Red Dot), polyHEMA based electrodes require skin abrasion. Pure cross-linked polyacrylate gel was found to provide the most attractive option, and yielded competitive low-impedance performance on non-abraded human skin.

Superconducting quantum interference devices (SQUIDs) have been widely utilized in biomedical applications due to their extremely high sensitivity to magnetic signals. The present study explores the feasibility of a new type of nanotechnology-based imaging method using standard clinical magnetoencephalographic (MEG) systems equipped with SQUID sensors. Previous studies have shown that biological targets labeled with non-toxic, magnetized nanoparticles can be imaged by measuring the magnetic field generated by these particles. In this work, we demonstrate that (1) the magnetic signals from certain nanoparticles can be detected without magnetization using standard clinical MEG, (2) for some types of nanoparticles, only bound particles produce detectable signals, and (3) the magnetic field of particles several hours after magnetization is significantly stronger than that of un-magnetized particles. These findings hold promise in facilitating the potential application of magnetic nanoparticles to in vivo tumor imaging. The minimum amount of nanoparticles that produce detectable signals is predicted by theoretical modeling and computer simulation.

Video transmission plays a critical role in robotic telesurgery because of the required high bandwidth and quality requirement. We propose an adaptive video preprocessing technique to accelerate the transmission of telesurgical video. Using this technique, the bandwidth can be reallocated adaptively from non-essential surrounding regions to the region of interest when preprocessed image sequences are passed to the video encoder, ensuring excellent image quality of critical regions (e.g. surgical landscape). The algorithm first generates a physiologically appropriate attention map which can be updated dynamically. Then, an adaptive edge-preserving image smoothing filter is utilized to remove insignificant features. The parameters of the filter are adjusted according to the attention map. Our experimental result shows that, with the preprocessing technique, over half of the bandwidth can be reduced while there is no significant visual effect at the site of remote observer.

Research interest in multi-frame Superresolution has risen substantially in recent years. This paper presents a modified Projection Onto Convex Set (POCS) superresolution method based on wavelet transform. The method analyzes the image formation model from wavelet multiresolution analysis point of view and defines an closed convex set and its corresponding projection based on wavelet transform. An iterative procedure is utilized to reduce the estimated errors of the result image, and this guarantees the estimated image to lay in the intersection of different convex sets, thus produces a high resolution image with a reduced error. The effectiveness of the algorithm is demonstrated by experimental results.