An acoustic aneurysm-detector

Intracranial aneurysms have a high prevalence in the adult population; and if they rupture, significant morbidity almost always ensues. However, pulsatile blood flow through aneurysms produces vibrational sound patterns that may be detected extracranially. Thus, an acoustic aneurysm-detector has been developed to detect the sounds produced by intracranial aneurysms prior to rupture. The design is based on the utilization of a hydrophone for signal detection and computational signal processing for signal extraction. Data are examined in the time domain, the frequency domain, and the time-frequency plane. Examples are presented from an animal model and from a patient with a known aneurysm.     

A wavelet transform based POCS Superresolution algorithm

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.     

The forward EEG solutions can be computed using artificial neural networks

Study of electroencenphalography (EEG) is the one of the most utilized methods in both basic brain research and clinical diagnosis of neurological disorders. Recent technological advances in computer and electronic systems have allowed the EEG to be recorded from large electrode arrays. Modeling the brain waves using a head volume conductor model provides an effective method to localize functional generators within the brain. However, the forward solutions to this model, which represent theoretical potentials in response to current sources within the volume conductor, are difficult to compute because of time-consuming numerical procedures utilized in either the boundary element method (BEM) or the finite element method (FEM). This paper presents a novel computational approach using an artificial neural network (ANN) to map two vectors of forward solutions. These two vectors correspond to different head models but with respect to the same current source. The input vector to the ANN is based on the spherical head model, which can be computed efficiently but involves large errors. The output vector from the ANN is based on the spheroidal model, which is more precise, but difficult to compute directly using the traditional means. Our experiments indicate that this ANN approach provides a remarkable improvement over the BEM and FEM methods: 1) the mean-square error of computation was only approximately 0.3% compared to the exact solution; 2) the online computation was extremely efficient, requiring only 168 floating point operations per channel to compute the forward solution, and 10.2 K-bytes of storage to represent the entire ANN. Using this approach it is possible to perform real-time EEG modeling accurately on personal computers.     

Real time signal processing in the clinical setting

We routinely use a variety of real time signal acquisition, enhancement, and display techniques in the operating room to provide the surgeon with functional information. This enables reduction of surgical morbidity in cases which present a significant risk to the nervous system. Here we present regression based signal processing algorithms which produce considerable signal-to-noise-ratio enhancement with corresponding reduction in the time required to obtain an interpretable neurophysiological signal. We also present the approach we have applied to fault tolerance and distributed data display for our workstation cluster environment.     

A comparison of conventional and matched filtering techniques for rapid eye movement detection of the newborn

This paper compares an extended conventional filter technique for automated detection and analysis of rapid eye movements (REM) in neonates, using amplitude, synchrony, velocity, and coherence threshold criteria, with a matched filtering technique using the morphology of the REM waveform. Analyses of both simulated and real data were carried out. Automated REM tabulations are compared with visual scoring by a trained observer. Both preterm and fullterm neonates were used to test these methods. Both the advantages and disadvantages of these two techniques are discussed as compared with conventional methods which use only amplitude and synchrony threshold criteria. The major advantage of the extended conventional over the conventional method, as well as the matched filtering over the extended conventional technique, is the increased REM detection rate for ten minute intervals of artifact-free sleep. More accurate methods of automated REM detection that can be applied over extended monitoring periods are still needed.     

NeuroNet: collaborative intraoperative guidance and control

Neurophysiological monitoring assesses CNS structure function relationships during surgery. NeuroNet supports remote performance of this task through real time multimodal data processing and multimedia network communication. The system is fully integrated, transparently combining the collection, processing, and presentation of real time data sources, including all physiological monitoring functions, with non real time functions and extensive online database information. Workstations are mounted in instrumentation racks and configured with appropriate electronics to support various data acquisition tasks including electroencephalograms (EEGs), electromyograms (EMGs), and multimodality evoked potentials. Multiple racks can be used in parallel on the same case if the number of variables to be monitored exceeds the capacity of a single tack. The data acquired on these systems is transparently accessible, in real time, across the network for both review and analysis     

Data Integration for Medical Information Management

We present a new method for data integration and security by mixing medical waveforms and images with encrypted patient identifiers and unencrypted ancillary information, such as acquisition parameters, diagnostic comments and notes in textual, pictorial, and voice forms. We vary the sampling rate according to the instantaneous frequency of the signal. Redundant samples (or pixels) are eliminated and replaced by associative data which are labeled using a status string encoded based on the Huffman and run-length techniques. This method achieves both data compression and integration simultaneously, allows synchronized presentation of information from different sources by using multimedia technology, and provides data security features. Mingui Sun received a B.S. degree from the Shenyang Chemical Engineering Institute, China, in 1982, and M.S. and Ph. D. degrees in Electrical Engineering from the University of Pittsburgh in 1986 and 1989, respectively. He was a Graduate Student Researcher from 1985 to 1989 working on signal and image processing projects. Currently, he is a Associate Professor and an Associate Director of the Center for Clinical Neurophysiology in the Department of Neurosurgery at the University of Pittsburgh, and a Director of Research at Computational Diagnostics, Inc. His current research and development interests include advanced biomedical electronic devices, biomedical signal and image processing, sensors and transducers, biomedical instruments, artificial neural networks, wavelet transforms, time-frequency analysis, and the inverse problem of neurophysiological signals. He has over 160 publications in these areas. Qiang Liu received his B.S. and M.S. degrees in electrical engineering from Xidian University, Xian, China, in 1996 and 1999 respectively. He is currently a Ph.D. student at the University of Pittsburgh, Pittsburgh, USA. His further research interests include biomedical signal processing, medical imaging, and image/video segmentation, coding and transmission. Robert J. Sclabassi received the B.S.E. degree from Loyola University, Los Angeles, the M.S.E.E., Engineer in Electrical Engineering, and Ph.D. degrees in electrical engineering from the University of Southern California, and the M.D. degree from the University of Pittsburgh. He was employed in the Advanced Systems Laboratory at TRW, Los Angeles, and was a postdoctoral fellow at the Brain Research Institute at the University of California, Los Angeles. He was on the faculties of Department of Neurology and Biomathematics at UCLA until he joined the University of Pittsburgh. Dr. Sclabassi is currently a Professor of Neurological Surgery, Psychiatry, Electrical Engineering, Mechanical Engineering, Psychiatry, and Behavioral Neuroscience at the University of Pittsburgh. Dr. Sclabassi has published over 400 papers, chapters and conference proceedings. Dr. Sclabassi is a Registered Professional Engineer.     

Association Among Parental Substance Use Disorder, P300 Amplitude, and Neurobehavioral Disinhibition in Preteen Boys at High Risk for Substance Use Disorder.

The P300 amplitude of the event-related potential as a mediator of the association between parental substance use disorder (SUD) and child's neurobehavioral disinhibition was assessed. The P300 amplitude was recorded using an oddball task in sons of fathers having either lifetime SUD (n = 105) or no psychiatric disorder (n = 160). Neurobehavioral disinhibition was assessed using measures of affect regulation, behavior control, and executive cognitive function. Parental SUD and child's P300 amplitude accounted for, respectively, 16.6% and 16.8% of neurobehavioral disinhibition variance. Controlling for parental and child psychopathology, an association between parental SUD and child's P300 amplitude was not observed. It was concluded that the P300 amplitude does not mediate the association between parental SUD and child's neurobehavioral disinhibition. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Spatial Filtering of MEG Signals for User-Specified Spherical Regions

We introduce a spatial filtering method in the spherical harmonics domain for constraining magnetoencephalographic (MEG) multichannel measurements to any user-specified spherical region of interest (ROI) inside the head. The method relies on a linear transformation of the signal space separation inner coefficients that represent the MEG signal generated by sources located inside the head. The spatial filtering is achieved effectively by constructing a spherical harmonics basis vector that is dependent on the center of the targeted ROI and it does not require any discrete division of the headspace into grids like the traditional MEG spatial filtering approaches. The validation and the performance of the method are demonstrated through both simulated and actual bilateral auditory-evoked data experiments.