Kustannuslaskenta isännöintiyrityksessä : asiakaskannattavuusnäkökulma

Siirretty Doriast

Processing of weak magnetic multichannel signals : the signal space separation method

This work concentrates on processing of multichannel magnetoencephalographic (MEG) data. The aim of the work is to improve the quality of the measured signals in order to enable reliable data analysis. A special requirement for the developed mathematical methods is that they should be applicable to all MEG measurements regardless of the level of cooperation of the subject. This is essential, e.g., with small children and in clinical investigations. In addition to MEG, the methods presented here can be used in other magnetic multichannel measurements, too. MEG measurements are used in basic brain research and recently also in clinical examinations. The method has excellent time resolution and reasonably good spatial resolution, which makes it a very useful tool in analysis of various brain functions. During the last 20 years, the instrumentation of MEG has been developed from devices containing less than 40 channels and limited coverage to whole-head systems with more than 300 channels. Yet, many of the signal processing and analysis methods used today date back to the time of the old instrumentation with limited coverage of the magnetic field. Traditionally, MEG investigations have been performed primarily only with cooperative subjects in order to avoid the characteristic problems of MEG, including signal distortions due to head movements and artifacts caused by sources attached to the body. In clinical measurements, however, the patient may have involuntary movements and carry artifact sources such as therapeutic stimulators. This work introduces the signal space separation method (SSS), which is based on Maxwell's equations and the generous spatial sampling by modern multichannel MEG devices. The thesis describes the theoretical foundations of SSS and its temporal extension tSSS, and demonstrates the results in several applications. SSS and tSSS are shown to significantly improve the quality of MEG data under conditions previously considered too challenging for meaningful analysis. Notably, the methods have potential to expand the applicability of MEG to some new patient groups, e.g., patients with deep brain stimulators

Signal Space Separation Beamformer

We have combined Signal Space Separation and beamformers (SSS beamformer). The SSS beamformer was tested by simulation in the presence of simulated brain noise. The SSS beamformer performs at least as well as the conventional beamformer, provided that the expansion order is sufficiently high. For beamformer outputs which depend on power or power difference normalized by the projected noise, the spatial resolution of the SSS beamformer is significantly better than that of the conventional beamformers if the sources are deeper, and about the same as that of the conventional beamformer when the sources are superficial. For beamformer outputs which depend on the ratio of powers, the spatial resolutions of the SSS and conventional beamfomers are the same. The sensor noise covariance matrix in the SSS basis is non-diagonal. The SSS beamformers with diagonalized noise covariance matrix exhibit better spatial resolution than that with non-diagonal noise covariance matrix. The SSS beamformers are computationally more efficient than the conventional beamformers

Automatic detection and visualisation of MEG ripple oscillations in epilepsy

High frequency oscillations (HFOs, 80–500 Hz) in invasive EEG are a biomarker for the epileptic focus. Ripples (80–250 Hz) have also been identified in non-invasive MEG, yet detection is impeded by noise, their low occurrence rates, and the workload of visual analysis. We propose a method that identifies ripples in MEG through noise reduction, beamforming and automatic detection with minimal user effort. We analysed 15 min of presurgical resting-state interictal MEG data of 25 patients with epilepsy. The MEG signal-to-noise was improved by using a cross-validation signal space separation method, and by calculating ~ 2400 beamformer-based virtual sensors in the grey matter. Ripples in these sensors were automatically detected by an algorithm optimized for MEG. A small subset of the identified ripples was visually checked. Ripple locations were compared with MEG spike dipole locations and the resection area if available. Running the automatic detection algorithm resulted in on average 905 ripples per patient, of which on average 148 ripples were visually reviewed. Reviewing took approximately 5 min per patient, and identified ripples in 16 out of 25 patients. In 14 patients the ripple locations showed good or moderate concordance with the MEG spikes. For six out of eight patients who had surgery, the ripple locations showed concordance with the resection area: 4/5 with good outcome and 2/3 with poor outcome. Automatic ripple detection in beamformer-based virtual sensors is a feasible non-invasive tool for the identification of ripples in MEG. Our method requires minimal user effort and is easily applicable in a clinical setting

Trauma Surveillance in Cape Town, South Africa: An Analysis of 9236 Consecutive Trauma Center Admissions.

PublishedJournal ArticleResearch Support, Non-U.S. Gov'tThis is the final version of the article. Available from American Medical Association via the DOI in this record.IMPORTANCE: Trauma is a leading cause of death and disability worldwide. In many low- and middle-income countries, formal trauma surveillance strategies have not yet been widely implemented. OBJECTIVE: To formalize injury data collection at Groote Schuur Hospital, the chief academic hospital of the University of Cape Town, a level I trauma center, and one of the largest trauma referral hospitals in the world. DESIGN, SETTING, AND PARTICIPANTS: This was a prospective study of all trauma admissions from October 1, 2010, through September 30, 2011, at Groote Schuur Hospital. A standard admission form was developed with multidisciplinary input and was used for both clinical and data abstraction purposes. Analysis of data was performed in 3 parts: demographics of injury, injury risk by location, and access to and maturity of trauma services. Geographic information science was then used to create satellite imaging of injury "hot spots" and to track referral patterns. Finally, the World Health Organization trauma system maturity index was used to evaluate the current breadth of the trauma system in place. MAIN OUTCOMES AND MEASURES: The demographics of trauma patients, the distribution of injury in a large metropolitan catchment, and the patterns of injury referral and patient movement within the trauma system. RESULTS: The minimum 34-point data set captured relevant demographic, geographic, incident, and clinical data for 9236 patients. Data field completion rates were highly variable. An analysis of demographics of injury (age, sex, and mechanism of injury) was performed. Most violence occurred toward males (71.3%) who were younger than 40 years of age (74.6%). We demonstrated high rates of violent interpersonal injury (71.6% of intentional injury) and motor vehicle injury (18.8% of all injuries). There was a strong association between injury and alcohol use, with alcohol implicated in at least 30.1% of trauma admissions. From a systems standpoint, the data suggest a mature pattern of referral consistent with the presence of an inclusive trauma system. CONCLUSIONS AND RELEVANCE: The implementation of injury surveillance at Groote Schuur Hospital improved insights about injury risk based on demographics and neighborhood as well as access to service based on patterns of referral. This information will guide further development of South Africa's already advanced trauma system.This work was supported by the Canadian Institute for Health Research and the Social Sciences and Humanities Research Council

An Iterative Implementation of the Signal Space Separation Method for Magnetoencephalography Systems with Low Channel Counts

The signal space separation (SSS) method is routinely employed in the analysis of multichannel magnetic field recordings (such as magnetoencephalography (MEG) data). In the SSS method, signal vectors are posed as a multipole expansion of the magnetic field, allowing contributions from sources internal and external to a sensor array to be separated via computation of the pseudo-inverse of a matrix of the basis vectors. Although powerful, the standard implementation of the SSS method on MEG systems based on optically pumped magnetometers (OPMs) is unstable due to the approximate parity of the required number of dimensions of the SSS basis and the number of channels in the data. Here we exploit the hierarchical nature of the multipole expansion to perform a stable, iterative implementation of the SSS method. We describe the method and investigate its performance via a simulation study on a 192-channel OPM-MEG helmet. We assess performance for different levels of truncation of the SSS basis and a varying number of iterations. Results show that the iterative method provides stable performance, with a clear separation of internal and external sources

Newborns discriminate novel from harmonic sounds: a study using magnetoencephalography

Objective: We investigated whether newborns respond differently to novel and deviant sounds during quiet sleep. Methods: Twelve healthy neonates were presented with a three-stimulus oddball paradigm, consisting of frequent standard (76%), infrequent deviant (12%), and infrequent novel stimuli (12%). The standards and deviants were counterbalanced between the newborns and consisted of 500 and 750 Hz tones with two upper harmonics. The novel stimuli contained animal, human, and mechanical sounds. All stimuli had a duration of 300 ms and the stimulus onset asynchrony was 1 s. Evoked magnetic responses during quiet sleep were recorded and averaged offline. Results: Two deflections peaking at 345 and 615 ms after stimulus onset were observed in the evoked responses of most of the newborns. The first deflection was larger to novel and deviant stimuli than to the standard and, furthermore, larger to novel than to deviant stimuli. The second deflection was larger to novel and deviant stimuli than to standards, but did not differ between the novels and deviants. Conclusions: The two deflections found in the present study reflect different mechanisms of auditory change detection and discriminative processes. Significance: The early brain indicators of novelty detection may be crucial in assessing the normal and abnormal cortical function in newborns. Further, studying evoked magnetic fields to complex auditory stimulation in healthy newborns is needed for studying the newborns at-risk for cognitive or language problems