P Murphy

University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States

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Publications (28)67.42 Total impact

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    ABSTRACT: Fetal magnetoencephalography (fMEG) recordings are contaminated by maternal and fetal magnetocardiography (MCG) signals and by other biological and environmental interference. Currently, all methods for the attenuation of these signals are based on a time-domain approach. We have developed and tested a frequency dependent procedure for removal of MCG and other interference from the fMEG recordings. The method uses a set of reference channels and performs subtraction of interference in the frequency domain (SUBTR). The interference-free frequency domain signals are converted back to the time domain. We compare the performance of the frequency dependent approach with our present approach for MCG attenuation based on orthogonal projection (OP). SUBTR has an advantage over OP and similar template approaches because it removes not only the MCG but also other small amplitude biological interference, avoids the difficulties with inaccurate determination of the OP operator, provides more consistent and stable fMEG results, does not cause signal redistribution, and if references are selected judiciously, it does not reduce fMEG signal amplitude. SUBTR was found to perform well in simulations and on real fMEG recordings, and has a potential to improve the detection of fetal brain signals. The SUBTR removes interference without the need for a model of the individual interference sources. The method may be of interest for any sensor array noise reduction application where signal-free reference channels are available.
    NeuroImage 09/2011; 59(3):2475-84. · 6.13 Impact Factor
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    ABSTRACT: The purpose of fetal magnetoencephalography (fMEG) is to record and analyze fetal brain activity. Unavoidably, these recordings consist of a complex mixture of bio-magnetic signals from both mother and fetus. The acquired data include biological signals that are related to maternal and fetal heart function as well as fetal gross body and breathing movements. Since fetal breathing generates a significant source of bio-magnetic interference during these recordings, the goal of this study was to identify and quantify the signatures pertaining to fetal breathing movements (FBM). The fMEG signals were captured using superconducting quantum interference devices (SQUIDs) The existence of FBM was verified and recorded concurrently by an ultrasound-based video technique. This simultaneous recording is challenging since SQUIDs are extremely sensitive to magnetic signals and highly susceptible to interference from electronic equipment. For each recording, an ultrasound-FBM (UFBM) signal was extracted by tracing the displacement of the boundary defined by the fetal thorax frame by frame. The start of each FBM was identified by using the peak points of the UFBM signal. The bio-magnetic signals associated with FBM were obtained by averaging the bio-magnetic signals time locked to the FBMs. The results showed the existence of a distinctive sinusoidal signal pattern of FBM in fMEG data.
    Physiological Measurement 02/2011; 32(2):263-73. · 1.50 Impact Factor
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    ABSTRACT: Analysis of fetal magnetoencephalographic brain recordings is restricted by low signal to noise ratio (SNR) and non-stationarity of the sources. Beamformer techniques have been applied to improve SNR of fetal evoked responses. However, until now the effect of non-stationarity was not taken into account in detail, because the detection of evoked responses is in most cases determined by averaging a large number of trials. We applied a windowing technique to improve the stationarity of the data by using short time segments recorded during a flash-evoked study. In addition, we implemented a random field theory approach for more stringent control of false-positives in the statistical parametric map of the search volume for the beamformer. The search volume was based on detailed individual fetal/maternal biometrics from ultrasound scans and fetal heart localization. Average power over a sliding window within the averaged evoked response against a randomized average background power was used as the test z-statistic. The significance threshold was set at 10% over all members of a contiguous cluster of voxels. There was at least one significant response for 62% of fetal and 95% of newborn recordings with gestational age (GA) between 28 and 45 weeks from 29 subjects. We found that the latency was either substantially unchanged or decreased with increasing GA for most subjects, with a nominal rate of about − 11 ms/week. These findings support the anticipated neurophysiological development, provide validation for the beamformer model search as a methodology, and may lead to a clinical test for fetal cognitive development.
    NeuroImage 01/2010; · 6.13 Impact Factor
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    ABSTRACT: Fetal magnetoencephalography (fMEG) is used to study neurological functions of the developing fetus by measuring magnetic signals generated by electrical sources within the fetal brain. For this aim either auditory or visual stimuli are presented and evoked brain activity or spontaneous activity is measured at the sensor level. However a limiting factor of this approach is the low signal to noise ratio (SNR) of recorded signals. To overcome this limitation, advanced signal processing techniques such as spatial filters (e.g., beamformer) can be used to increase SNR. One crucial aspect of this technique is the forward model and, in general, a simple spherical head model is used. This head model is an integral part of a model search approach to analyze the data due to the lack of exact knowledge about the location of the fetal head. In the present report we overcome this limitation by a coregistration of volumetric ultrasound images with fMEG data. In a first step we validated the ultrasound to fMEG coregistration with a phantom and were able to show that the coregistration error is below 2 cm. In the second step we compared the results gained by the model search approach to the exact location of the fetal head determined on pregnant mothers by ultrasound. The results of this study clearly show that the results of the model search approach are in accordance with the location of the fetal head.
    NeuroImage 09/2009; 49(2):1469-78. · 6.13 Impact Factor
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    ABSTRACT: The design, safety analysis and performance of a fetal visual stimulation system suitable for fetal and neonatal magnetoencephalography studies are presented. The issue of fetal, neonatal and maternal safety is considered and the maximum permissible exposure is computed for the maternal skin and the adult eye. The risk for neonatal eye exposure is examined. It is demonstrated that the fetus, neonate and mother are not at risk.
    Physiological Measurement 02/2009; 30(1):N1-10. · 1.62 Impact Factor
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    ABSTRACT: We propose to use cross-correlation function to determine significant fetal and neonatal evoked responses (ERs). We quantify ERs by cross-correlation between the stimulus time series and the recorded brain signals. The statistical significance of the correlation is calculated by surrogate analysis. For validation of our approach we investigated a model which mimics the generation of ERs. The model assumes a fixed latency of the ER and contains two parameters, epsilon and lambda. Whether or not the system responds to a given stimulus is controlled by epsilon. The amount to which the system is excited from the base line (background activity) is governed by lambda. We demonstrate the technique by applying it to auditory evoked responses from four fetuses (21 records) between 27 and 39 weeks of gestational age and four neonates (eight records). The method correctly identified the ER and the latency incorporated in the model. A combined analysis of fetuses and neonates data resulted in a significant negative correlation between age and latency. The analysis of ER, especially for fetal and newborn recordings, should be based on advanced data analysis including the assessment of the significance of responses. The negative correlation between age and latency indicates the neurological maturation. The proposed method can be used to objectively assess the ER in fetuses and neonates.
    NeuroImage 09/2008; 43(3):521-7. · 6.13 Impact Factor
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    ABSTRACT: In order to obtain adequate signal to noise ratio (SNR), stimulus-evoked brain signals are averaged over a large number of trials. However, in certain applications, e.g. fetal magnetoencephalography (MEG), this approach fails due to underlying conditions (inherently small signals, non-stationary/poorly characterized signals, or limited number of trials). The resulting low SNR makes it difficult to reliably identify a response by visual examination of the averaged time course, even after pre-processing to attenuate interference. The purpose of this work was to devise an intuitive statistical significance test for low SNR situations, based on non-parametric bootstrap resampling. We compared a two-parameter measure of p-value and statistical power with a bootstrap equal means test and a traditional rank test using fetal MEG data collected with a light flash stimulus. We found that the two-parameter measure generally agreed with established measures, while p-value alone was overly optimistic. In an extension of our approach, we compared methods to estimate the background noise. A method based on surrogate averages resulted in the most robust estimate. In summary we have developed a flexible and intuitively satisfying bootstrap-based significance measure incorporating appropriate noise estimation.
    Journal of Neuroscience Methods 03/2008; 168(1):265-72. · 1.96 Impact Factor
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    ABSTRACT: Using detrended fluctuation analysis (DFA), we studied the scaling properties of the time instances (occurrence) of the fetal breathing, gross-body, and extremity movements scored on a second by second basis from the recorded ultrasound measurements of 49 fetuses. The DFA exponent α of all the three movements of the fetuses varied between 0.63 and 1.1. We found an increase in α obtained for the movement due to breathing as a function of the gestational age while this trend was not observed for gross-body and extremity movements. This trend was argued as the indication of the maturation of lung and functional development of respiratory aspect of the fetal central nervous system. This result may be useful in discriminating normal fetuses from high-risk fetuses.
    Physica A: Statistical Mechanics and its Applications 01/2008; 386(1):231-239. · 1.72 Impact Factor
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    ABSTRACT: Flash-evoked responses can be recorded from the fetus in utero. However, a standard analysis approach based on orthogonal projection (OP) to attenuate maternal and fetal cardiac signals leads to a spatial redistribution of the signal. This effect prevents the correlation of source location with a known fetal head location in some cases and the signal-to-noise ratio (SNR) is sometimes limited such that the response latency is difficult to determine. We used a modified beamformer model search analysis to avoid the redistribution shortcoming and to improve the SNR. We included a statistical test for residual interference in the average and quantified significance of the evoked response with a bootstrap method. Selected source locations compared favorably to fetal head locations estimated from ultrasound exams. The evoked response time course was found to have a significant post-trigger peak with a latency between about 180 and 770 ms in more than 90% of the subject measurements. These results confirm that the combined application of a beamformer model search and bootstrap significance test provides a validation of the flash-evoked response observed in OP processed fetal MEG channels.
    Physics in Medicine and Biology 11/2007; 52(19):5803-13. · 2.92 Impact Factor
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    ABSTRACT: Non-invasive technique such as magneto-encephalography (MEG), initially pioneered to study human brain signals, has found many other applications in medicine. SQUID(1) Array for Reproductive Assessment (SARA) is a unique non-invasive scanning-device developed at the University of Arkansas for Medical Sciences (UAMS) that can detect fetal brain and other signals. The fetal magneto-encephalography (fMEG) signals often have many bio-magnetic signals mixed in. Examples include the movement of the fetus or muscle contraction of the mother. As a result, the recorded signals may show unexpected patterns, other than the target signal of interest. These "interventions" make it difficult for a physician to assess the exact fetal condition, including its response to various stimuli. We propose using intervention analysis and spatial-temporal auto-regressive moving-average (STARMA) modeling to address the problem. STARMA is a statistical method that examines the relationship between the current observations as a linear combination of past observations as well as observations at neighboring sensors. Through intervention analysis, the change in a pattern due to "interfering" signals can be accounted for. When these interferences are "removed," the end product is a "template" time series, or a typical signal from the target of interest. In this research, a "universal" template is obtained. The template is then used to detect intervention in other datasets by the method of template matching. By this method, it is possible to detect if there is an intervention in any dataset. It will assist physicians in monitoring the actual signal generated by fetal brain and other organs of interest.
    Journal of Neuroscience Methods 06/2007; 162(1-2):333-45. · 1.96 Impact Factor
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    ABSTRACT: We have evaluated a newly implemented optical stimulator in a fetal MEG system for effectiveness of eliciting fetal flash evoked response using 29 normal, low-risk fetuses. MEG recordings were performed with a 151 channel array conforming to the maternal abdomen. Interference from maternal and fetal heart was attenuated by orthogonal projection and stimulus events were averaged to improve SNR after rejection of data segments with fetal motion. The responses identified by visual examination of the averaged time courses were subjected to validation by a significance measure based on a bootstrap confidence interval computation. The second generation stimulator increased comfort and ease of placement between the maternal abdomen and sensor array while delivering greater optical power to the fetus. After discarding 2 measurements for excessive artifact, averaged evoked responses with good baseline above background noise levels were observed in 24 recordings resulting in a detection rate of 89%; 26% greater than previously reported by our group using a first generation stimulator. Use of the new fetal visual stimulation delivery system resulted in an increased detection rate of fetal flash evoked response and the bootstrap significance measure provided some validation of results.
    International Congress Series 06/2007; 1300:749-752.
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    ABSTRACT: Fetal brain signals produce weak magnetic fields at the maternal abdominal surface. In the presence of much stronger interference these weak fetal fields are often nearly indistinguishable from noise. Our initial objective was to validate these weak fetal brain fields by demonstrating that they agree with the electromagnetic model of the fetal brain. The fetal brain model is often not known and we have attempted to fit the data to not only the brain source position, orientation and magnitude, but also to the brain model position. Simulation tests of this extended model search on fetal MEG recordings using dipole fit and beamformers revealed a region of ambiguity. The region of ambiguity consists of a family of models which are not distinguishable in the presence of noise, and which exhibit large and comparable SNR when beamformers are used. Unlike the uncertainty of a dipole fit with known model plus noise, this extended ambiguity region yields nearly identical forward solutions, and is only weakly dependent on noise. The ambiguity region is located in a plane defined by the source position, orientation, and the true model centre, and will have a diameter approximately 0.67 of the modelled fetal head diameter. Existence of the ambiguity region allows us to only state that the fetal brain fields do not contradict the electromagnetic model; we can associate them with a family of models belonging to the ambiguity region, but not with any specific model. In addition to providing a level of confidence in the fetal brain signals, the ambiguity region knowledge in combination with beamformers allows detection of undistorted temporal waveforms with improved signal-to-noise ratio, even though the source position cannot be uniquely determined.
    Physics in Medicine and Biology 03/2007; 52(3):757-76. · 2.92 Impact Factor
  • Clinical Neurophysiology 09/2006; 117:1-1. · 2.98 Impact Factor
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    ABSTRACT: Recording fetal magnetoencephalographic (fMEG) signals in-utero is a demanding task due to biological interference, especially maternal and fetal magnetocardiographic (MCG) signals. A method based on orthogonal projection of MCG signal space vectors (OP) was evaluated and compared with independent component analysis (ICA). The evaluation was based on MCG amplitude reduction and signal-to-noise ratio of fetal brain signals using exemplary datasets recorded during ongoing studies related to auditory evoked fields. The results indicate that the OP method is the preferable approach for attenuation of MCG and for preserving the fetal brain signals in fMEG recordings.
    IEEE Transactions on Biomedical Engineering 09/2006; 53(8):1720-4. · 2.23 Impact Factor
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    ABSTRACT: Current standard magnetoencephalographic and -cardiographic systems do not allow real-time access to the measured data. We developed a software solution for real-time access and used it to create an online fetal heart rate monitor.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2005; 6:5987-90.
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    ABSTRACT: Fetal magnetoencephalogram (fMEG) is measured in the presence of a large interference from maternal and fetal magnetocardiograms (mMCG and fMCG). This cardiac interference can be successfully removed by orthogonal projection of the corresponding spatial vectors. However, orthogonal projection redistributes the fMEG signal among channels. Such redistribution can be readily accounted for in the forward solution, and the signal topography can also be corrected. To assure that the correction has been done properly, and also to verify that the measured signal originates from within the fetal head, we have modeled the observed fMEG by two extreme models where the fetal head is assumed to be either electrically transparent or isolated from the abdominal tissue. Based on the measured spontaneous, sharp wave, and flash-evoked fMEG signals, we have concluded that the model of the electrically isolated fetal head is more appropriate for fMEG analysis. We show with the help of this model that the redistribution due to projection was properly corrected, and also, that the measured fMEG is consistent with the known position of the fetal head. The modeling provides additional confidence that the measured signals indeed originate from within the fetal head.
    NeuroImage 04/2004; 21(3):1009-20. · 6.13 Impact Factor
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    ABSTRACT: Fetal magnetoencephalographic (fMEG) measurements are performed with interference from the fetal and maternal magnetocardiogram (MCG). Fetal movement, fetal breathing, fetal eye blinks or eye rollings and maternal muscle-contraction may generate detectable signals. These factors can be called "interventions," which can be manifested in space and/or time. They make the fMEG signals nonstationary. By examining temporal relationship of the multi-channel records, we are able to find the spatial signature of these "interventions." The aim of this study is to examine nonstationarity in single channel and nonhomogeniety in multiple channels of the fMEG data. Preliminary results are reported here, and may be used in further studies, leading toward intervention identification, and ultimately fetal state determination.
    Neurology & Clinical Neurophysiology 02/2004; 2004:11.
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    ABSTRACT: Magnetoencephalography (MEG) is a technique used to non-invasively record neuromagnetic fields generated by the human brain. Our new SARA (SQUID Array for Reproductive Assessment) is a unique MEG device designed specifically for the study of the fetal neurophysiology. During the acquistion of fetal magnetoencephalography (fMEG), many other interfering bio-magnetic signals are collected as well. Examples include the movement of fetus or muscle contraction of the mother. As a result, the recorded signals may show unexpected patterns, other than the target signal of interest. These interventions makes it difficult for a physician to assess the exact fetal condition, including its response to various stimuli. We propose using intervention analysis and spatial-temporal autoregressive moving average (STARMA) modeling to address the problem. STARMA is a statistical method that examines the relationship between the current observations as a linear combination of past observations, as well as observations at neighboring sites. Through intervention analysis, the change in pattern due to interfering signals can be well accounted for. When these interferences are removed, the end product is a template time series, or a typical signal from the target of interest thus providing a more reliable means to monitor the actual signals generated by the fetal brain and other organs of interest.
    Neurology & Clinical Neurophysiology 02/2004; 2004:100.
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    ABSTRACT: The lack of an effective method for the diagnosis and management of labor points to the need for a new device. SARA-SQUID Array for Reproductive Assessment, is capable of recording spatial-temporal biomagnetic activity. The SARA system is first of its kind in the world dedicated to maternal-fetal research. We non-invasively recorded the magnetomyographic (MMG) signals corresponding to the uterine electrical activity from 7 pregnant mothers. The detailed physiological information obtained simultaneously from 151 sensors spread over the entire abdomen, will help in understanding the origin and propagation of the uterine activity. This information could give us better insight into the mechanism of uterine contraction and may help in better diagnosis and management of labor.
    The Journal of the Arkansas Medical Society 10/2003; 100(3):90-1.
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    ABSTRACT: The development of suitable techniques for quantifying mechanical and electrophysiological aspects of uterine contractions has been an active area of research. The uterus is a physiological system consisting of a large number of interacting muscle cells. The activity of these cells evolves with time, a trait characteristic of a dynamical system. While such complex physiological systems are non-linear by their very nature, whether this non-linearity is exhibited in the external recording is far from trivial. Traditional techniques such as spectral analysis have been used in the past, but these techniques implicitly assume that the process generating the contractions is linear and hence may be biased. In this tutorial review, a systematic approach using a hierarchy of surrogate algorithms is used to determine the nature of the process generating the contractions produced during labor. The results reveal that uterine contractions are probably generated by non-linear processes. The contraction segments were obtained through simultaneous recordings of the electrical and magnetic signals corresponding to the electrophysiological activity of the uterus and then analyzed. The electrical activity was recorded by placement of non-invasive electrodes onto the maternal abdomen and magnetic activity was recorded non-invasively using a superconducting quantum interference device (SQUID).
    Journal of Maternal-Fetal and Neonatal Medicine 08/2003; 14(1):8-21. · 1.21 Impact Factor

Publication Stats

173 Citations
67.42 Total Impact Points

Institutions

  • 2009–2011
    • University of Arkansas for Medical Sciences
      • Department of Obstetrics and Gynecology
      Little Rock, Arkansas, United States
  • 1995–2011
    • University of Arkansas at Little Rock
      • • Graduate Institute of Technology (GIT)
      • • Department of Applied Sciences
      Little Rock, AR, United States
  • 2005–2010
    • University of Tuebingen
      • Institute of Medical Psychology and Behavioral Neurobiology
      Tübingen, Baden-Württemberg, Germany