Biomedical subjects

Richard B Reilly

Publications and source records attributed to Richard B Reilly.

11 recordsLinked to original sources

PubMed2006

The VESPA: a method for the rapid estimation of a visual evoked potential.

Faster and less obtrusive means for measuring a Visual Evoked Potential would be valuable in clinical testing and basic neuroscience research. This study presents a method for accomplishing this by smoothly modulating the luminance of a visual stimulus using a stochastic process. Despite its visually unobtrusive nature, the rich statistical structure of the stimulus enables rapid estimation of the visual system's impulse response. The profile of these responses, which we call VESPAs, correlates with standard VEPs, with r=0.91, p<10(-28) for the group average. The time taken to obtain a VESPA with a given signal-to-noise ratio compares favorably to that required to obtain a VEP with a similar level of certainty. Additionally, we show that VESPA responses to two independent stimuli can be obtained simultaneously, which could drastically reduce the time required to collect responses to multiple stimuli. The new method appears to provide a useful alternative to standard VEP methods, and to have potential application both in clinical practice and to the study of sensory and perceptual functions.

Adult↗

PubMed2006

Increases in alpha oscillatory power reflect an active retinotopic mechanism for distracter suppression during sustained visuospatial attention.

Human electrophysiological (EEG) studies have demonstrated the involvement of alpha band (8- to 14-Hz) oscillations in the anticipatory biasing of attention. In the context of visual spatial attention within bilateral stimulus arrays, alpha has exhibited greater amplitude over parietooccipital cortex contralateral to the hemifield required to be ignored, relative to that measured when the same hemifield is to be attended. Whether this differential effect arises solely from alpha desynchronization (decreases) over the "attending" hemisphere, from synchronization (increases) over the "ignoring" hemisphere, or both, has not been fully resolved. This is because of the confounding effect of externally evoked desynchronization that occurs involuntarily in response to visual cues. Here, bilateral flickering stimuli were presented simultaneously and continuously over entire trial blocks, such that externally evoked alpha desynchronization is equated in precue baseline and postcue intervals. Equivalent random letter sequences were superimposed on the left and right flicker stimuli. Subjects were required to count the presentations of the target letter "X" at the cued hemifield over an 8-s period and ignore the sequence in the opposite hemifield. The data showed significant increases in alpha power over the ignoring hemisphere relative to the precue baseline, observable for both cue directions. A strong attentional bias necessitated by the subjective difficulty in gating the distracting letter sequence is reflected in a large effect size of 2.1 (eta2 = 0.82), measured from the attention x hemisphere interaction. This strongly suggests that alpha synchronization reflects an active attentional suppression mechanism, rather than a passive one reflecting "idling" circuits.

Adult↗

PubMed2006

Heart and respiration rate changes in the neonate during electroencephalographic seizure.

An investigation of changes in the neonatal electrocardiogram (ECG) and respiration signals from labelled seizure data for five neonatal patient records is reported. A decrease during seizure of 5.70% in the mean RR-interval was found. The mean respiration rate per epoch was found to decrease by a mean 18.44% during seizure. No significant change in the RR-interval standard deviation or in the mean respiration amplitude during seizure was observed. These results raise the possibility of using ECG and respiration based methods instead of existing electrcoenceplogram (EEG) based methods, or in concert with EEG-based neonatal seizure detection methods to improve on previously reported seizure detection methods.

Analysis of Variance↗

PubMed2006

Telephony-based voice pathology assessment using automated speech analysis.

A system for remotely detecting vocal fold pathologies using telephone-quality speech is presented. The system uses a linear classifier, processing measurements of pitch perturbation, amplitude perturbation and harmonic-to-noise ratio derived from digitized speech recordings. Voice recordings from the Disordered Voice Database Model 4337 system were used to develop and validate the system. Results show that while a sustained phonation, recorded in a controlled environment, can be classified as normal or pathologic with accuracy of 89.1%, telephone-quality speech can be classified as normal or pathologic with an accuracy of 74.2%, using the same scheme. Amplitude perturbation features prove most robust for telephone-quality speech. The pathologic recordings were then subcategorized into four groups, comprising normal, neuromuscular pathologic, physical pathologic and mixed (neuromuscular with physical) pathologic. A separate classifier was developed for classifying the normal group from each pathologic subcategory. Results show that neuromuscular disorders could be detected remotely with an accuracy of 87%, physical abnormalities with an accuracy of 78% and mixed pathology voice with an accuracy of 61%. This study highlights the real possibility for remote detection and diagnosis of voice pathology.

Algorithms↗

PubMed2006

A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features.

An adaptive system for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats into one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard is presented. The heartbeat classification system processes an incoming recording with a global-classifier to produce the first set of beat annotations. An expert then validates and if necessary corrects a fraction of the beats of the recording. The system then adapts by first training a local-classifier using the newly annotated beats and combines this with the global-classifier to produce an adapted classification system. The adapted system is then used to update beat annotations. The results of this study show that the performance of a patient adaptable classifier increases with the amount of training of the system on the local record. Crucially, the performance of the system can be significantly boosted with a small amount of adaptation even when all beats used for adaptation are from a single class. This study illustrates the ability to provide highly beneficial automatic arrhythmia monitoring and is an improvement on previously reported results for automated heartbeat classification systems.

Algorithms↗

PubMed2005

Neurophysiological markers of alert responding during goal-directed behavior: a high-density electrical mapping study.

The ability to dynamically modulate the intensity of sustained attention (i.e., alertness) is an essential component of the human executive control system, allowing us to function purposefully in accordance with our goals. In this study we examine high-density ERP markers of alert responding during the fixed sequence sustained attention to response task (SART(fixed)). This paradigm has proven to be a sensitive clinical metric in patient populations with deficits in their ability to sustain attention (e.g., attention deficit hyperactivity disorder). In this task subjects withhold a button press to an infrequent no-go target ('3') embedded within a predictable sequence of numbers ('1' to '9'). Our data reveal a complex pattern of effects across the trial sequence of the SART, with clear contributions from frontal and parietal cortices to sustained attentional performance. Over occipito-parietal regions, early visual attention processes were increased during trial 2 (i.e., trial in which the digit '2' was presented) and trial 3, giving rise to the so-called selection negativity (SN). Two prominent late components were manifest during trial 2: LP1 (550-800 ms) and LP2 (850-1150 ms) over occipito-parietal and central sites. We interpret the LP1 component on trial 2 as reflecting retrieval of the task goal and the subsequent LP2 as reflecting competition between the currently relevant go response and the subsequent no-go response. On trial 3, an enhanced "no-go N2" (250-450 ms) was seen fronto-centrally in the absence of the "no-go P3" that typically follows. Fronto-polar activity was also seen across all trials and may be indicative of subgoal processes to integrate the association between stimulus and goal. Prior to a lapse of attention (i.e., failure to inhibit a response to "3") the LP1 was significantly attenuated on the preceding trial 2 indicating a failure of anticipatory goal-directed processing. The results are discussed in terms of models of sustained attention involving frontal and parietal cortices.

Adolescent↗

PubMed2005

Visual spatial attention control in an independent brain-computer interface.

This paper presents a novel brain computer interface (BCI) design employing visual evoked potential (VEP) modulations in a paradigm involving no dependency on peripheral muscles or nerves. The system utilizes electrophysiological correlates of visual spatial attention mechanisms, the self-regulation of which is naturally developed through continuous application in everyday life. An interface involving real-time biofeedback is described, demonstrating reduced training time in comparison to existing BCIs based on self-regulation paradigms. Subjects were cued to covertly attend to a sequence of letters superimposed on a flicker stimulus in one visual field while ignoring a similar stimulus of a different flicker frequency in the opposite visual field. Classification of left/right spatial attention is achieved by extracting steady-state visual evoked potentials (SSVEPs) elicited by the stimuli. Six out of eleven physically and neurologically healthy subjects demonstrate reliable control in binary decision-making, achieving at least 75% correct selections in at least one of only five sessions, each of approximately 12-min duration. The highest-performing subject achieved over 90% correct selections in each of four sessions. This independent BCI may provide a new method of real-time interaction for those with little or no peripheral control, with the added advantage of requiring only brief training.

Adult↗

PubMed2005

A parametric feature extraction and classification strategy for brain-computer interfacing.

Parametric modeling strategies are explored in conjunction with linear discriminant analysis for use in an electroencephalogram (EEG)-based brain-computer interface (BCI). A left/right self-paced typing exercise is analyzed by extending the usual autoregressive (AR) model for EEG feature extraction with an AR with exogenous input (ARX) model for combined filtering and feature extraction. The ensemble averaged Bereitschafts potential (an event related potential preceding the onset of movement) forms the exogenous signal input to the ARX model. Based on trials with six subjects, the ARX case of modeling both the signal and noise was found to be considerably more effective than modeling the noise alone (common in BCI systems) with the AR method yielding a classification accuracy of 52.8+/-4.8% and the ARX method an accuracy of 79.1+/-3.9 % across subjects. The results suggest a role for ARX-based feature extraction in BCIs based on evoked and event-related potentials.

Adult↗

PubMed2005

Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication.

The steady-state visual evoked potential (SSVEP) has been employed successfully in brain-computer interface (BCI) research, but its use in a design entirely independent of eye movement has until recently not been reported. This paper presents strong evidence suggesting that the SSVEP can be used as an electrophysiological correlate of visual spatial attention that may be harnessed on its own or in conjunction with other correlates to achieve control in an independent BCI. In this study, 64-channel electroencephalography data were recorded from subjects who covertly attended to one of two bilateral flicker stimuli with superimposed letter sequences. Offline classification of left/right spatial attention was attempted by extracting SSVEPs at optimal channels selected for each subject on the basis of the scalp distribution of SSVEP magnitudes. This yielded an average accuracy of approximately 71% across ten subjects (highest 86%) comparable across two separate cases in which flicker frequencies were set within and outside the alpha range respectively. Further, combining SSVEP features with attention-dependent parieto-occipital alpha band modulations resulted in an average accuracy of 79% (highest 87%).

Adult↗

PubMed2004

Behavioural and physiological impairments of sustained attention after traumatic brain injury.

Sustaining attention under conditions of low external demand taxes our ability to stay on task and to avoid more appealing trains of thought or environmental distractions. By contrast, a stimulating, novel environment engages attention far more freely without the subjective feeling of having to override monotony. Our ability to maintain a goal-directed focus without support from the environment requires the endogenous control of behaviour. This control can be modulated by fronto-parietal circuits and this ability is compromised following traumatic brain injury (TBI) leading to increased lapses of attention. In this paper, we further explore a laboratory paradigm that we argue is particularly sensitive to sustained attention as opposed to other aspects of attentional control involving the selection and management of goals in working memory. The paradigm (fixed sequence Sustained Attention to Response Task--SARTfixed) involves withholding a key press to an infrequent no-go target embedded within a predictable sequence of numbers. We demonstrate that TBI patients in this study make disproportionately more errors than controls on this task. An analysis of response times (RTs) and EEG alpha power across the task demonstrates group differences preceding the critical no-go trial. Controls demonstrate a lengthening of RTs accompanied by desynchronization of power within the alpha band (approximately 10 Hz) preceding the no-go trial. Conversely, the TBI group showed a shortening of RTs during this period with no evidence of alpha desynchronization. These findings suggest that TBI patients may have dysfunctional alpha generators as a consequence of their injury that impairs endogenous control during the task.

Adult↗

PubMed2004

Automatic classification of heartbeats using ECG morphology and heartbeat interval features.

A method for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats is presented. The method allocates manually detected heartbeats to one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard, i.e., normal beat, ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB), fusion of a normal and a VEB, or unknown beat type. Data was obtained from the 44 nonpacemaker recordings of the MIT-BIH arrhythmia database. The data was split into two datasets with each dataset containing approximately 50,000 beats from 22 recordings. The first dataset was used to select a classifier configuration from candidate configurations. Twelve configurations processing feature sets derived from two ECG leads were compared. Feature sets were based on ECG morphology, heartbeat intervals, and RR-intervals. All configurations adopted a statistical classifier model utilizing supervised learning. The second dataset was used to provide an independent performance assessment of the selected configuration. This assessment resulted in a sensitivity of 75.9%, a positive predictivity of 38.5%, and a false positive rate of 4.7% for the SVEB class. For the VEB class, the sensitivity was 77.7%, the positive predictivity was 81.9%, and the false positive rate was 1.2%. These results are an improvement on previously reported results for automated heartbeat classification systems.

Algorithms↗