ORCID Profile
0000-0003-2411-5358
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Central Nervous System | Pattern Recognition and Data Mining | Medical Devices | Artificial Intelligence and Image Processing
Expanding Knowledge in the Information and Computing Sciences | Health and Support Services not elsewhere classified |
Publisher: Elsevier BV
Date: 09-2015
DOI: 10.1016/J.NEUROIMAGE.2015.06.048
Abstract: This paper provides a new method for model-based estimation of intra-cortical connectivity from electrophysiological measurements. A novel closed-form solution for the connectivity function of the Amari neural field equations is derived as a function of electrophysiological observations. The resultant intra-cortical connectivity estimate is driven from experimental data, but constrained by the mesoscopic neurodynamics that are encoded in the computational model. A demonstration is provided to show how the method can be used to image physiological mechanisms that govern cortical dynamics, which are normally hidden in clinical data from epilepsy patients. Accurate estimation performance is demonstrated using synthetic data. Following the computational testing, results from patient data are obtained that indicate a dominant increase in surround inhibition prior to seizure onset that subsides in the cases when the seizures spread.
Publisher: Elsevier BV
Date: 06-2011
DOI: 10.1016/J.NEUROIMAGE.2011.02.027
Abstract: This paper presents a framework for creating neural field models from electrophysiological data. The Wilson and Cowan or Amari style neural field equations are used to form a parametric model, where the parameters are estimated from data. To illustrate the estimation framework, data is generated using the neural field equations incorporating modeled sensors enabling a comparison between the estimated and true parameters. To facilitate state and parameter estimation, we introduce a method to reduce the continuum neural field model using a basis function decomposition to form a finite-dimensional state-space model. Spatial frequency analysis methods are introduced that systematically specify the basis function configuration required to capture the dominant characteristics of the neural field. The estimation procedure consists of a two-stage iterative algorithm incorporating the unscented Rauch-Tung-Striebel smoother for state estimation and a least squares algorithm for parameter estimation. The results show that it is theoretically possible to reconstruct the neural field and estimate intracortical connectivity structure and synaptic dynamics with the proposed framework.
Publisher: IEEE
Date: 09-2009
Publisher: Wiley
Date: 30-12-2020
DOI: 10.1111/EPI.16785
Abstract: Most seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors, such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient‐specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts. This study performed post hoc analysis on data from eight patients with a total of 12.2 years of continuous intracranial electroencephalographic recordings (average = 1.5 years, range = 1.0–2.1 years), originally collected in a prospective trial. Patients also had sleep scoring and location‐specific weather data. Histograms of future seizure likelihood were generated for each feature. The predictive utility of in idual features was measured using a Bayesian approach to combine different features into an overall forecast of seizure likelihood. Performance of different feature combinations was compared using the area under the receiver operating curve. Performance evaluation was pseudoprospective. For the eight patients studied, seizures could be predicted above chance accuracy using sleep (five patients), weather (two patients), and temporal features (six patients). Forecasts using combined features performed significantly better than chance in six patients. For four of these patients, combined forecasts outperformed any in idual feature. Environmental and physiological data, including sleep, weather, and temporal features, provide significant predictive information on upcoming seizures. Although forecasts did not perform as well as algorithms that use invasive intracranial electroencephalography, the results were significantly above chance. Complementary signal features derived from an in idual's historic seizure records may provide useful prior information to augment traditional seizure detection or forecasting algorithms. Importantly, many predictive features used in this study can be measured noninvasively.
Publisher: Wiley
Date: 27-03-2020
DOI: 10.1111/EPI.16485
Publisher: Wiley
Date: 31-08-2019
DOI: 10.1111/EPI.16321
Abstract: In idual seizure rates are highly volatile, with large fluctuations from month-to-month. Nevertheless, changes in in idual mean seizure rates are used to measure whether or not trial participants successfully respond to treatment. This study aims to quantify the challenges in identifying in idual treatment responders in epilepsy. A power calculation was performed to determine the trial duration required to detect a significant 50% decrease in seizure rates (P < .05) for in iduals. Seizure rate simulations were also performed to determine the number of people who would appear to be 50% responders by chance. Seizure rate statistics were derived from long-term seizure counts recorded during a previous clinical trial for an implantable seizure monitoring device. We showed that in idual variance in monthly seizure rates can lead to an unacceptably high false-positive rate in the detection of in idual treatment responders. This error rate cannot be reduced by increasing the trial population however, it can be reduced by increasing the duration of clinical trials. This finding suggests that some drugs may be incorrectly evaluated as effective or, conversely, that helpful drugs could be rejected based on 50% response rates. It is important to pursue more nuanced approaches to measuring in idual treatment response, which consider the patient-specific distributions of seizure rates.
Publisher: Wiley
Date: 31-12-2016
DOI: 10.1111/EPI.13291
Abstract: We report on a quantitative analysis of data from a study that acquired continuous long-term ambulatory human electroencephalography (EEG) data over extended periods. The objectives were to examine the seizure duration and interseizure interval (ISI), their relationship to each other, and the effect of these features on the clinical manifestation of events. Chronic ambulatory intracranial EEG data acquired for the purpose of seizure prediction were analyzed and annotated. A detection algorithm identified potential seizure activity, which was manually confirmed. Events were classified as clinically corroborated, electroencephalographically identical but not clinically corroborated, or subclinical. K-means cluster analysis supplemented by finite mixture modeling was used to locate groupings of seizure duration and ISI. Quantitative analyses confirmed well-resolved groups of seizure duration and ISIs, which were either mono-modal or multimodal, and highly subject specific. Subjects with a single population of seizures were linked to improved seizure prediction outcomes. There was a complex relationship between clinically manifest seizures, seizure duration, and interval. These data represent the first opportunity to reliably investigate the statistics of seizure occurrence in a realistic, long-term setting. The presence of distinct duration groups implies that the evolution of seizures follows a predetermined course. Patterns of seizure activity showed considerable variation between in iduals, but were highly predictable within in iduals. This finding indicates seizure dynamics are characterized by subject-specific time scales therefore, temporal distributions of seizures should also be interpreted on an in idual level. Identification of duration and interval subgroups may provide a new avenue for improving seizure prediction.
Publisher: Frontiers Media SA
Date: 28-11-2014
Publisher: Wiley
Date: 16-04-2022
DOI: 10.1111/EPI.17252
Abstract: One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient‐specific seizure forecasting is possible using remote, minimally invasive ultra‐long‐term subcutaneous EEG. We analyzed a two‐center cohort of ultra‐long‐term subcutaneous EEG recordings, including six patients with drug‐resistant focal epilepsy monitored for 46–230 days with median 18 h/day of recorded data, totaling 000 h of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient. Three candidate subject‐specific long short‐term memory network deep learning classifiers were trained offline and pseudoprospectively on preictal (1 h before) and interictal ( day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation. Depending on each architecture, significant forecasting performance was achieved in three to five of six patients, with overall mean area under the receiver operating characteristic curve of .65–.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient‐specific circadian patterns of seizure occurrence. This study demonstrates proof‐of‐principle for the possibility of subject‐specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra‐long‐term at‐home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally invasive EEG.
Publisher: Cold Spring Harbor Laboratory
Date: 28-11-2020
DOI: 10.1101/2020.11.24.20237990
Abstract: Circadian and multiday rhythms are found across many biological systems, including cardiology, endocrinology, neurology, and immunology. In people with epilepsy, epileptic brain activity and seizure occurrence have been found to follow circadian, weekly, and monthly rhythms. Understanding the relationship between these cycles of brain excitability and other physiological systems can provide new insight into the causes of multiday cycles. The brain-heart link is relevant for epilepsy, with implications for seizure forecasting, therapy, and mortality (i.e., sudden unexpected death in epilepsy). We report the results from a non-interventional, observational cohort study, Tracking Seizure Cycles. This study sought to examine multiday cycles of heart rate and seizures in adults with diagnosed uncontrolled epilepsy (N=31) and healthy adult controls (N=15) using wearable smartwatches and mobile seizure diaries over at least four months (M=12.0, SD=5.9 control M=10.6, SD=6.4). Cycles in heart rate were detected using a continuous wavelet transform. Relationships between heart rate cycles and seizure occurrence were measured from the distributions of seizure likelihood with respect to underlying cycle phase. Heart rate cycles were found in all 46 participants (people with epilepsy and healthy controls), with circadian (N=46), about-weekly (N=25) and about-monthly (N=13) rhythms being the most prevalent. Of the participants with epilepsy, 19 people had at least 20 reported seizures, and 10 of these had seizures significantly phase locked to their multiday heart rate cycles. Heart rate cycles showed similarities to multiday epileptic rhythms and may be comodulated with seizure likelihood. The relationship between heart rate and seizures is relevant for epilepsy therapy, including seizure forecasting, and may also have implications for cardiovascular disease. More broadly, understanding the link between multiday cycles in the heart and brain can shed new light on endogenous physiological rhythms in humans.
Publisher: Wiley
Date: 20-04-2023
DOI: 10.1111/EPI.17607
Abstract: The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a erse set of physiological signals, brain activity, and seizure timing. In this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist‐worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter–Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology. Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal. Seizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the in idual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time‐varying approaches to epilepsy care.
Publisher: Cold Spring Harbor Laboratory
Date: 11-05-2021
DOI: 10.1101/2021.05.09.21256558
Abstract: Accurate identification of seizure activity, both clinical and subclinical, has important implications in the management of epilepsy. Accurate recognition of seizure activity is essential for diagnostic, management and forecasting purposes, but patient-reported seizures have been shown to be unreliable. Earlier work has revealed accurate capture of electrographic seizures and forecasting is possible with an implantable intracranial device, but less invasive electroencephalography (EEG) recording systems would be optimal. Here, we present preliminary results of seizure detection and forecasting with a minimally invasive sub-scalp device that continuously records EEG. Five participants with refractory epilepsy who experience at least two clinically identifiable seizures monthly have been implanted with sub-scalp devices (Minder™), providing two channels of data from both hemispheres of the brain. Data is continuously captured via a behind-the-ear system, which also powers the device, and transferred wirelessly to a mobile phone, from where it is accessible remotely via cloud storage. EEG recordings from the sub-scalp device were compared to data recorded from a conventional system during a 1-week ambulatory video-EEG monitoring session. Suspect epileptiform activity (EA) was detected using machine learning algorithms and reviewed by trained neurophysiologists. Seizure forecasting was demonstrated retrospectively by utilising cycles in EA and previous seizure times. The procedures and devices were well tolerated, and no significant complications have been reported. Seizures were accurately identified on the sub-scalp system, as confirmed by periods of concurrent conventional scalp EEG recordings. The data acquired also allowed seizure forecasting to be successfully undertaken. The area under the receiver operating characteristic curve (AUC score) achieved (0.88) is comparable to the best score in recent, state-of-the-art forecasting work using intracranial EEG.
Publisher: Elsevier BV
Date: 10-2010
DOI: 10.1016/J.EPLEPSYRES.2010.07.014
Abstract: This paper evaluates the patient-specific seizure prediction performance of pre-ictal changes in bivariate-synchrony between pairs of intracranial electroencephalographic (iEEG) signals within 15min of a seizure in patients with pharmacoresistant focal epilepsy. Prediction horizons under 15min reduce the durations of warning times and should provide adequate time for a seizure control device to intervene. Long-term continuous iEEG was obtained from 6 patients. The seizure prediction performance was evaluated for all possible channel pairs and for different prediction methods to find the best performing channel pairs and methods for both pre-ictal decreases and increases in synchrony. The different prediction methods involved changes in window duration, signal filtering, thresholding approach, and prediction horizon durations. Performance for each patient, for all seizures, was first compared with an analytical-Poisson-based random predictor. The performance of the top 5% of channel pairs for each patient closely matched the top 5% of analytical-Poisson-based random predictor performance indicating that patient-specific, bivariate-synchrony-based seizure prediction could be random in general (under the assumption that channel-pair prediction times are statistically independent). Analysis of the spatial patterns of performance showed no clear relationship to the seizure onset zone. For each patient the best channel pair showed better performance than Poisson-based random prediction for a selected subset of prediction thresholds. Given the caveats of comparing with this form of random prediction, alarm time surrogates were employed to assess statistical significance of a four-fold out-of-s le cross-validation analysis applied to the best channel-pairs. The cross-validation analysis obtained reasonable testing performance for most patients when performance was compared to random prediction based on alarm time surrogates. The most significant case was a patient whose testing set sensitivity and false positive rate were 0.67±0.09 and 3.04±0.29h(-1), respectively, for decreases in synchrony, an intervention time of 15min and a seizure onset period of 5min. For each testing set for this patient, performance was better than that obtained by random prediction at the significance level of 0.05 (average sensitivity of 0.47±0.05). Moreover, there were 9 seizures in each testing set which gives greater power to this cross-validation result, although the cross-validation was performed on the best channel pair selected by within-s le optimization for all seizures of the patient. Further validation with larger datasets from in idual patients is needed. Improvements in prediction performance should be achievable through investigations of multivariate synchrony combined with non-linear classification methods.
Publisher: Elsevier BV
Date: 10-2012
DOI: 10.1016/J.NEUROSCIENCE.2012.07.015
Abstract: It has been proposed that the underlying epileptic process is mediated by changes in both excitatory and inhibitory circuits leading to the formation of hyper-excitable seizure networks. In this review we aim to shed light on the many physiological factors that modulate excitability within these networks. These factors have been discussed extensively in many reviews each as a separate entity and cannot be extensively covered in a single manuscript. Thus for the purpose of this work in which we aim to bring those factors together to explain how they interact with epilepsy, we only provide brief descriptions. We present reported evidence supporting the existence of the epileptic brain in several states interictal, peri-ictal and ictal, each with distinct excitability features. We then provide an overview of how many physiological factors influence the excitatory/inhibitory balance within the interictal state, where the networks are presumed to be functioning normally. We conclude that these changes result in constantly changing states of cortical excitability in patients with epilepsy.
Publisher: Public Library of Science (PLoS)
Date: 23-01-2013
Publisher: Elsevier BV
Date: 02-2013
DOI: 10.1016/J.NEUROIMAGE.2012.10.039
Abstract: Neural fields are spatially continuous state variables described by integro-differential equations, which are well suited to describe the spatiotemporal evolution of cortical activations on multiple scales. Here we develop a multi-resolution approximation (MRA) framework for the integro-difference equation (IDE) neural field model based on semi-orthogonal cardinal B-spline wavelets. In this way, a flexible framework is created, whereby both macroscopic and microscopic behavior of the system can be represented simultaneously. State and parameter estimation is performed using the expectation maximization (EM) algorithm. A synthetic ex le is provided to demonstrate the framework.
Publisher: Springer Science and Business Media LLC
Date: 24-09-2015
DOI: 10.1007/S11910-015-0596-3
Abstract: This review highlights recent developments in the field of epileptic seizure prediction. We argue that seizure prediction is possible however, most previous attempts have used data with an insufficient amount of information to solve the problem. The review discusses four methods for gaining more information above standard clinical electrophysiological recordings. We first discuss developments in obtaining long-term data that enables better characterisation of signal features and trends. Then, we discuss the usage of electrical stimulation to probe neural circuits to obtain robust information regarding excitability. Following this, we present a review of developments in high-resolution micro-electrode technologies that enable neuroimaging across spatial scales. Finally, we present recent results from data-driven model-based analyses, which enable imaging of seizure generating mechanisms from clinical electrophysiological measurements. It is foreseeable that the field of seizure prediction will shift focus to a more probabilistic forecasting approach leading to improvements in the quality of life for the millions of people who suffer uncontrolled seizures. However, a missing piece of the puzzle is devices to acquire long-term high-quality data. When this void is filled, seizure prediction will become a reality.
Publisher: Elsevier BV
Date: 05-2010
DOI: 10.1016/J.EPLEPSYRES.2010.01.010
Abstract: The aim of this study was to determine the current intensities necessary to elicit three levels of varying EEG and behavioural phenomena with electrical stimulation, and also to determine the consistency of the EEG and behavioural components of the triggered seizures over time. Electrical stimulation of the primary motor/somatosensory cortex was performed in 16 adult rats with multichannel microwire electrode arrays. Stimulation was delivered at a frequency of 60 Hz (1 ms pulse width), for 2 s duration, as biphasic rectangular pulses over four of the eight available electrode pairs. Current intensity thresholds for interruption of normal behaviour, epileptiform afterdischarge (EAD) longer than 5 s and motor seizures with Racine severity greater than 3 were not correlated to time post-surgery. The Racine threshold was shown to be negatively correlated to the EAD duration and Racine severity of seizures elicited in the following sessions. Seizures were reliably generated in rats through cortical stimulation with microwire electrode arrays and these seizures were not shown to be subject to any kindling type effects up to 53 days post-implantation. Both the electrographic duration and behavioural severity of stimulated seizures remained, on average, constant during this experimental period. Approximately one-third of stimulations did not cause observable motor seizures and of those that did result in seizures, forelimb clonus was the most common manifestation and the mean EAD duration was 18.5 s. No damage beyond that caused by surgical implantation of electrodes was observed in the histological analyses of stimulated and non-stimulated tissue. The consistency, duration and severity of seizures within this timeframe make this cortical stimulation model suitable for investigations into novel therapeutic interventions for epilepsy that require a known seizure focus.
Publisher: Elsevier BV
Date: 10-2011
DOI: 10.1016/J.EPLEPSYRES.2011.06.011
Abstract: A modified cortical stimulation model was used to investigate the effects of varying the synchronicity and periodicity of electrical stimuli delivered to multiple pairs of electrodes on seizure initiation. In this model, electrical stimulation of the motor cortex of rats, along four pairs of a microwire electrode array, results in an observable seizure with quantifiable electrographic duration and behavioural severity. Periodic stimuli had a constant inter-stimulus intervals across the two-second stimulus duration, whilst synchronous stimuli consisted of singular biphasic, bipolar pulses delivered to the four pairs of electrodes at precisely the same time for the entire two second stimulation period. In this way four combinations of stimulation were possible periodic/synchronous (P/S), periodic/asynchronous (P/As), aperiodic/synchronous (Ap/S) and aperiodic/asynchronous (Ap/As). All stimulation types were designed with equal pulse width, current intensity and mean frequency of stimulation (60 Hz), standardizing net charge transfer. It was expected that the periodicity of the stimulus would be the primary determinant of seizure initiation and therefore severity and electrographic duration. However, the results showed that significant differences in both severity and duration only occurred when the synchronicity was altered. For periodic stimuli, synchronous delivery increased median seizure duration from 5 s to 13 s and increased median Racine severity from 1 to 3. In the aperiodic case, synchronous stimulus delivery increased median duration from 5.5 s to 11s and resulted in seizures of median severity 3 vs. 0 in the asynchronous case. These findings may have implications for the design of future neurostimulation waveform designs as higher numbers of electrodes and stimulator output channels become available in next generation implants.
Publisher: IEEE
Date: 04-2015
Publisher: IEEE
Date: 08-2011
Publisher: Cold Spring Harbor Laboratory
Date: 21-12-2019
DOI: 10.1101/2019.12.19.19015453
Abstract: Seizure unpredictability is rated as one of the most challenging aspects of living with epilepsy. Seizure likelihood can be influenced by a range of environmental and physiological factors that are difficult to measure and quantify. However, some generalizable patterns have been demonstrated in seizure onset. A majority of people with epilepsy exhibit circadian rhythms in their seizure times and many also show slower, multiday patterns. Seizure cycles can be measured using a range of recording modalities, including self-reported electronic seizure diaries. This study aimed to develop personalized forecasts from a mobile seizure diary app. Forecasts based on circadian and multiday seizure cycles were tested pseudo-prospectively using data from 33 app users (mean of 103 seizures per subject). In idual’s strongest cycles were estimated from their reported seizure times and used to derive the likelihood of future seizures. The forecasting approach was validated using self-reported events and electrographic seizures from the Neurovista dataset, an existing database of long-term electroencephalography that has been widely used to develop forecasting algorithms. The validation dataset showed that forecasts of seizure likelihood based on self-reported cycles were predictive of electrographic seizures. Forecasts using only mobile app diaries allowed users to spend an average of 62.8% of their time in a low-risk state, with 16.6% of their time in a high-risk warning state. On average, 64.5% of seizures occurred during high-risk states and less than 10% of seizures occurred in low-risk states. Seizure diary apps can provide personalized forecasts of seizure likelihood that are accurate and clinically relevant for electrographic seizures. These results have immediate potential for translation to a prospective seizure forecasting trial using a mobile diary app. It is our hope that seizure forecasting apps will one day give people with epilepsy greater confidence in managing their daily activities.
Publisher: Elsevier BV
Date: 04-2016
Publisher: Cold Spring Harbor Laboratory
Date: 07-10-2020
DOI: 10.1101/2020.10.05.20207407
Abstract: Objective: Video-electroencephalography (vEEG) is an important component of epilepsy diagnosis and management. Nevertheless, inpatient vEEG monitoring fails to capture seizures in up to one third of patients during diagnostic and pre-surgical monitoring. We hypothesized that personalized seizure forecasts could be used to optimize the timing of vEEG and improve diagnostic yield. Methods: We used a database of ambulatory vEEG studies to select a cohort with linked electronic seizure diaries of more than 20 reported seizures over at least 8 weeks. The total cohort included 48 participants. Diary seizure times were used to detect in iduals' multi-day cycles and estimate times of high seizure risk. We then compared whether estimated seizure risk was significantly different between diagnostic and non-diagnostic vEEGs, and between vEEG with and without recorded epileptic activity. Results: Estimated seizure risk was significantly higher for diagnostic vEEGs and vEEGs with epileptic activity. Across all cycle strengths, the average time in high risk during vEEG was 29.1% compared with 14% for the diagnostic/non-diagnostic groups and 32% compared to 18% for the epileptic activity/no epileptic activity groups. On average, 62.5% of the cohort showed increased time in high risk during vEEG when epileptic activity was recorded (compared to 28% of the cohort where epileptic activity was not recorded). For diagnostic vEEGs, 50% of the cohort had increased time in high risk, compared to 21.5% for non-diagnostic vEEGs. Significance: This study provides a proof of principle that scheduling monitoring times based on personalized seizure risk forecasts can improve the yield of vEEG. Importantly, forecasts can be developed at low cost from mobile seizure diaries. A simple scheduling tool to improve diagnostic outcomes has the potential to reduce the significant cost and risks associated with delayed or missed diagnosis in epilepsy.
Publisher: IEEE
Date: 08-2014
Publisher: Wiley
Date: 04-05-2022
DOI: 10.1111/EPI.17265
Abstract: This study describes a generalized cross‐patient seizure‐forecasting approach using recurrent neural networks with ultra‐long‐term subcutaneous EEG (sqEEG) recordings. Data from six patients diagnosed with refractory epilepsy and monitored with an sqEEG device were used to develop a generalized algorithm for seizure forecasting using long short‐term memory (LSTM) deep‐learning classifiers. Electrographic seizures were identified by a board‐certified epileptologist. One‐minute data segments were labeled as preictal or interictal based on their relationship to confirmed seizures. Data were separated into training and testing data sets, and to compensate for the unbalanced data ratio in training, noise‐added copies of preictal data segments were generated to expand the training data set. The mean and standard deviation (SD) of the training data were used to normalize all data, preserving the pseudo‐prospective nature of the analysis. Different architecture classifiers were trained and tested using a leave‐one‐patient‐out cross‐validation method, and the area under the receiver‐operating characteristic (ROC) curve (AUC) was used to evaluate the performance classifiers. The importance of each input signal was evaluated using a leave‐one‐signal‐out method with repeated training and testing for each classifier. Cross‐patient classifiers achieved performance significantly better than chance in four of the six patients and an overall mean AUC of 0.602 ± 0.126 (mean ± SD). A time in warning of 37.386% ± 5.006% (mean ± std) and sensitivity of 0.691 ± 0.068 (mean ± std) were observed for patients with better than chance results. Analysis of input channels showed a significant contribution ( p .05) by the Fourier transform of signals channels to overall classifier performance. The relative contribution of input signals varied among patients and architectures, suggesting that the inclusion of all signals contributes to robustness in a cross‐patient classifier. These early results show that it is possible to forecast seizures training with data from different patients using two‐channel ultra‐long‐term sqEEG.
Publisher: Elsevier BV
Date: 09-2023
Publisher: Public Library of Science (PLoS)
Date: 26-06-2015
Publisher: IEEE
Date: 07-2013
Publisher: Proceedings of the National Academy of Sciences
Date: 09-11-2015
Abstract: Dynamic changes of cortical excitability are relevant in both healthy and pathological network dynamics. In epilepsy, pathological changes in excitability commonly underlie the initiation and spread of seizures. Accordingly, the ability to monitor excitability and control its degree is important for adequate clinical care and treatment because classic EEG markers found in epilepsy such as interictal spikes do not reflect seizure propensity and thus excitability. Here, we identify excitability markers and test them on long-term electrocorticogram and EEG recordings. We show that they correlate with more direct excitability measures using external stimulation and allow for real-time excitability monitoring. Our results provide evidence that excitability of cortical networks is reduced by antiepileptic drugs and increases as a function of time awake.
Publisher: Public Library of Science (PLoS)
Date: 27-03-2018
Publisher: World Scientific Pub Co Pte Lt
Date: 04-2011
DOI: 10.1142/S0129065711002717
Abstract: A closed-loop system for the automated detection and control of epileptic seizures was created and tested in three Genetic Absence Epilepsy Rats from Strasbourg (GAERS) rats. In this preliminary study, a set of four EEG features were used to detect seizures and three different electrical stimulation strategies (standard (130 Hz), very high (500 Hz) and ultra high (1000 Hz)) were delivered to terminate seizures. Seizure durations were significantly shorter with all three stimulation strategies when compared to non-stimulated (control) seizures. We used mean seizure duration of epileptiform discharges persisting beyond the end of electrical stimulation as a measure of stimulus efficacy. When compared to the duration of seizures stimulated in the standard approach (7.0 s ± 10.1), both very high and ultra high frequency stimulation strategies were more effective at shortening seizure durations (1.3 ± 2.2 s and 3.5 ± 6.4 s respectively). Further studies are warranted to further understand the mechanisms by which this therapeutic effect may be conveyed, and which of the novel aspects of the very high and ultra high frequency stimulation strategies may have contributed to the improvement in seizure abatement performance when compared to standard electrical stimulation approaches.
Publisher: Public Library of Science (PLoS)
Date: 11-10-2018
Publisher: Elsevier BV
Date: 12-2011
DOI: 10.1016/J.YEBEH.2011.09.005
Abstract: Standard methods for seizure prediction involve passive monitoring of intracranial electroencephalography (iEEG) in order to track the 'state' of the brain. This paper introduces a new method for measuring cortical excitability using an electrical probing stimulus. Electrical probing enables feature extraction in a more robust and controlled manner compared to passively tracking features of iEEG signals. The probing stimuli consist of 100 bi-phasic pulses, delivered every 10 min. Features representing neural excitability are estimated from the iEEG responses to the stimuli. These features include the litude of the electrically evoked potential, the mean phase variance (univariate), and the phase-locking value (bivariate). In one patient, it is shown how the features vary over time in relation to the sleep-wake cycle and an epileptic seizure. For a second patient, it is demonstrated how the features vary with the rate of interictal discharges. In addition, the spatial pattern of increases and decreases in phase synchrony is explored when comparing periods of low and high interictal discharge rates, or sleep and awake states. The results demonstrate a proof-of-principle for the method to be applied in a seizure anticipation framework. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.
Start Date: 06-2018
End Date: 12-2024
Amount: $4,133,659.00
Funder: Australian Research Council
View Funded Activity