Biomedical Engineering - Research Publications

Permanent URI for this collection

Search Results

Now showing 1 - 10 of 17
  • Item
    No Preview Available
    Model Parameter Estimation As Features to Predict the Duration of Epileptic Seizures From Onset.
    Liu, Y ; Xia, S ; Soto-Breceda, A ; Karoly, P ; Cook, MJ ; Grayden, DB ; Schmidt, D ; Kuhlmann, L (IEEE, 2023-07)
    The durations of epileptic seizures are linked to severity and risk for patients. It is unclear if the spatiotemporal evolution of a seizure has any relationship with its duration. Understanding such mechanisms may help reveal treatments for reducing the duration of a seizure. Here, we present a novel method to predict whether a seizure is going to be short or long at its onset using features that can be interpreted in the parameter space of a brain model. The parameters of a Jansen-Rit neural mass model were tracked given intracranial electroencephalography (iEEG) signals, and were processed as time series features using MINIROCKET. By analysing 2954 seizures from 10 patients, patient-specific classifiers were built to predict if a seizure would be short or long given 7 s of iEEG at seizure onset. The method achieved an area under the receiver operating characteristic curve (AUC) greater than 0.6 for five of 10 patients. The behaviour in the parameter space has shown different mechanisms are associated with short/long seizures.Clinical relevance-This shows that it is possible to classify whether a seizure will be short or long based on its early characteristics. Timely interventions and treatments can be applied if the duration of the seizures can be predicted.
  • Item
    No Preview Available
    Sleep and seizure risk in epilepsy: bed and wake times are more important than sleep duration
    Stirling, RE ; Hidajat, CM ; Grayden, DB ; D'Souza, WJ ; Naim-Feil, J ; Dell, KL ; Schneider, LD ; Nurse, E ; Freestone, D ; Cook, MJ ; Karoly, PJ (OXFORD UNIV PRESS, 2023-07-03)
    Sleep duration, sleep deprivation and the sleep-wake cycle are thought to play an important role in the generation of epileptic activity and may also influence seizure risk. Hence, people diagnosed with epilepsy are commonly asked to maintain consistent sleep routines. However, emerging evidence paints a more nuanced picture of the relationship between seizures and sleep, with bidirectional effects between changes in sleep and seizure risk in addition to modulation by sleep stages and transitions between stages. We conducted a longitudinal study investigating sleep parameters and self-reported seizure occurrence in an ambulatory at-home setting using mobile and wearable monitoring. Sixty subjects wore a Fitbit smartwatch for at least 28 days while reporting their seizure activity in a mobile app. Multiple sleep features were investigated, including duration, oversleep and undersleep, and sleep onset and offset times. Sleep features in participants with epilepsy were compared to a large (n = 37 921) representative population of Fitbit users, each with 28 days of data. For participants with at least 10 seizure days (n = 34), sleep features were analysed for significant changes prior to seizure days. A total of 4956 reported seizures (mean = 83, standard deviation = 130) and 30 485 recorded sleep nights (mean = 508, standard deviation = 445) were included in the study. There was a trend for participants with epilepsy to sleep longer than the general population, although this difference was not significant. Just 5 of 34 participants showed a significant difference in sleep duration the night before seizure days compared to seizure-free days. However, 14 of 34 subjects showed significant differences between their sleep onset (bed) and/or offset (wake) times before seizure occurrence. In contrast to previous studies, the current study found undersleeping was associated with a marginal 2% decrease in seizure risk in the following 48 h (P < 0.01). Nocturnal seizures were associated with both significantly longer sleep durations and increased risk of a seizure occurring in the following 48 h. Overall, the presented results demonstrated that day-to-day changes in sleep duration had a minimal effect on reported seizures, while patient-specific changes in bed and wake times were more important for identifying seizure risk the following day. Nocturnal seizures were the only factor that significantly increased the risk of seizures in the following 48 h on a group level. Wearables can be used to identify these sleep-seizure relationships and guide clinical recommendations or improve seizure forecasting algorithms.
  • Item
    No Preview Available
    Brain model state space reconstruction using an LSTM neural network
    Liu, Y ; Soto-Breceda, A ; Karoly, P ; Grayden, DB ; Zhao, Y ; Cook, MJ ; Schmidt, D ; Kuhlmann, L (IOP Publishing Ltd, 2023-06-01)
    Objective. Kalman filtering has previously been applied to track neural model states and parameters, particularly at the scale relevant to electroencephalography (EEG). However, this approach lacks a reliable method to determine the initial filter conditions and assumes that the distribution of states remains Gaussian. This study presents an alternative, data-driven method to track the states and parameters of neural mass models (NMMs) from EEG recordings using deep learning techniques, specifically a long short-term memory (LSTM) neural network.Approach. An LSTM filter was trained on simulated EEG data generated by a NMM using a wide range of parameters. With an appropriately customised loss function, the LSTM filter can learn the behaviour of NMMs. As a result, it can output the state vector and parameters of NMMs given observation data as the input.Main results. Test results using simulated data yielded correlations withRsquared of around 0.99 and verified that the method is robust to noise and can be more accurate than a nonlinear Kalman filter when the initial conditions of the Kalman filter are not accurate. As an example of real-world application, the LSTM filter was also applied to real EEG data that included epileptic seizures, and revealed changes in connectivity strength parameters at the beginnings of seizures.Significance. Tracking the state vector and parameters of mathematical brain models is of great importance in the area of brain modelling, monitoring, imaging and control. This approach has no need to specify the initial state vector and parameters, which is very difficult to do in practice because many of the variables being estimated cannot be measured directly in physiological experiments. This method may be applied using any NMM and, therefore, provides a general, novel, efficient approach to estimate brain model variables that are often difficult to measure.
  • Item
    No Preview Available
    Ambient air pollution and epileptic seizures: A panel study in Australia
    Chen, Z ; Yu, W ; Xu, R ; Karoly, PJ ; Maturana, M ; Payne, DE ; Li, L ; Nurse, ES ; Freestone, DR ; Li, S ; Burkitt, AN ; Cook, MJ ; Guo, Y ; Grayden, DB (WILEY, 2022-07)
    OBJECTIVE: Emerging evidence has shown that ambient air pollution affects brain health, but little is known about its effect on epileptic seizures. This work aimed to assess the association between daily exposure to ambient air pollution and the risk of epileptic seizures. METHODS: This study used epileptic seizure data from two independent data sources (NeuroVista and Seer App seizure diary). In the NeuroVista data set, 3273 seizures were recorded using intracranial electroencephalography (iEEG) from 15 participants with refractory focal epilepsy in Australia in 2010-2012. In the seizure diary data set, 3419 self-reported seizures were collected through a mobile application from 34 participants with epilepsy in Australia in 2018-2021. Daily average concentrations of carbon monoxide (CO), nitrogen dioxide (NO2 ), ozone (O3 ), particulate matter ≤10 μm in diameter (PM10 ), and sulfur dioxide (SO2 ) were retrieved from the Environment Protection Authority (EPA) based on participants' postcodes. A patient-time-stratified case-crossover design with the conditional Poisson regression model was used to determine the associations between air pollutants and epileptic seizures. RESULTS: A significant association between CO concentrations and epileptic seizure risks was observed, with an increased seizure risk of 4% (relative risk [RR]: 1.04, 95% confidence interval [CI]: 1.01-1.07) for an interquartile range (IQR) increase of CO concentrations (0.13 parts per million), whereas no significant associations were found for the other four air pollutants in the whole study population. Female participants had a significantly increased risk of seizures when exposed to elevated CO and NO2 , with RRs of 1.05 (95% CI: 1.01-1.08) and 1.09 (95% CI: 1.01-1.16), respectively. In addition, a significant association was observed between CO and the risk of subclinical seizures (RR: 1.20, 95% CI: 1.12-1.28). SIGNIFICANCE: Daily exposure to elevated CO concentrations may be associated with an increased risk of epileptic seizures, especially for subclinical seizures.
  • Item
    Thumbnail Image
    Multiday cycles of heart rate are associated with seizure likelihood: An observational cohort study
    Karoly, PJ ; Stirling, RE ; Freestone, DR ; Nurse, ES ; Maturana, M ; Halliday, AJ ; Neal, A ; Gregg, NM ; Brinkmann, BH ; Richardson, MP ; La Gerche, A ; Grayden, DB ; D'Souza, W ; Cook, MJ (ELSEVIER, 2021-10)
    BACKGROUND: 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 has previously been considered in epilepsy research, with potential implications for seizure forecasting, therapy, and mortality (i.e., sudden unexpected death in epilepsy). METHODS: 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. FINDINGS: 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. INTERPRETATION: 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. FUNDING: This research received funding from the Australian Government National Health and Medical Research Council (investigator grant 1178220), the Australian Government BioMedTech Horizons program, and the Epilepsy Foundation of America's 'My Seizure Gauge' grant.
  • Item
    Thumbnail Image
    Seizure Forecasting Using a Novel Sub-Scalp Ultra-Long Term EEG Monitoring System
    Stirling, RE ; Maturana, M ; Karoly, PJ ; Nurse, ES ; McCutcheon, K ; Grayden, DB ; Ringo, SG ; Heasman, JM ; Hoare, RJ ; Lai, A ; D'Souza, W ; Seneviratne, U ; Seiderer, L ; McLean, KJ ; Bulluss, KJ ; Murphy, M ; Brinkmann, BH ; Richardson, MP ; Freestone, DR ; Cook, MJ (FRONTIERS MEDIA SA, 2021-08-23)
    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 utilizing 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 visually 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), which is comparable to the best score in recent, state-of-the-art forecasting work using intracranial EEG.
  • Item
    Thumbnail Image
    Forecasting Seizure Likelihood With Wearable Technology
    Stirling, RE ; Grayden, DB ; D'Souza, W ; Cook, MJ ; Nurse, E ; Freestone, DR ; Payne, DE ; Brinkmann, BH ; Pal Attia, T ; Viana, PF ; Richardson, MP ; Karoly, PJ (FRONTIERS MEDIA SA, 2021-07-15)
    The unpredictability of epileptic seizures exposes people with epilepsy to potential physical harm, restricts day-to-day activities, and impacts mental well-being. Accurate seizure forecasters would reduce the uncertainty associated with seizures but need to be feasible and accessible in the long-term. Wearable devices are perfect candidates to develop non-invasive, accessible forecasts but are yet to be investigated in long-term studies. We hypothesized that machine learning models could utilize heart rate as a biomarker for well-established cycles of seizures and epileptic activity, in addition to other wearable signals, to forecast high and low risk seizure periods. This feasibility study tracked participants' (n = 11) heart rates, sleep, and step counts using wearable smartwatches and seizure occurrence using smartphone seizure diaries for at least 6 months (mean = 14.6 months, SD = 3.8 months). Eligible participants had a diagnosis of refractory epilepsy and reported at least 20 seizures (mean = 135, SD = 123) during the recording period. An ensembled machine learning and neural network model estimated seizure risk either daily or hourly, with retraining occurring on a weekly basis as additional data was collected. Performance was evaluated retrospectively against a rate-matched random forecast using the area under the receiver operating curve. A pseudo-prospective evaluation was also conducted on a held-out dataset. Of the 11 participants, seizures were predicted above chance in all (100%) participants using an hourly forecast and in ten (91%) participants using a daily forecast. The average time spent in high risk (prediction time) before a seizure occurred was 37 min in the hourly forecast and 3 days in the daily forecast. Cyclic features added the most predictive value to the forecasts, particularly circadian and multiday heart rate cycles. Wearable devices can be used to produce patient-specific seizure forecasts, particularly when biomarkers of seizure and epileptic activity cycles are utilized.
  • Item
    Thumbnail Image
    Ensembling crowdsourced seizure prediction algorithms using long-term human intracranial EEG
    Reuben, C ; Karoly, P ; Freestone, DR ; Temko, A ; Barachant, A ; Li, F ; Titericz, G ; Lang, BW ; Lavery, D ; Roman, K ; Broadhead, D ; Jones, G ; Tang, Q ; Ivanenko, I ; Panichev, O ; Proix, T ; Nahlik, M ; Grunberg, DB ; Grayden, DB ; Cook, MJ ; Kuhlmann, L (Wiley, 2020-02)
    Seizure prediction is feasible, but greater accuracy is needed to make seizure prediction clinically viable across a large group of patients. Recent work crowdsourced state‐of‐the‐art prediction algorithms in a worldwide competition, yielding improvements in seizure prediction performance for patients whose seizures were previously found hard to anticipate. The aim of the current analysis was to explore potential performance improvements using an ensemble of the top competition algorithms. The results suggest that minor increments in performance may be possible; however, the outcomes of statistical testing limit the confidence in these increments. Our results suggest that for the specific algorithms, evaluation framework, and data considered here, incremental improvements are achievable but there may be upper bounds on machine learning–based seizure prediction performance for some patients whose seizures are challenging to predict. Other more tailored approaches that, for example, take into account a deeper understanding of preictal mechanisms, patient‐specific sleep‐wake rhythms, or novel measurement approaches, may still offer further gains for these types of patients.
  • Item
    Thumbnail Image
    Postictal suppression and seizure durations: A patient-specific, long-term iEEG analysis
    Payne, DE ; Karoly, PJ ; Freestone, DR ; Boston, R ; D'Souza, W ; Nurse, E ; Kuhlmann, L ; Cook, MJ ; Grayden, DB (WILEY, 2018-05)
    OBJECTIVE: We report on patient-specific durations of postictal periods in long-term intracranial electroencephalography (iEEG) recordings. The objective was to investigate the relationship between seizure duration and postictal suppression duration. METHODS: Long-term recording iEEG from 9 patients (>50 seizures recorded) were analyzed. In total, 2310 seizures were recorded during a total of 13.8 years of recording. Postictal suppression duration was calculated as the duration after seizure termination until total signal energy returned to background levels. The relationship between seizure duration and postictal suppression duration was quantified using the correlation coefficient (r). The effects of populations of seizures within patients, on correlations, were also considered. Populations of seizures within patients were distinguished by seizure duration thresholds and k-means clustering along the dimensions of seizure duration and postictal suppression duration. The effects of bursts of seizures were also considered by defining populations based on interseizure interval (ISI). RESULTS: Seizure duration accounted for 40% of postictal suppression duration variance, aggregated across all patients and seizures. Seizure duration accounted for more than 25% of the variance in postictal suppression duration in 2 patients and accounted for less than 25% in the remaining 7. In 3 patients, heat maps showed multiple distinct postictal patterns indicating multiple populations of seizures. When accounting for these populations, seizure duration accounted for less than 25% of the variance in postictal duration in all populations. Variance in postictal suppression duration accounted for less than 10% of ISI variance in all patients. SIGNIFICANCE: We have previously demonstrated that some patients have multiple seizure populations distinguishable by seizure duration. This article shows that different seizure populations have distinct and consistent postictal behaviors. The existence of multiple populations in some patients has implications for seizure management and forecasting, whereas the distinct postictal behaviors may have implications for sudden unexpected death in epilepsy (SUDEP) prediction and prevention.
  • Item
    Thumbnail Image
    Identifying seizure risk factors: A comparison of sleep, weather, and temporal features using a Bayesian forecast
    Payne, DE ; Dell, KL ; Karoly, PJ ; Kremen, V ; Gerla, V ; Kuhlmann, L ; Worrell, GA ; Cook, MJ ; Grayden, DB ; Freestone, DR (WILEY, 2021-02)
    OBJECTIVE: 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. METHODS: 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 individual 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. RESULTS: 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 individual feature. SIGNIFICANCE: 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 individual'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.