Medicine (RMH) - Research Publications
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ItemNo Preview AvailableDeep learning for automated epileptiform discharge detection from scalp EEG: A systematic review(IOP Publishing Ltd, 2022-10-01)Automated interictal epileptiform discharge (IED) detection has been widely studied, with machine learning methods at the forefront in recent years. As computational resources become more accessible, researchers have applied deep learning (DL) to IED detection with promising results. This systematic review aims to provide an overview of the current DL approaches to automated IED detection from scalp electroencephalography (EEG) and establish recommendations for the clinical research community. We conduct a systematic review according to the PRISMA guidelines. We searched for studies published between 2012 and 2022 implementing DL for automating IED detection from scalp EEG in major medical and engineering databases. We highlight trends and formulate recommendations for the research community by analyzing various aspects: data properties, preprocessing methods, DL architectures, evaluation metrics and results, and reproducibility. The search yielded 66 studies, and 23 met our inclusion criteria. There were two main DL networks, convolutional neural networks in 14 studies and long short-term memory networks in three studies. A hybrid approach combining a hidden Markov model with an autoencoder was employed in one study. Graph convolutional network was seen in one study, which considered a montage as a graph. All DL models involved supervised learning. The median number of layers was 9 (IQR: 5-21). The median number of IEDs was 11 631 (IQR: 2663-16 402). Only six studies acquired data from multiple clinical centers. AUC was the most reported metric (median: 0.94; IQR: 0.94-0.96). The application of DL to IED detection is still limited and lacks standardization in data collection, multi-center testing, and reporting of clinically relevant metrics (i.e. F1, AUCPR, and false-positive/minute). However, the performance is promising, suggesting that DL might be a helpful approach. Further testing on multiple datasets from different clinical centers is required to confirm the generalizability of these methods.
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ItemNo Preview AvailableDiagnostic delay in focal epilepsy: Association with brain pathology and age(W B SAUNDERS CO LTD, 2022-03)PURPOSE: Between 16-77% of patients with newly diagnosed epilepsy report seizures before diagnosis but little is known about the risk factors for diagnostic delay. Here, we examined the association between prior seizures and neuroimaging findings in newly diagnosed focal epilepsy. METHODS: Adults diagnosed with focal epilepsy at First Seizure Clinics (FSC) at the Royal Melbourne Hospital or Austin Health, Melbourne, Australia, between 2000 and 2010 were included. Medical records were audited for seizure history accrued from the detailed FSC interview. Potentially epileptogenic brain abnormality type, location and extent was determined from neuroimaging. Statistical analysis comprised multivariate logistic regression. RESULTS: Of 735 patients, 44% reported seizure/s before the index seizure. Among the 260 individuals with a potentially epileptogenic brain imaging abnormality, 34% reported prior seizures. Of 475 individuals with no abnormality, 50% reported prior seizures (p < 0.001). Patients with post-stroke changes had lower odds of prior seizures (n = 24/95, OR 0.5, p = 0.005) compared to patients without abnormalities, as did patients with high-grade tumors (n = 1/10, OR 0.1, p = 0.04). Abnormality location or extent was not associated with seizures. Prior seizures were inversely associated with age, patients aged >50 years had lower odds compared to those 18-30 years (OR 0.5, p = 0.01). CONCLUSIONS: A history of prior seizures is less common in patients with newly diagnosed focal epilepsy associated with antecedent stroke or high-grade tumor than in those without a lesion, and is also less common in older individuals. These findings may be related to age, biological mechanisms or aspects of diagnosis and assessment of these events.
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ItemInterictal EEG and ECG for SUDEP Risk Assessment: A Retrospective Multicenter Cohort Study(FRONTIERS MEDIA SA, 2022-03-18)OBJECTIVE: Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Although lots of effort has been made in identifying clinical risk factors for SUDEP in the literature, there are few validated methods to predict individual SUDEP risk. Prolonged postictal EEG suppression (PGES) is a potential SUDEP biomarker, but its occurrence is infrequent and requires epilepsy monitoring unit admission. We use machine learning methods to examine SUDEP risk using interictal EEG and ECG recordings from SUDEP cases and matched living epilepsy controls. METHODS: This multicenter, retrospective, cohort study examined interictal EEG and ECG recordings from 30 SUDEP cases and 58 age-matched living epilepsy patient controls. We trained machine learning models with interictal EEG and ECG features to predict the retrospective SUDEP risk for each patient. We assessed cross-validated classification accuracy and the area under the receiver operating characteristic (AUC) curve. RESULTS: The logistic regression (LR) classifier produced the overall best performance, outperforming the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). Among the 30 patients with SUDEP [14 females; mean age (SD), 31 (8.47) years] and 58 living epilepsy controls [26 females (43%); mean age (SD) 31 (8.5) years], the LR model achieved the median AUC of 0.77 [interquartile range (IQR), 0.73-0.80] in five-fold cross-validation using interictal alpha and low gamma power ratio of the EEG and heart rate variability (HRV) features extracted from the ECG. The LR model achieved the mean AUC of 0.79 in leave-one-center-out prediction. CONCLUSIONS: Our results support that machine learning-driven models may quantify SUDEP risk for epilepsy patients, future refinements in our model may help predict individualized SUDEP risk and help clinicians correlate predictive scores with the clinical data. Low-cost and noninvasive interictal biomarkers of SUDEP risk may help clinicians to identify high-risk patients and initiate preventive strategies.
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ItemAssociation Between Psychiatric Comorbidities and Mortality in Epilepsy(LIPPINCOTT WILLIAMS & WILKINS, 2021-10)OBJECTIVE: To explore the impact of psychiatric comorbidities on all-cause mortality in adults with epilepsy from a cohort of patients admitted for video-EEG monitoring (VEM) over 2 decades. METHODS: A retrospective medical record audit was conducted on 2,709 adults admitted for VEM and diagnosed with epilepsy at 3 Victorian comprehensive epilepsy programs from 1995 to 2015. A total of 1,805 patients were identified in whom the record of a clinical evaluation by a neuropsychiatrist was available, excluding 27 patients who died of a malignant brain tumor known at the time of VEM admission. Epilepsy and lifetime psychiatric diagnoses were determined from consensus opinion of epileptologists and neuropsychiatrists involved in the care of each patient. Mortality and cause of death were determined by linkage to the Australian National Death Index and National Coronial Information System. RESULTS: Compared with the general population, mortality was higher in people with epilepsy (PWE) with a psychiatric illness (standardized mortality ratio [SMR] 3.6) and without a psychiatric illness (SMR 2.5). PWE with a psychiatric illness had greater mortality compared with PWE without (hazard ratio 1.41, 95% confidence interval 1.02-1.97) after adjusting for age and sex. No single psychiatric disorder by itself conferred increased mortality in PWE. The distribution of causes of death remained similar between PWE with psychiatric comorbidities and those without. CONCLUSION: The presence of comorbid psychiatric disorders in adults with epilepsy is associated with increased mortality, highlighting the importance of identifying and treating psychiatric comorbidities in these patients.
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ItemInflammation, ictogenesis, and epileptogenesis: An exploration through human disease(WILEY, 2021-02)Epilepsy is seen historically as a disease of aberrant neuronal signaling manifesting as seizures. With the discovery of numerous auto-antibodies and the subsequent growth in understanding of autoimmune encephalitis, there has been an increasing emphasis on the contribution of the innate and adaptive immune system to ictogenesis and epileptogenesis. Pathogenic antibodies, complement activation, CD8+ cytotoxic T cells, and microglial activation are seen, to various degrees, in different seizure-associated neuroinflammatory and autoimmune conditions. These aberrant immune responses are thought to cause disruptions in neuronal signaling, generation of acute symptomatic seizures, and, in some cases, the development of long-term autoimmune epilepsy. Although early treatment with immunomodulatory therapies improves outcomes in autoimmune encephalitides and autoimmune epilepsies, patient identification and treatment selection are not always clear-cut. This review examines the role of the different components of the immune system in various forms of seizure disorders including autoimmune encephalitis, autoimmune epilepsy, Rasmussen encephalitis, febrile infection-related epilepsy syndrome (FIRES), and new-onset refractory status epilepticus (NORSE). In particular, the pathophysiology and unique cytokine profiles seen in these disorders and their links with diagnosis, prognosis, and treatment decision-making are discussed.
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ItemAntiepileptic Drug Teratogenicity and De Novo Genetic Variation Load(WILEY, 2020-06)OBJECTIVE: The mechanisms by which antiepileptic drugs (AEDs) cause birth defects (BDs) are unknown. Data suggest that AED-induced BDs may result from a genome-wide increase of de novo variants in the embryo, a mechanism that we investigated. METHODS: Whole exome sequencing data from child-parent trios were interrogated for de novo single-nucleotide variants/indels (dnSNVs/indels) and de novo copy number variants (dnCNVs). Generalized linear models were applied to assess de novo variant burdens in children exposed prenatally to AEDs (AED-exposed children) versus children without BDs not exposed prenatally to AEDs (AED-unexposed unaffected children), and AED-exposed children with BDs versus those without BDs, adjusting for confounders. Fisher exact test was used to compare categorical data. RESULTS: Sixty-seven child-parent trios were included: 10 with AED-exposed children with BDs, 46 with AED-exposed unaffected children, and 11 with AED-unexposed unaffected children. The dnSNV/indel burden did not differ between AED-exposed children and AED-unexposed unaffected children (median dnSNV/indel number/child [range] = 3 [0-7] vs 3 [1-5], p = 0.50). Among AED-exposed children, there were no significant differences between those with BDs and those unaffected. Likely deleterious dnSNVs/indels were detected in 9 of 67 (13%) children, none of whom had BDs. The proportion of cases harboring likely deleterious dnSNVs/indels did not differ significantly between AED-unexposed and AED-exposed children. The dnCNV burden was not associated with AED exposure or birth outcome. INTERPRETATION: Our study indicates that prenatal AED exposure does not increase the burden of de novo variants, and that this mechanism is not a major contributor to AED-induced BDs. These results can be incorporated in routine patient counseling. ANN NEUROL 2020;87:897-906.
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ItemContinuous EEG use and status epilepticus treatment in Australasia: a practice survey of Australian and New Zealand epileptologists(BMJ PUBLISHING GROUP, 2020-12)OBJECTIVE: Continuous electroencephalography (cEEG) is increasingly used to detect non-convulsive seizures in critically ill patients but is not widely practised in Australasia. Use of cEEG is also influencing the management of status epilepticus (SE), which is rapidly evolving. We aimed to survey Australian and New Zealand cEEG use and current treatment of SE. METHODS: A web-based survey was distributed to Epilepsy Society of Australia (ESA) members, between October and November 2019. Adult and paediatric neurologists/epileptologists with ESA membership involved in clinical epilepsy care and cEEG interpretation were invited to participate. RESULTS: Thirty-five paediatric/adult epileptologists completed the survey, 51% with over 10 years of consultant experience. cEEG was always available for only 31% of respondents, with the majority having no or only ad hoc access to cEEG. Lack of funding (74%) and personnel (71%) were the most common barriers to performing cEEG. Although experience with SE was common, responses varied regarding treatment approaches for both convulsive and non-convulsive SE. Escalation to anaesthetic treatment of convulsive SE tended to occur later than international guideline recommendations. There was general agreement that formal training in cEEG and national guidelines for SE/cEEG were needed. CONCLUSIONS: cEEG availability remains limited in Australia, with lack of funding and resourcing being key commonly identified barriers. Current opinions on the use of cEEG and treatment of SE vary reflecting the complexity of management and a rapidly evolving field. An Australian-based guideline for the management of SE, including the role of cEEG is recommended.
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ItemNewly diagnosed seizures assessed at two established first seizure clinics: Clinic characteristics, investigations, and findings over 11 years(WILEY, 2021-03)OBJECTIVE: 'First seizure' clinics (FSCs) aim to achieve early expert assessment for individuals with possible new-onset epilepsy. These clinics also have substantial potential for research into epilepsy evolution, outcomes, and costs. However, a paucity of FSCs details has implications for interpretation and utilization of this research. METHODS: We reviewed investigation findings over 11 years (2000-2010) from two established independent FSCs at Austin Health (AH) and Royal Melbourne Hospital (RMH), Australia. These adult clinics are in major public hospitals and operate with similar levels of expertise. Organizational differences include screening and dedicated administration at AH. Included were N = 1555 patients diagnosed with new-onset unprovoked seizures/epilepsy (AH n = 901, RMH n = 654). Protocol-driven interviews and investigations had been recorded prospectively and were extracted from medical records for study. RESULTS: Median patient age was 37 (IQR 26-52, range 18-94) years (AH 34 vs RMH 42 years; P < .001). Eighty-six percent of patients attended FSC within three weeks postindex seizure (median AH 12 vs RMH 25 days; P < .01). By their first appointment, 42% had experienced ≥2 seizures. An EEG was obtained within three weeks postindex seizure in 73% of patients, demonstrating epileptiform discharges in 25% (AH 33% vs RMH 15%). Seventy-six percent of patients had an MRI within 6 weeks. Of those with imaging (n = 1500), 19% had potentially epileptogenic abnormalities (RMH 28% vs AH 12%; P < .01). At both sites, changes due to previous stroke/hemorrhage were the commonest lesions, followed by traumatic brain injury. ≥WHO level 1 brain tumors diagnosed at presentation comprised a very small proportion (<1%) at each clinic. At both sites, epilepsy type could be determined in 60% of patients; RMH had more focal and AH more generalized epilepsy diagnoses. SIGNIFICANCE: Differences between the clinics' administrative and screening practices may contribute to differences in investigation findings. Insight into these differences will facilitate interpretation and utilization, and planning of future research.