A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models

We propose a novel Coupled Hidden Markov Model to detect epileptic seizures in multichannel electroencephalography (EEG) data. Our model defines a network of seizure propagation paths to capture both the temporal and spatial evolution of epileptic activity. To address the intractability introduced by the coupled interactions, we derive a variational inference procedure to efficiently infer the seizure evolution from spectral patterns in the EEG data. We validate our model on EEG aquired under clinical conditions in the Epilepsy Monitoring Unit of the Johns Hopkins Hospital. Using 5-fold cross validation, we demonstrate that our model outperforms three baseline approaches which rely on a classical detection framework. Our model also demonstrates the potential to localize seizure onset zones in focal epilepsy.Comment: To appear in MICCAI 2018 Proceeding

Seasonality of presentation and birth in catatonia

BACKGROUND: Catatonia is a neuropsychiatric syndrome associated with both psychiatric disorders and medical conditions. Understanding of the pathophysiology of catatonia remains limited, and the role of the environment is unclear. Although seasonal variations have been shown for many of the disorders underlying catatonia, the seasonality of this syndrome has not yet been adequately explored. METHODS: Clinical records were screened to identify a cohort of patients suffering from catatonia and a control group of psychiatric inpatients, from 2007 to 2016 in South London. In a cohort study, the seasonality of presentation was explored fitting regression models with harmonic terms, while the effect of season of birth on subsequent development of catatonia was analyzed using regression models for count data. In a case-control study, the association between month of birth and catatonia was studied fitting logistic regression models. RESULTS: In total, 955 patients suffering from catatonia and 23,409 controls were included. The number of catatonic episodes increased during winter, with a peak in February. Similarly, an increasing number of cases was observed during summer, with a second peak in August. However, no evidence for an association between month of birth and catatonia was found. CONCLUSIONS: The presentation of catatonia showed seasonal variation in accordance with patterns described for many of the disorders underlying catatonia, such as mood disorders and infections. We found no evidence for an association between season of birth and risk of developing catatonia. This may imply that recent triggers may underpin catatonia, rather than distal events

Catatonia: demographic, clinical and laboratory associations

Background: Catatonia, a severe neuropsychiatric syndrome, has few studies of sufficient scale to clarify its epidemiology or pathophysiology. We aimed to characterise demographic associations, peripheral inflammatory markers and outcome of catatonia. / Methods: Electronic healthcare records were searched for validated clinical diagnoses of catatonia. In a case–control study, demographics and inflammatory markers were compared in psychiatric inpatients with and without catatonia. In a cohort study, the two groups were compared in terms of their duration of admission and mortality. / Results: We identified 1456 patients with catatonia (of whom 25.1% had two or more episodes) and 24 956 psychiatric inpatients without catatonia. Incidence was 10.6 episodes of catatonia per 100 000 person-years. Patients with and without catatonia were similar in sex, younger and more likely to be of Black ethnicity. Serum iron was reduced in patients with catatonia [11.6 v. 14.2 μmol/L, odds ratio (OR) 0.65 (95% confidence interval (CI) 0.45–0.95), p = 0.03] and creatine kinase was raised [2545 v. 459 IU/L, OR 1.53 (95% CI 1.29–1.81), p < 0.001], but there was no difference in C-reactive protein or white cell count. N-Methyl-D-aspartate receptor antibodies were significantly associated with catatonia, but there were small numbers of positive results. Duration of hospitalisation was greater in the catatonia group (median: 43 v. 25 days), but there was no difference in mortality after adjustment. / Conclusions: In the largest clinical study of catatonia, we found catatonia occurred in approximately 1 per 10 000 person-years. Evidence for a proinflammatory state was mixed. Catatonia was associated with prolonged inpatient admission but not with increased mortality

Time-Frequency based Feature Selection for Discrimination of non stationary Biosignals.

This research proposes a generic methodology for dimensionality reduction upon time-frequency representations applied to the classification of different types of biosignals. The methodology directly deals with the highly redundant and irrelevant data contained in these representations, combining a first stage of irrelevant data removal by variable selection, with a second stage of redundancy reduction using methods based on linear transformations. The study addresses two techniques that provided a similar performance: the first one is based on the selection of a set of the most relevant time?frequency points, whereas the second one selects the most relevant frequency bands. The first methodology needs a lower quantity of components, leading to a lower feature space; but the second improves the capture of the time-varying dynamics of the signal, and therefore provides a more stable performance. In order to evaluate the generalization capabilities of the methodology proposed it has been applied to two types of biosignals with different kinds of non-stationary behaviors: electroencephalographic and phonocardiographic biosignals. Even when these two databases contain samples with different degrees of complexity and a wide variety of characterizing patterns, the results demonstrate a good accuracy for the detection of pathologies, over 98%.The results open the possibility to extrapolate the methodology to the study of other biosignals

Epileptic Seizure Detection Using a Convolutional Neural Network

The availability of electroencephalogram (EEG) data has opened up the possibility for new interesting applications, such as epileptic seizure detection. The detection of epileptic activity is usually performed by an expert based on the analysis of the EEG data. This paper shows how a convolutional neural network (CNN) can be applied to EEG images for a full and accurate classification. The proposed methodology was applied on images reflecting the amplitude of the EEG data over all electrodes. Two groups are considered: (a) healthy subjects and (b) epileptic subjects. Classification results show that CNN has a potential in the classification of EEG signals, as well as the detection of epileptic seizures by reaching 99.48% of overall classification accuracy

Nonsteroidal anti-inflammatory drug use and Alzheimer's disease risk: the MIRAGE Study

BACKGROUND: Nonsteroidal anti-inflammatory drugs (NSAID) use may protect against Alzheimer's disease (AD) risk. We sought examine the association between NSAID use and risk of AD, and potential effect modification by APOE-ε4 carrier status and ethnicity. METHODS: The MIRAGE Study is a multi-center family study of genetic and environmental risk factors for AD. Subjects comprised 691 AD patients (probands) and 973 family members enrolled at 15 research centers between 1996 and 2002. The primary independent and dependent variables were prior NSAID use and AD case status, respectively. We stratified the dataset in order to evaluate whether the association between NSAID use and AD was similar in APOE-ε4 carriers and non-carriers. Ethnicity was similarly examined as an effect modifier. RESULTS: NSAID use was less frequent in cases compared to controls in the overall sample (adjusted OR = 0.64; 95% CI = 0.38–1.05). The benefit of NSAID use appeared more pronounced among APOE-ε4 carriers (adjusted OR = 0.49; 95% CI = 0.24–0.98) compared to non-carriers, although this association was not statistically significant. The pattern of association was similar in Caucasian and African Americans. CONCLUSIONS: NSAID use is inversely associated with AD and may be modified by APOE genotype. Prospective studies and clinical trials of sufficient power to detect effect modification by APOE-ε4 carrier status are needed

iSupport : a WHO global online intervention for informal caregivers of people with dementia

In 2015, it was estimated that worldwide 47 million people had dementia, increasing to 75 million in 2030 and 132 million by 2050. Nearly 9.9 million people are expected to develop dementia each year, which translates to one new case every three seconds. While dementia occurs across all levels of socioeconomic status, nearly 60% of people with dementia currently live in low‐ and middle‐income countries (LMICs) and most new cases (71%) are expected to occur in those countries. The majority of people with dementia in those countries do not have access to care and support

Epidemiological and cohort study finds no association between COVID-19 and Guillain-Barré syndrome

Reports of Guillain-Barré syndrome (GBS) have emerged during the Coronavirus disease 2019 (COVID-19) pandemic. This epidemiological and cohort study sought to investigate any causative association between COVID-19 infection and GBS. The epidemiology of GBS cases reported to the UK National Immunoglobulin Database was studied from 2016 to 2019 and compared to cases reported during the COVID-19 pandemic. Data were stratified by hospital trust and region, with numbers of reported cases per month. UK population data for COVID-19 infection were collated from UK public health bodies. In parallel, but separately, members of the British Peripheral Nerve Society prospectively reported incident cases of GBS during the pandemic at their hospitals to a central register. The clinical features, investigation findings and outcomes of COVID-19 (definite or probable) and non-COVID-19 associated GBS cases in this cohort were compared. The incidence of GBS treated in UK hospitals from 2016 to 2019 was 1.65–1.88 per 100 000 individuals per year. GBS incidence fell between March and May 2020 compared to the same months of 2016–19. GBS and COVID-19 incidences during the pandemic also varied between regions and did not correlate with one another (r = 0.06, 95% confidence interval: −0.56 to 0.63, P = 0.86). In the independent cohort study, 47 GBS cases were reported (COVID-19 status: 13 definite, 12 probable, 22 non-COVID-19). There were no significant differences in the pattern of weakness, time to nadir, neurophysiology, CSF findings or outcome between these groups. Intubation was more frequent in the COVID-19 affected cohort (7/13, 54% versus 5/22, 23% in COVID-19-negative) attributed to COVID-19 pulmonary involvement. Although it is not possible to entirely rule out the possibility of a link, this study finds no epidemiological or phenotypic clues of SARS-CoV-2 being causative of GBS. GBS incidence has fallen during the pandemic, which may be the influence of lockdown measures reducing transmission of GBS inducing pathogens such as Campylobacter jejuni and respiratory viruses

Simvastatin is associated with a reduced incidence of dementia and Parkinson's disease

<p>Abstract</p> <p>Background</p> <p>Statins are a class of medications that reduce cholesterol by inhibiting 3-hydroxy-3-methylglutaryl-coenzyme A reductase. Whether statins can benefit patients with dementia remains unclear because of conflicting results. We hypothesized that some of the confusion in the literature might arise from differences in efficacy of different statins. We used a large database to compare the action of several different statins to investigate whether some statins might be differentially associated with a reduction in the incidence of dementia and Parkinson's disease.</p> <p>Methods</p> <p>We analyzed data from the decision support system of the US Veterans Affairs database, which contains diagnostic, medication and demographic information on 4.5 million subjects. The association of lovastatin, simvastatin and atorvastatin with dementia was examined with Cox proportional hazard models for subjects taking statins compared with subjects taking cardiovascular medications other than statins, after adjusting for covariates associated with dementia or Parkinson's disease.</p> <p>Results</p> <p>We observed that simvastatin is associated with a significant reduction in the incidence of dementia in subjects ≥65 years, using any of three models. The first model incorporated adjustment for age, the second model included adjusted for three known risk factors for dementia, hypertension, cardiovascular disease or diabetes, and the third model incorporated adjustment for the Charlson index, which is an index that provides a broad assessment of chronic disease. Data were obtained for over 700000 subjects taking simvastatin and over 50000 subjects taking atorvastatin who were aged >64 years. Using model 3, the hazard ratio for incident dementia for simvastatin and atorvastatin are 0.46 (CI 0.44–0.48, <it>p </it>< 0.0001) and 0.91 (CI 0.80–1.02, <it>p </it>= 0.11), respectively. Lovastatin was not associated with a reduction in the incidence of dementia. Simvastatin also exhibited a reduced hazard ratio for newly acquired Parkinson's disease (HR 0.51, CI 0.4–0.55, <it>p </it>< 0.0001).</p> <p>Conclusion</p> <p>Simvastatin is associated with a strong reduction in the incidence of dementia and Parkinson's disease, whereas atorvastatin is associated with a modest reduction in incident dementia and Parkinson's disease, which shows only a trend towards significance.</p