Browsing by Author "Viana, Pedro"
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- 230 days of ultra long‐term subcutaneous EEG : seizure cycle analysis and comparison to patient diaryPublication . Viana, Pedro; Duun‐Henriksen, Jonas; Glasstëter, Martin; Dümpelmann, Matthias; Nurse, Ewan S.; Martins, Isabel Pavão; Dumanis, Sonya B.; Schulze‐Bonhage, Andreas; Freestone, Dean R.; Brinkmann, Benjamin H.; Richardson, Mark P.We describe the longest period of subcutaneous EEG (sqEEG) monitoring to date, in a 35-year-old female with refractory epilepsy. Over 230 days, 4791/5520 h of sqEEG were recorded (86%, mean 20.8 [IQR 3.9] hours/day). Using an electronic diary, the patient reported 22 seizures, while automatically-assisted visual sqEEG review detected 32 seizures. There was substantial agreement between days of reported and recorded seizures (Cohen's kappa 0.664), although multiple clustered seizures remained undocumented. Circular statistics identified significant sqEEG seizure cycles at circadian (24-hour) and multidien (5-day) timescales. Electrographic seizure monitoring and analysis of long-term seizure cycles are possible with this neurophysiological tool.
- Feasibility and acceptability of an ultra-long-term at-home EEG monitoring system (EEG@HOME) for people with epilepsyPublication . Biondi, Andrea; Simblett, Sara K.; Viana, Pedro; Laiou, Petroula; Fiori, Anna M.G.; Nurse, Ewan; Schreuder, Martijn; Pal, Deb K.; Richardson, Mark P.Background: Recent technological advancements offer new ways to monitor and manage epilepsy. The adoption of these devices in routine clinical practice will strongly depend on patient acceptability and usability, with their perspectives being crucial. Previous studies provided feedback from patients, but few explored the experience of them using independently multiple devices independently at home. Purpose: The study, assessed through a mixed methods design, the direct experiences of people with epilepsy independently using a non-invasive monitoring system (EEG@HOME) for an extended duration of 6 months, at home. We aimed to investigate factors affecting engagement, gather qualitative insights, and provide recommendations for future home epilepsy monitoring systems. Materials and methods: Adults with epilepsy independently were trained to use a wearable dry EEG system, a wrist-worn device, and a smartphone app for seizure tracking and behaviour monitoring for 6 months at home. Monthly acceptability questionnaires (PSSUQ, SUS) and semi-structured interviews were conducted to explore participant experience. Adherence with the procedure, acceptability scores and systematic thematic analysis of the interviews, focusing on the experience with the procedure, motivation and benefits and opinion about the procedure were assessed. Results: Twelve people with epilepsy took part into the study for an average of 193.8 days (range 61 to 312) with a likelihood of using the system at six months of 83 %. The e-diary and the smartwatch were highly acceptable and preferred to a wearable EEG system (PSSUQ score of 1.9, 1.9, 2.4). Participants showed an acceptable level of adherence with all solutions (Average usage of 63 %, 66 %, 92 %) reporting more difficulties using the EEG twice a day and remembering to complete the daily behavioural questionnaires. Clear information and training, continuous remote support, perceived direct and indirect benefits and the possibility to have a flexible, tailored to daily routine monitoring were defined as key factors to ensure compliance with long-term monitoring systems. Conclusions: EEG@HOME study demonstrated people with epilepsy' interest and ability in active health monitoring using new technologies. Remote training and support enable independent home use of new non-invasive technologies, but to ensure long term acceptability and usability systems will require to be integrated into patients' routines, include healthcare providers, and offer continuous support and personalized feedback.
- InMS: chronic insomnia disorder in multiple sclerosis – a portuguese multicentre study on prevalence, subtypes, associated factors and impact on quality of lifePublication . Viana, Pedro; Rodrigues, Elisabete; Fernandes, Carina; Matas, Andreia; Barreto, Rui; Mendonça, Marcelo; Peralta, Ana; Geraldes, RuthBackground: Sleep may be disrupted in Multiple Sclerosis (MS), but the prevalence of chronic insomnia disorder (CID) using standard diagnostic criteria is unknown. Objectives: To determine the prevalence of CID in an MS population, the frequency of CID subtypes, associated factors and impact on quality of life (QoL). Methods: Multicentre, hospital-based cross-sectional study. An adapted version of the Brief Insomnia Questionnaire was applied to a consecutively recruited MS population. The influence of demographic, MS-related features, fatigue, medical and psychiatric comorbidities, nocturnal symptoms, other sleep disorders, dysfunctional beliefs about sleep in CID was evaluated. The relation between CID and QoL was analysed. Results: Of 206 MS patients, 22.3% fulfilled criteria for CID, with initial insomnia in 11.7%, maintenance insomnia in 11.2% and terminal insomnia in 10.2% of patients. CID was more frequent in female patients, those with nocturnal symptoms, medical comorbidities, higher levels of anxiety, depression and fatigue. Multivariable analysis identified female sex, medical comorbidities, anxiety and fatigue as independent factors for CID. CID patients had a significantly lower self-reported QoL. Conclusions: CID is prevalent in MS patients and associated with psychiatric and medical comorbidities, as well as fatigue. It has a negative impact on QoL.
- Minimum clinical utility standards for wearable seizure detectors: a simulation studyPublication . Goldenholz, Daniel M.; Karoly, Philippa J.; Viana, Pedro; Nurse, Ewan; Loddenkemper, Tobias; Schulze‐Bonhage, Andreas; Vieluf, Solveig; Bruno, Elisa; Nasseri, Mona; Richardson, Mark P.; Brinkmann, Benjamin H.; Westover, M. BrandonObjective: Epilepsy management employs self-reported seizure diaries, despite evidence of seizure underreporting. Wearable and implantable seizure detection devices are now becoming more widely available. There are no clear guidelines about what levels of accuracy are sufficient. This study aimed to simulate clinical use cases and identify the necessary level of accuracy for each. Methods: Using a realistic seizure simulator (CHOCOLATES), a ground truth was produced, which was then sampled to generate signals from simulated seizure detectors of various capabilities. Five use cases were evaluated: (1) randomized clinical trials (RCTs), (2) medication adjustment in clinic, (3) injury prevention, (4) sudden unexpected death in epilepsy (SUDEP) prevention, and (5) treatment of seizure clusters. We considered sensitivity (0%-100%), false alarm rate (FAR; 0-2/day), and device type (external wearable vs. implant) in each scenario. Results: The RCT case was efficient for a wide range of wearable parameters, though implantable devices were preferred. Lower accuracy wearables resulted in subtle changes in the distribution of patients enrolled in RCTs, and therefore higher sensitivity and lower FAR values were preferred. In the clinic case, a wide range of sensitivity, FAR, and device type yielded similar results. For injury prevention, SUDEP prevention, and seizure cluster treatment, each scenario required high sensitivity and yet was minimally influenced by FAR. Significance: The choice of use case is paramount in determining acceptable accuracy levels for a wearable seizure detection device. We offer simulation results for determining and verifying utility for specific use case and specific wearable parameters.
- Non-invasive wearable seizure detection using long–short-term memory networks with transfer learningPublication . Nasseri, Mona; Pal Attia, Tal; Joseph, Boney; Gregg, Nicholas M.; Nurse, Ewan S.; Viana, Pedro; Schulze-Bonhage, Andreas; Dümpelmann, Matthias; Worrell, Gregory; Freestone, Dean R.; Richardson, Mark P.; Brinkmann, Benjamin H.Objective. The detection of seizures using wearable devices would improve epilepsy management, but reliable detection of seizures in an ambulatory environment remains challenging, and current studies lack concurrent validation of seizures using electroencephalography (EEG) data. Approach. An adaptively trained long–short-term memory deep neural network was developed and trained using a modest number of seizure data sets from wrist-worn devices. Transfer learning was used to adapt a classifier that was initially trained on intracranial electroencephalography (iEEG) signals to facilitate classification of non-EEG physiological datasets comprising accelerometry, blood volume pulse, skin electrodermal activity, heart rate, and temperature signals. The algorithm’s performance was assessed with and without pre-training on iEEG signals and transfer learning. To assess the performance of the seizure detection classifier using long-term ambulatory data, wearable devices were used for multiple months with an implanted neurostimulator capable of recording iEEG signals, which provided independent electrographic seizure detections that were reviewed by a board-certified epileptologist. Main results. For 19 motor seizures from 10 in-hospital patients, the algorithm yielded a mean area under curve (AUC), a sensitivity, and an false alarm rate per day (FAR/day) of 0.98, 0.93, and 2.3, respectively. Additionally, for eight seizures with probable motor semiology from two ambulatory patients, the classifier achieved a mean AUC of 0.97 and an FAR of 2.45 events/day at a sensitivity of 0.9. For all seizure types in the ambulatory setting, the classifier had a mean AUC of 0.82 with a sensitivity of 0.47 and an FAR of 7.2 events/day. Significance. The performance of the algorithm was evaluated using motor and non-motor seizures during in-hospital and ambulatory use. The classifier was able to detect multiple types of motor and non-motor seizures, but performed significantly better on motor seizures.
- On the clinical potential of ultra long-term subcutaneous EEG monitoring in epilepsyPublication . Viana, Pedro; Petiz, Maria Isabel Segurado Pavão Martins Catarino; Richardson, Mark Philip
- A peripheral pathway to restless legs syndrome? Clues from familial amyloid polyneuropathyPublication . Teodoro, Tiago; Viana, Pedro; Abreu, Daisy; Conceição, isabel; Peralta, Ana; Ferreira, Joaquim JBackground: The relationship between restless legs syndrome (RLS) and peripheral neuropathy remains unclear. In order to clarify this relationship, we investigated if RLS is increased in familial amyloid polyneuropathy related to transthyretin (TTR-FAP) and investigated factors associated with RLS in this population. Methods: RLS frequency was compared between TTR-FAP patients and controls. Secondly, TTR-FAP patients with and without RLS were compared regarding demographic and clinical characteristics. Results: RLS frequency was significantly increased in TTR-FAP, with 18/98 (18.4%) cases contrasting with 5/104 (4.8%) controls (p-value 0.002). This difference remained significant after adjusting for confounders. In TTR-FAP patients, female sex (p-value 0.037), obesity (p-value 0.036) and weight excess (p-value 0.048) were associated with RLS, contrary to other classical RLS risk factors. Conclusions: RLS frequency is increased in TTR-FAP, thus supporting an association between RLS and neuropathy. This may represent a peripheral pathway in RLS pathogenesis. Furthermore, our results suggest that female sex and obesity/weight excess may be risk factors for RLS development among TTR-FAP patients.
- Quantitative EEG and functional outcome following acute ischemic strokePublication . Bentes, Carla; Peralta, Ana; Viana, Pedro; Martins, Hugo F G; Morgado, Carlos; Casimiro, Carlos; Franco, Ana Catarina; Fonseca, Ana Catarina; Geraldes, Ruth; Canhão, Patrícia; Melo, Teresa Pinho e; Paiva, Teresa; Ferro, JoséObjective: To identify the most accurate quantitative electroencephalographic (qEEG) predictor(s) of unfavorable post-ischemic stroke outcome, and its discriminative capacity compared to already known demographic, clinical and imaging prognostic markers. Methods: Prospective cohort of 151 consecutive anterior circulation ischemic stroke patients followed for 12 months. EEG was recorded within 72 h and at discharge or 7 days post-stroke. QEEG (global band power, symmetry, affected/unaffected hemisphere and time changes) indices were calculated from mean Fast Fourier Transform and analyzed as predictors of unfavorable outcome (mRS ≥ 3), at discharge and 12 months poststroke, before and after adjustment for age, admission NIHSS and ASPECTS. Results: Higher delta, lower alpha and beta relative powers (RP) predicted outcome. Indices with higher discriminative capacity were delta-theta to alpha-beta ratio (DTABR) and alpha RP. Outcome models including either of these and other clinical/imaging stroke outcome predictors were superior to models without qEEG data. In models with qEEG indices, infarct size was not a significant outcome predictor. Conclusions: DTAABR and alpha RP are the best qEEG indices and superior to ASPECTS in post-stroke outcome prediction. They improve the discriminative capacity of already known clinical and imaging stroke outcome predictors, both at discharge and 12 months after stroke. Significance: qEEG indices are independent predictors of stroke outcome.
- Remote and long-term self-monitoring of electroencephalographic and noninvasive measurable variables at home in patients with epilepsy (EEG@HOME) : protocol for an observational studyPublication . Biondi, Andrea; Laiou, Petroula; Bruno, Elisa; Viana, Pedro; Schreuder, Martijn; Hart, William; Nurse, Ewan; Pal, Deb K.; Richardson, Mark P.Background: Epileptic seizures are spontaneous events that severely affect the lives of patients due to their recurrence and unpredictability. The integration of new wearable and mobile technologies to collect electroencephalographic (EEG) and extracerebral signals in a portable system might be the solution to prospectively identify times of seizure occurrence or propensity. The performances of several seizure detection devices have been assessed by validated studies, and patient perspectives on wearables have been explored to better match their needs. Despite this, there is a major gap in the literature on long-term, real-life acceptability and performance of mobile technology essential to managing chronic disorders such as epilepsy. Objective: EEG@HOME is an observational, nonrandomized, noninterventional study that aims to develop a new feasible procedure that allows people with epilepsy to independently, continuously, and safely acquire noninvasive variables at home. The data collected will be analyzed to develop a general model to predict periods of increased seizure risk. Methods: A total of 12 adults with a diagnosis of pharmaco-resistant epilepsy and at least 20 seizures per year will be recruited at King's College Hospital, London. Participants will be asked to self-apply an easy and portable EEG recording system (ANT Neuro) to record scalp EEG at home twice daily. From each serial EEG recording, brain network ictogenicity (BNI), a new biomarker of the propensity of the brain to develop seizures, will be extracted. A noninvasive wrist-worn device (Fitbit Charge 3; Fitbit Inc) will be used to collect non-EEG biosignals (heart rate, sleep quality index, and steps), and a smartphone app (Seer app; Seer Medical) will be used to collect data related to seizure occurrence, medication taken, sleep quality, stress, and mood. All data will be collected continuously for 6 months. Standardized questionnaires (the Post-Study System Usability Questionnaire and System Usability Scale) will be completed to assess the acceptability and feasibility of the procedure. BNI, continuous wrist-worn sensor biosignals, and electronic survey data will be correlated with seizure occurrence as reported in the diary to investigate their potential values as biomarkers of seizure risk. Results: The EEG@HOME project received funding from Epilepsy Research UK in 2018 and was approved by the Bromley Research Ethics Committee in March 2020. The first participants were enrolled in October 2020, and we expect to publish the first results by the end of 2022. Conclusions: With the EEG@HOME study, we aim to take advantage of new advances in remote monitoring technology, including self-applied EEG, to investigate the feasibility of long-term disease self-monitoring. Further, we hope our study will bring new insights into noninvasively collected personalized risk factors of seizure occurrence and seizure propensity that may help to mitigate one of the most difficult aspects of refractory epilepsy: the unpredictability of seizure occurrence
- Seizure diaries and forecasting with wearables: epilepsy monitoring outside the clinicPublication . Brinkmann, Benjamin H.; Karoly, Philippa J.; Nurse, Ewan S.; Dumanis, Sonya B.; Nasseri, Mona; Viana, Pedro; Schulze-Bonhage, Andreas; Freestone, Dean R.; Worrell, Greg; Richardson, Mark P.; Cook, Mark J.It is a major challenge in clinical epilepsy to diagnose and treat a disease characterized by infrequent seizures based on patient or caregiver reports and limited duration clinical testing. The poor reliability of self-reported seizure diaries for many people with epilepsy is well-established, but these records remain necessary in clinical care and therapeutic studies. A number of wearable devices have emerged, which may be capable of detecting seizures, recording seizure data, and alerting caregivers. Developments in non-invasive wearable sensors to measure accelerometry, photoplethysmography (PPG), electrodermal activity (EDA), electromyography (EMG), and other signals outside of the traditional clinical environment may be able to identify seizure-related changes. Non-invasive scalp electroencephalography (EEG) and minimally invasive subscalp EEG may allow direct measurement of seizure activity. However, significant network and computational infrastructure is needed for continuous, secure transmission of data. The large volume of data acquired by these devices necessitates computer-assisted review and detection to reduce the burden on human reviewers. Furthermore, user acceptability of such devices must be a paramount consideration to ensure adherence with long-term device use. Such devices can identify tonic-clonic seizures, but identification of other seizure semiologies with non-EEG wearables is an ongoing challenge. Identification of electrographic seizures with subscalp EEG systems has recently been demonstrated over long (>6 month) durations, and this shows promise for accurate, objective seizure records. While the ability to detect and forecast seizures from ambulatory intracranial EEG is established, invasive devices may not be acceptable for many individuals with epilepsy. Recent studies show promising results for probabilistic forecasts of seizure risk from long-term wearable devices and electronic diaries of self-reported seizures. There may also be predictive value in individuals' symptoms, mood, and cognitive performance. However, seizure forecasting requires perpetual use of a device for monitoring, increasing the importance of the system's acceptability to users. Furthermore, long-term studies with concurrent EEG confirmation are lacking currently. This review describes the current evidence and challenges in the use of minimally and non-invasive devices for long-term epilepsy monitoring, the essential components in remote monitoring systems, and explores the feasibility to detect and forecast impending seizures via long-term use of these systems.