95 research outputs found

    Dispositivos médicos na abordagem de doentes com epilepsia

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    O número crescente de dispositivos médicos desenvolvidos e comercializados para melhorar a gestão de doentes com epilepsia reflete o crescente interesse em traduzir os avanços tecnológicos e o conhecimento sobre epilepsia numa melhor prestação de cuidados de saúde a esta população. O objetivo desta revisão narrativa da literatura é analisar as opções de dispositivos médicos disponíveis para deteção, tratamento e registo de crises epiléticas e a sua possível aplicação clínica. Os artigos incluídos foram selecionados através da base de dados PubMed, utilizando a query "(Epilepsy[MeSH Terms]) AND (SUDEP)) AND (Medical Device)) AND (English[Language])". A deteção de crises epiléticas é essencial para a intervenção precoce e para otimizar a terapêutica de cada doente. No ambulatório, essa deteção é um desafiado devido à sua imprevisibilidade. Tradicionalmente, o eletroencefalograma é o método direto de deteção utilizado em contexto hospitalar. Métodos indiretos de deteção, como eletrocardiograma, fotopletismografia, oxímetro, atividade eletrodérmica, acelerómetro e eletromiografia, mostraram potencial para detetar crises epiléticas em ambulatório. Vários dispositivos médicos foram desenvolvidos com base nos métodos mencionados, com o objetivo de fornecer aos doentes soluções que possam usar no seu dia-a-dia. Alguns dos designs disponíveis são o eletroencefalograma com elétrodos retroauriculares, pulseiras, braçadeiras e sensores de pressão na cama. Equipados com diferentes funções, esses dispositivos podem ajudar na deteção precoce de crises epiléticas e melhorar a qualidade de vida de doentes e cuidadores. Existem também dispositivos disponíveis para o tratamento de crises epiléticas. Por meio de técnicas de neuromodulação, como a estimulação do nervo vago, a estimulação cerebral profunda e a neuroestimulação responsiva, esses dispositivos são apresentados como soluções para doentes com epilepsias refratárias não elegíveis para cirurgia ressetiva. Os doentes com epilepsia têm várias aplicações disponíveis online para o registo adequado de crises epiléticas. Essas aplicações ajudam os médicos na otimização da terapêutica com base na evolução clínica. A ampla gama de dispositivos disponíveis cria a oportunidade de personalizar a abordagem às necessidades específicas do doente. O conhecimento das características de cada dispositivo pode ajudar os médicos a melhorar a abordagem dos doentes com epilepsia.The increasing number of medical devices developed and marketed towards management of patients with epilepsy reflects the growing interest in translating technological advances and knowledge about epilepsy into better healthcare for this population. The objective of this narrative literature review is to analyze the available options of medical devices for detecting, treating, and recording epileptic seizures, and their potential clinical application. The included articles were selected from the PubMed database using the query "(Epilepsy[MeSH Terms]) AND (SUDEP)) AND (Medical Device)) AND (English[Language])" The detection of epileptic seizures is essential for early intervention and to optimize the therapy for each patient. In outpatient settings, this detection is further challenging due to their unpredictability. Traditionally electroencephalography is the direct detection method used in a hospital environment. Indirect methods, such as electrocardiogram, photoplethysmography, oximeter, electrodermal activity, accelerometer, and electromyography, have shown potential for detecting seizures in the outpatient setting. Several medical devices have been developed based on the mentioned methods, with the aim of providing patients with solutions they can use in their daily lives. Behind-the-ear EEG, wristbands, armbands and bed sensors are some of the designs available. Equipped with different features, these devices can answer the need for early seizure detection and improve patients' and caregivers' quality of life. There are also devices available for the treatment of epileptic seizures. Through neuromodulation techniques such as vagus nerve stimulation, deep brain stimulation, and responsive neurostimulation, these devices are presented as solutions for patients with refractory epilepsy not eligible for ressective surgery. Patients with epilepsy have several apps available online for proper recording of seizures. These apps help doctors optimize therapy based on clinical evolution. The wide range of devices available creates the opportunity to personalize the approach to patient's specific needs. Understanding each device's characteristics can help clinicians improve management of patients with epilepsy

    Evaluation of Wearable Electronics for Epilepsy: A Systematic Review

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    Epilepsy is a neurological disorder that affects 50 million people worldwide. It is characterised by seizures that can vary in presentation, from short absences to protracted convulsions. Wearable electronic devices that detect seizures have the potential to hail timely assistance for individuals, inform their treatment, and assist care and self-management. This systematic review encompasses the literature relevant to the evaluation of wearable electronics for epilepsy. Devices and performance metrics are identified, and the evaluations, both quantitative and qualitative, are presented. Twelve primary studies comprising quantitative evaluations from 510 patients and participants were collated according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Two studies (with 104 patients/participants) comprised both qualitative and quantitative evaluation components. Despite many works in the literature proposing and evaluating novel and incremental approaches to seizure detection, there is a lack of studies evaluating the devices available to consumers and researchers, and there is much scope for more complete evaluation data in quantitative studies. There is also scope for further qualitative evaluations amongst individuals, carers, and healthcare professionals regarding their use, experiences, and opinions of these devices

    Decentralized Federated Learning for Epileptic Seizures Detection in Low-Power Wearable Systems

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    In healthcare, data privacy of patients regulations prohibits data from being moved outside the hospital, preventing international medical datasets from being centralized for AI training. Federated learning (FL) is a data privacy-focused method that trains a global model by aggregating local models from hospitals. Existing FL techniques adopt a central server-based network topology, where the server assembles the local models trained in each hospital to create a global model. However, the server could be a point of failure, and models trained in FL usually have worse performance than those trained in the centralized learning manner when the patient's data are not independent and identically distributed (Non-IID) in the hospitals. This paper presents a decentralized FL framework, including training with adaptive ensemble learning and a deployment phase using knowledge distillation. The adaptive ensemble learning step in the training phase leads to the acquisition of a specific model for each hospital that is the optimal combination of local models and models from other available hospitals. This step solves the non-IID challenges in each hospital. The deployment phase adjusts the model's complexity to meet the resource constraints of wearable systems. We evaluated the performance of our approach on edge computing platforms using EPILEPSIAE and TUSZ databases, which are public epilepsy datasets.RYC2021-032853-

    Performance of ECG-based seizure detection algorithms strongly depends on training and test conditions

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    Objective To identify non-EEG-based signals and algorithms for detection of motor and non-motor seizures in people lying in bed during video-EEG (VEEG) monitoring and to test whether these algorithms work in freely moving people during mobile EEG recordings. Methods Data of three groups of adult people with epilepsy (PwE) were analyzed. Group 1 underwent VEEG with additional devices (accelerometry, ECG, electrodermal activity); group 2 underwent VEEG; and group 3 underwent mobile EEG recordings both including one-lead ECG. All seizure types were analyzed. Feature extraction and machine-learning techniques were applied to develop seizure detection algorithms. Performance was expressed as sensitivity, precision, F1_{1} score, and false positives per 24 hours. Results The algorithms were developed in group 1 (35 PwE, 33 seizures) and achieved best results (F1_{1} score 56%, sensitivity 67%, precision 45%, false positives 0.7/24 hours) when ECG features alone were used, with no improvement by including accelerometry and electrodermal activity. In group 2 (97 PwE, 255 seizures), this ECG-based algorithm largely achieved the same performance (F1_{1} score 51%, sensitivity 39%, precision 73%, false positives 0.4/24 hours). In group 3 (30 PwE, 51 seizures), the same ECG-based algorithm failed to meet up with the performance in groups 1 and 2 (F1_{1} score 27%, sensitivity 31%, precision 23%, false positives 1.2/24 hours). ECG-based algorithms were also separately trained on data of groups 2 and 3 and tested on the data of the other groups, yielding maximal F1 scores between 8% and 26%. Significance Our results suggest that algorithms based on ECG features alone can provide clinically meaningful performance for automatic detection of all seizure types. Our study also underscores that the circumstances under which such algorithms were developed, and the selection of the training and test data sets need to be considered and limit the application of such systems to unseen patient groups behaving in different conditions

    Characterization and processing of novel neck photoplethysmography signals for cardiorespiratory monitoring

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    Epilepsy is a neurological disorder causing serious brain seizures that severely affect the patients' quality of life. Sudden unexpected death in epilepsy (SUDEP), for which no evident decease reason is found after post-mortem examination, is a common cause of mortality. The mechanisms leading to SUDEP are uncertain, but, centrally mediated apneic respiratory dysfunction, inducing dangerous hypoxemia, plays a key role. Continuous physiological monitoring appears as the only reliable solution for SUDEP prevention. However, current seizure-detection systems do not show enough sensitivity and present a high number of intolerable false alarms. A wearable system capable of measuring several physiological signals from the same body location, could efficiently overcome these limitations. In this framework, a neck wearable apnea detection device (WADD), sensing airflow through tracheal sounds, was designed. Despite the promising performance, it is still necessary to integrate an oximeter sensor into the system, to measure oxygen saturation in blood (SpO2) from neck photoplethysmography (PPG) signals, and hence, support the apnea detection decision. The neck is a novel PPG measurement site that has not yet been thoroughly explored, due to numerous challenges. This research work aims to characterize neck PPG signals, in order to fully exploit this alternative pulse oximetry location, for precise cardiorespiratory biomarkers monitoring. In this thesis, neck PPG signals were recorded, for the first time in literature, in a series of experiments under different artifacts and respiratory conditions. Morphological and spectral characteristics were analyzed in order to identify potential singularities of the signals. The most common neck PPG artifacts critically corrupting the signal quality, and other breathing states of interest, were thoroughly characterized in terms of the most discriminative features. An algorithm was further developed to differentiate artifacts from clean PPG signals. Both, the proposed characterization and classification model can be useful tools for researchers to denoise neck PPG signals and exploit them in a variety of clinical contexts. In addition to that, it was demonstrated that the neck also offered the possibility, unlike other body parts, to extract the Jugular Venous Pulse (JVP) non-invasively. Overall, the thesis showed how the neck could be an optimum location for multi-modal monitoring in the context of diseases affecting respiration, since it not only allows the sensing of airflow related signals, but also, the breathing frequency component of the PPG appeared more prominent than in the standard finger location. In this context, this property enabled the extraction of relevant features to develop a promising algorithm for apnea detection in near-real time. These findings could be of great importance for SUDEP prevention, facilitating the investigation of the mechanisms and risk factors associated to it, and ultimately reduce epilepsy mortality.Open Acces

    Heart rate variability processing in epilepsy: The role in detection and prediction of seizures and SUDEP

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    Epilepsy is a very prevalent neurological disorder. The gold standard in diagnosis of epilepsy is the EEG signal recorded during a seizure with characteristic ictal pattern. Automated systems for detection of seizures are a field of intensive research, in an attempt to create a reproducible, observer-independent mechanism for epilepsy diagnosis. Chronic therapy is a cornerstone of the epilepsy treatment, but the possibility to predict seizure onset and, consequently, to act with medications right before the seizure, instead of relying on everyday medications, is considered the holy grail of epilepsy research. Significant element of morbidity and mortality in epilepsy is sudden unexpected death in epilepsy (SUDEP) that occurs in roughly 1% of patients. Signal analysis techniques for EEG have been a staple in epilepsy research, but recently, with the rise of telemetric systems, heart rate variability (HRV) analysis derived from the ECG signal has been gaining importance. It has been found that perturbations in autonomic nervous system (ANS) regulation occur during, and even up to several minutes before, seizure onset allowing for changes in HRV to act in prediction, as well as detection, of seizures. Also, there is a compelling research exploring the extent of autonomic disbalance during seizures, as well as in the interictal periods in patients at risk for or that have had SUDEP. The focus of this review is to give a short crossection of research involving the utility HRV has in prediction and detection of seizure onset, as well as determining etiology classification and risk evaluation in SUDEP

    Design of a wearable sensor system for neonatal seizure monitoring

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