Realization of Analog Wavelet Filter using Hybrid Genetic Algorithm for On-line Epileptic Event Detection

© 2020 The Author(s). This open access work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/.As the evolution of traditional electroencephalogram (EEG) monitoring unit for epilepsy diagnosis, wearable ambulatory EEG (WAEEG) system transmits EEG data wirelessly, and can be made miniaturized, discrete and social acceptable. To prolong the battery lifetime, analog wavelet filter is used for epileptic event detection in WAEEG system to achieve on-line data reduction. For mapping continuous wavelet transform to analog filter implementation with low-power consumption and high approximation accuracy, this paper proposes a novel approximation method to construct the wavelet base in analog domain, in which the approximation process in frequency domain is considered as an optimization problem by building a mathematical model with only one term in the numerator. The hybrid genetic algorithm consisting of genetic algorithm and quasi-Newton method is employed to find the globally optimum solution, taking required stability into account. Experiment results show that the proposed method can give a stable analog wavelet base with simple structure and higher approximation accuracy compared with existing method, leading to a better spike detection accuracy. The fourth-order Marr wavelet filter is designed as an example using Gm-C filter structure based on LC ladder simulation, whose power consumption is only 33.4 pW at 2.1Hz. Simulation results show that the design method can be used to facilitate low power and small volume implementation of on-line epileptic event detector.Peer reviewe

From hospital to home. The application of e-health solutions for monitoring and management of people with epilepsy

Background. In the last 10 years, there has been an explosion in the development of mobile and wearable technologies. Recent events such as Covid 19 emergency, showed the world how clinicians need to focus more on the application of these technologies to monitor and manage their patients. Despite this, the use of innovative technologies is not now a common practice in epilepsy. This thesis aims to demonstrate how people with epilepsy (PWE) are ready to use these mobile and wearable technologies and how data collected from these solutions can have a direct impact on PWE’s life. Methods. A systematic literature search was performed to provide an accurate overview of new non- invasive EEGs and their applications in epilepsy health care and an online survey was performed to fill the literature gap on this topic. To accurately study the PWE’s experience using wearable sensors, and the value of physiological and non-physiological data collected from wearable sensors, we used EEG data collected from the hospital (RADAR-CNS), and we collected original data from an at-home study (EEG@HOME). The data can be divided into two main categories: qualitative data (online survey, semi-structured interviews), and quantitative data analysis (questionnaires, EEG, and additional non-invasive physiological variables). Results. The systematic review showed us how non-invasive portable EEGs could provide valuable data for clinical purposes in epilepsy and become useful tools in different settings (i.e., rural areas, Hospitals, and homes). These are well accepted and tolerated by PWE and health care providers, especially for the easy application, cost, and comfort. The information obtained on the acceptability of repeated long-term non-invasive measures at home (EEG@HOME) showed that the use of the portable EEG cap was in general well tolerated over the 6 months but, the use of a smartwatch and the e-seizure diary was usually preferred. The level of compliance was good in most of the individuals and any barriers or issues which affected their experience or quality of the data were highlighted (i.e., life events, issues with equipment, and hairstyle of patients). Semi-structured interviews showed that participants found the combination of the three solutions very well-integrated and easy to use. The support received and the possibility to be trained and monitored remotely were well accepted and no privacy issues were reported by any of the participants. Most of the participants also suggested how they will be happy to have a mobile solution in the future to help to monitor their condition. The graph theory measures extracted from short and/or repeated EEG segments recorded from hospitals (RADAR-CNS) allowed us to explore the temporal evolution of brain activity prior to a seizure. Finally, physiological data and non-physiological data (EEG@HOME) were combined to understand and develop a model for each participant which explained a higher or lower risk of seizure over time. We also evaluated the value of repeated unsupervised resting state EEG recorded at home for seizure detection. Conclusion. The use of new technologies is well accepted by PWE in different settings. This thesis gives a detailed overview of two main points. First: PWE can be monitored in the hospital or at home using new wearable sensors or smartphone apps, and they are ready to use them after a short training and minimal supervision. Second: repeated data collection could provide a new way of a monitor, managing, and diagnosing people with epilepsy. Future studies should focus on balancing the acceptability of the solutions and the quality of the data collected. We also suggest that more studies focusing on seizure forecasting and detection using data collected from long-term monitoring need to be conducted. Digital health is the future of clinical practice and will increase PWE safety, independency, treatment, and monitoring

An introduction to future truly wearable medical devices--from application to ASIC.

Accepted versio

Utilising noise to improve an interictal spike detector

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A 60 pW g(m)C Continuous Wavelet Transform Circuit for Portable EEG Systems

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Algorithms and circuits for truly wearable physiological monitoring

Truly wearable physiological sensors, monitoring for example breathing or the electroencephalogram (EEG), require accurate and reliable algorithms for the automated analysis of the collected signal. This facilitates real-time signal interpretation and reduces the burden on human interpreters. It is well known that to reduce the total device power in many physiological sensors the automated analysis is best carried out using dedicated circuits in the sensor device itself, rather than transmitting all of the raw data and using an external system for the processing. To allow the physiological sensor to operate from the physically smallest batteries and energy harvesters new algorithms optimized for low power operation are thus required. This results in designers being presented with new trade-offs between the algorithm performance (for example the number of correct detections of an event and the number of false detections) and the power consumption of the circuit implementation. This presentation explores the state-of-the-art algorithms and circuits for use in these situations, drawing on particular examples from algorithms and circuits for use in breathing monitoring and EEG analysis.Accepted versio

Evaluation of Wearable Electronics for Epilepsy: A Systematic Review

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