EpiDeNet: An Energy-Efficient Approach to Seizure Detection for Embedded Systems

Epilepsy is a prevalent neurological disorder that affects millions of individuals globally, and continuous monitoring coupled with automated seizure detection appears as a necessity for effective patient treatment. To enable long-term care in daily-life conditions, comfortable and smart wearable devices with long battery life are required, which in turn set the demand for resource-constrained and energy-efficient computing solutions. In this context, the development of machine learning algorithms for seizure detection faces the challenge of heavily imbalanced datasets. This paper introduces EpiDeNet, a new lightweight seizure detection network, and Sensitivity-Specificity Weighted Cross-Entropy (SSWCE), a new loss function that incorporates sensitivity and specificity, to address the challenge of heavily unbalanced datasets. The proposed EpiDeNet-SSWCE approach demonstrates the successful detection of 91.16% and 92.00% seizure events on two different datasets (CHB-MIT and PEDESITE, respectively), with only four EEG channels. A three-window majority voting-based smoothing scheme combined with the SSWCE loss achieves 3x reduction of false positives to 1.18 FP/h. EpiDeNet is well suited for implementation on low-power embedded platforms, and we evaluate its performance on two ARM Cortex-based platforms (M4F/M7) and two parallel ultra-low power (PULP) systems (GAP8, GAP9). The most efficient implementation (GAP9) achieves an energy efficiency of 40 GMAC/s/W, with an energy consumption per inference of only 0.051 mJ at high performance (726.46 MMAC/s), outperforming the best ARM Cortex-based solutions by approximately 160x in energy efficiency. The EpiDeNet-SSWCE method demonstrates effective and accurate seizure detection performance on heavily imbalanced datasets, while being suited for implementation on energy-constrained platforms.Comment: 5 pages, 4 tables, 1 figure, Accepted at BioCAS 202

A structured, blended learning program towards proficiency in epileptology: the launch of the ILAE Academy Level 2 Program

The ILAE Academy is the online learning platform of the International League Against Epilepsy (ILAE) and offers a structured educational program addressing the competency-based ILAE curriculum in epileptology. The platform was launched in July 2020 with a self-paced course portfolio of interactive e-learning modules addressing ILAE Level 1 learning objectives, defined as the entry level in epileptology. Using feedback questionnaires from completed Level 1 courses as well as sociodemographic and learning-related data obtained from 47 participants, we show that over 50% of learners have an entry level in epileptology and do not have access to on-site training and over 40% do not have access to on-site training. Most respondents found the case-based e-learning modules relevant to their practice needs, and the time for completion was regarded as viable for most, reiterating the value of an online self-paced training in the field. Participants who have successfully completed all compulsory e-learning material of the Level 1 program and received their final certificate will now be eligible to subscribe to the Level 2 program. The Level 2 program addressing the proficiency level of the ILAE curriculum of epileptology was launched on the ILAE Academy platform in May 2022. The Level 2 program will offer an evolving series of self-paced, interactive, case-based e-learning modules on diagnosis, treatment, and counseling of common as well as rare epilepsies at a higher level of care. An interactive online EEG and MRI reader was developed and is embedded into the course content to satisfy the demands of the learners. The hallmark of this level will be the blended learning with tutored online courses, e.g., the established VIREPA courses on EEG and the newly introduced VIREPA MRI program. Our distinguished faculty will hold live tutored online courses in small groups in various languages and continental time zones. Finally, the ILAE face-to-face curricular teaching courses at summer schools and congresses will represent another pillar of this advanced teaching level. The ILAE Academy will also provide Continuing Medical Education (CME) credits to support career planning in epileptology

Current clinical magnetoencephalography practice across Europe : Are we closer to use MEG as an established clinical tool?

Purpose: This comprehensive survey aims at characterizing the current clinical use of magnetoencephalography (MEG) across European MEG centres. Methods: Forty-four MEG centres across Europe were contacted in May 2015 via personalized e-mail to contribute to survey. The web-based survey was available on-line for 1 month and the MEG centres that did not respond were further contacted to maximize participation. Results: Among the 57% of responders, 12 centres from 10 different countries reported to use MEG for clinical applications. A total of 524 MEG investigations were performed in 2014 for the pre-surgical evaluation of epilepsy, while in the same period 244 MEG investigations were performed for pre-surgical functional brain mapping. Seven MEG centres located in different European countries performed >50 MEG investigations for epilepsy mapping in 2014, both in children and adults. In those centres, time from patient preparation to MEG data reporting tends to be lower than those investigating a lower annual number of patients. Conclusion: This survey demonstrates that there is in Europe an increasing and widespread expertise in the field of clinical MEG. These findings should serve as a basis to harmonize clinical MEG procedures and promote the clinical added value of MEG across Europe. MEG should now be considered in Europe as a mature clinical neurophysiological technique that should be used routinely in two specific clinical indications, i.e, the pre-surgical evaluation of refractory focal epilepsy and functional brain mapping. (C) 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.Peer reviewe

Ictal EEG source imaging in presurgical evaluation: high agreement between analysis methods

AbstractPurposeTo determine the agreement between five different methods of ictal EEG source imaging, and to assess their accuracy in presurgical evaluation of patients with focal epilepsy. It was hypothesized that high agreement between methods was associated with higher localization-accuracy.MethodsEEGs were recorded with a 64-electrode array. Thirty-eight seizures from 22 patients were analyzed using five different methods phase mapping, dipole fitting, CLARA, cortical-CLARA and minimum norm. Localization accuracy was determined at sub-lobar level. Reference standard was the final decision of the multidisciplinary epilepsy surgery team, and, for the operated patients, outcome one year after surgery.ResultsAgreement between all methods was obtained in 13 patients (59%) and between all but one methods in additional six patients (27%). There was a trend for minimum norm being less accurate than phase mapping, but none of the comparisons reached significance. Source imaging in cases with agreement between all methods was not more accurate than in the other cases. Ictal source imaging achieved an accuracy of 73% (for operated patients: 86%).ConclusionThere was good agreement between different methods of ictal source imaging. However, good inter-method agreement did not necessarily imply accurate source localization, since all methods faced the limitations of the inverse solution

Simulation and 3D visualisation of physical phenomena on mobile devices

Izobraževalne simulacije se vedno bolj širijo iz tradicionalnih področij in postajajo pomembno orodje za izobraževanje. Čeprav so to zelo praktična orodja za pomoč v klasičnem izobraževanju, so bila do sedaj podprta predvsem v namiznem okolju in se niso tako uveljavila za širšo uporabo. Iz tega razloga smo razvili orodje za predstavitev fizikalnih simulacij v obliki mobilne aplikacije. Aplikacija je podprta s strani različnih naprav, tudi pametnih telefonov in tablic. Deluje na različnih velikostih zaslona in izkorišča prednosti prenosljivosti in računskih zmožnosti naprav. Z uporabo okolja jQuery Mobile, ki je prilagojen za naprave na dotik, smo dosegli, da aplikacija ni uporabna le v namiznem okolju, ampak dostopna tudi na drugih napravah. Razvili smo tudi podporo za hkratno manipuliranje simulacije s strani več uporabnikov v realnem času. V ta namen smo implementirali spletni vtičnik, ki nam omogoča hkratno kreiranje in manipuliranje simulacije na več napravah.Educational simulations are breaking out from traditional areas of use and emerging as an increasingly important tool for education. Even though, they are very practical tool for assisting in the classrooms, they have been supported mostly on desktop environments and didn’t fulfill the desired adoption. For this reason we created the mobile application that is a tool for presenting physics simulations. It is supported by different devices, including mobile phones and tablets, taking advantage of the different display size, and computational power characteristics of the devices. To make this application reachable not just from desktop computers, we used jQuery Mobile, framework that is optimized for touch devices. We have also provided the multiuser support of the simulations, offering users the possibility to manipulate shared real-time simulations. For that purpose we implemented WebSockets, technology that is facilitating live content and the creation of real-time simulations

Noninvasive detection of focal seizures in ambulatory patients

Reliably detecting focal seizures without secondary generalization during daily life activities, chronically, using convenient portable or wearable devices, would offer patients with active epilepsy a number of potential benefits, such as providing more reliable seizure count to optimize treatment and seizure forecasting, and triggering alarms to promote safeguarding interventions. However, no generic solution is currently available to reach these objectives. A number of biosignals are sensitive to specific forms of focal seizures, in particular heart rate and its variability for seizures affecting the neurovegetative system, and accelerometry for those responsible for prominent motor activity. However, most studies demonstrate high rates of false detection or poor sensitivity, with only a minority of patients benefiting from acceptable levels of accuracy. To tackle this challenging issue, several lines of technological progress are envisioned, including multimodal biosensing with cross‐modal analytics, a combination of embedded and distributed self‐aware machine learning, and ultra–low‐power design to enable appropriate autonomy of such sophisticated portable solutions

Electric Source Imaging in Presurgical Evaluation of Epilepsy: An Inter-Analyser Agreement Study

Mattioli P, Cleeren E, Hadady L, et al. Electric Source Imaging in Presurgical Evaluation of Epilepsy: An Inter-Analyser Agreement Study. Diagnostics. 2022;12(10): 2303.Electric source imaging (ESI) estimates the cortical generator of the electroencephalography (EEG) signals recorded with scalp electrodes. ESI has gained increasing interest for the presurgical evaluation of patients with drug-resistant focal epilepsy. In spite of a standardised analysis pipeline, several aspects tailored to the individual patient involve subjective decisions of the expert performing the analysis, such as the selection of the analysed signals (interictal epileptiform discharges and seizures, identification of the onset epoch and time-point of the analysis). Our goal was to investigate the inter-analyser agreement of ESI in presurgical evaluations of epilepsy, using the same software and analysis pipeline. Six experts, of whom five had no previous experience in ESI, independently performed interictal and ictal ESI of 25 consecutive patients (17 temporal, 8 extratemporal) who underwent presurgical evaluation. The overall agreement among experts for the ESI methods was substantial (AC1 = 0.65; 95% CI: 0.59-0.71), and there was no significant difference between the methods. Our results suggest that using a standardised analysis pipeline, newly trained experts reach similar ESI solutions, calling for more standardisation in this emerging clinical application in neuroimaging