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1. SzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithms

Author

Dan, Jonathan, Pale, Una, Amirshahi, Alireza, Cappelletti, William, Ingolfsson, Thorir Mar, Wang, Xiaying, Cossettini, Andrea, Bernini, Adriano, Benini, Luca, Beniczky, Sándor, Atienza, David, and Ryvlin, Philippe

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning
Abstract

The need for high-quality automated seizure detection algorithms based on electroencephalography (EEG) becomes ever more pressing with the increasing use of ambulatory and long-term EEG monitoring. Heterogeneity in validation methods of these algorithms influences the reported results and makes comprehensive evaluation and comparison challenging. This heterogeneity concerns in particular the choice of datasets, evaluation methodologies, and performance metrics. In this paper, we propose a unified framework designed to establish standardization in the validation of EEG-based seizure detection algorithms. Based on existing guidelines and recommendations, the framework introduces a set of recommendations and standards related to datasets, file formats, EEG data input content, seizure annotation input and output, cross-validation strategies, and performance metrics. We also propose the 10-20 seizure detection benchmark, a machine-learning benchmark based on public datasets converted to a standardized format. This benchmark defines the machine-learning task as well as reporting metrics. We illustrate the use of the benchmark by evaluating a set of existing seizure detection algorithms. The SzCORE (Seizure Community Open-source Research Evaluation) framework and benchmark are made publicly available along with an open-source software library to facilitate research use, while enabling rigorous evaluation of the clinical significance of the algorithms, fostering a collective effort to more optimally detect seizures to improve the lives of people with epilepsy.

Published
2024

2. Implications of therapy interruption on monthly migraine days and modified migraine disability assessment in patients treated with erenumab for chronic and episodic migraine: SQUARE study interim results

Author

Gantenbein, Andreas R., Bonvin, Christophe, Kamm, Christian P., Schankin, Christoph J., Zecca, Chiara, Zieglgänsberger, Dominik, Merki-Feld, Gabriele Susanne, Pohl, Heiko, Rudolph, Nicole, Ryvlin, Philippe, Agosti, Reto, Schäfer, Elisabeth, Meyer, Ina, Kulartz-Schank, Monika, and Arzt, Michael E.

Published
2024
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3. EpiDeNet: An Energy-Efficient Approach to Seizure Detection for Embedded Systems

Author

Ingolfsson, Thorir Mar, Chakraborty, Upasana, Wang, Xiaying, Beniczky, Sandor, Ducouret, Pauline, Benatti, Simone, Ryvlin, Philippe, Cossettini, Andrea, and Benini, Luca

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

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 2023

Published
2023

7. Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices

Author

Ingolfsson, Thorir Mar, Cossettini, Andrea, Wang, Xiaying, Tabanelli, Enrico, Tagliavini, Giuseppe, Ryvlin, Philippe, Benini, Luca, and Benatti, Simone

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

We present the implementation of seizure detection algorithms based on a minimal number of EEG channels on a parallel ultra-low-power embedded platform. The analyses are based on the CHB-MIT dataset, and include explorations of different classification approaches (Support Vector Machines, Random Forest, Extra Trees, AdaBoost) and different pre/post-processing techniques to maximize sensitivity while guaranteeing no false alarms. We analyze global and subject-specific approaches, considering all 23-electrodes or only 4 temporal channels. For 8s window size and subject-specific approach, we report zero false positives and 100% sensitivity. These algorithms are parallelized and optimized for a parallel ultra-low power (PULP) platform, enabling 300h of continuous monitoring on a 300 mAh battery, in a wearable form factor and power budget. These results pave the way for the implementation of affordable, wearable, long-term epilepsy monitoring solutions with low false-positive rates and high sensitivity, meeting both patient and caregiver requirements., Comment: 4 pages, 3 figures, 2 tables, preprint

Published
2021

10. Interpreting Deep Learning Models for Epileptic Seizure Detection on EEG signals

Author

Gabeff, Valentin, Teijeiro, Tomas, Zapater, Marina, Cammoun, Leila, Rheims, Sylvain, Ryvlin, Philippe, and Atienza, David

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

While Deep Learning (DL) is often considered the state-of-the art for Artificial Intelligence-based medical decision support, it remains sparsely implemented in clinical practice and poorly trusted by clinicians due to insufficient interpretability of neural network models. We have tackled this issue by developing interpretable DL models in the context of online detection of epileptic seizure, based on EEG signal. This has conditioned the preparation of the input signals, the network architecture, and the post-processing of the output in line with the domain knowledge. Specifically, we focused the discussion on three main aspects: 1) how to aggregate the classification results on signal segments provided by the DL model into a larger time scale, at the seizure-level; 2) what are the relevant frequency patterns learned in the first convolutional layer of different models, and their relation with the delta, theta, alpha, beta and gamma frequency bands on which the visual interpretation of EEG is based; and 3) the identification of the signal waveforms with larger contribution towards the ictal class, according to the activation differences highlighted using the DeepLIFT method. Results show that the kernel size in the first layer determines the interpretability of the extracted features and the sensitivity of the trained models, even though the final performance is very similar after post-processing. Also, we found that amplitude is the main feature leading to an ictal prediction, suggesting that a larger patient population would be required to learn more complex frequency patterns. Still, our methodology was successfully able to generalize patient inter-variability for the majority of the studied population with a classification F1-score of 0.873 and detecting 90% of the seizures., Comment: 28 pages, 11 figures, 12 tables

Published
2020

11. Synthetic Epileptic Brain Activities Using Generative Adversarial Networks

Author

Pascual, Damian, Aminifar, Amir, Atienza, David, Ryvlin, Philippe, and Wattenhofer, Roger

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning
Abstract

Epilepsy is a chronic neurological disorder affecting more than 65 million people worldwide and manifested by recurrent unprovoked seizures. The unpredictability of seizures not only degrades the quality of life of the patients, but it can also be life-threatening. Modern systems monitoring electroencephalography (EEG) signals are being currently developed with the view to detect epileptic seizures in order to alert caregivers and reduce the impact of seizures on patients' quality of life. Such seizure detection systems employ state-of-the-art machine learning algorithms that require a considerably large amount of labeled personal data for training. However, acquiring EEG signals of epileptic seizures is a costly and time-consuming process for medical experts and patients, currently requiring in-hospital recordings in specialized units. In this work, we generate synthetic seizure-like brain electrical activities, i.e., EEG signals, that can be used to train seizure detection algorithms, alleviating the need for recorded data. First, we train a Generative Adversarial Network (GAN) with data from 30 epilepsy patients. Then, we generate synthetic personalized training sets for new, unseen patients, which overall yield higher detection performance than the real-data training sets. We demonstrate our results using the datasets from the EPILEPSIAE Project, one of the world's largest public databases for seizure detection., Comment: Machine Learning for Health (ML4H) at NeurIPS 2019 - Extended Abstract

Published
2019

13. Reducing False Alarms in Wearable Seizure Detection With EEGformer: A Compact Transformer Model for MCUs

Author

Busia, Paola, primary, Cossettini, Andrea, additional, Ingolfsson, Thorir Mar, additional, Benatti, Simone, additional, Burrello, Alessio, additional, Jung, Victor J. B., additional, Scherer, Moritz, additional, Scrugli, Matteo A., additional, Bernini, Adriano, additional, Ducouret, Pauline, additional, Ryvlin, Philippe, additional, Meloni, Paolo, additional, and Benini, Luca, additional

Published
2024
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15. Postconvulsive central apnea as a biomarker for sudden unexpected death in epilepsy (SUDEP).

Author

Vilella, Laura, Lacuey, Nuria, Hampson, Johnson P, Rani, MR Sandhya, Sainju, Rup K, Friedman, Daniel, Nei, Maromi, Strohl, Kingman, Scott, Catherine, Gehlbach, Brian K, Zonjy, Bilal, Hupp, Norma J, Zaremba, Anita, Shafiabadi, Nassim, Zhao, Xiuhe, Reick-Mitrisin, Victoria, Schuele, Stephan, Ogren, Jennifer, Harper, Ronald M, Diehl, Beate, Bateman, Lisa, Devinsky, Orrin, Richerson, George B, Ryvlin, Philippe, and Lhatoo, Samden D

Subjects
Clinical Research, Epilepsy, Brain Disorders, Prevention, Neurodegenerative, Neurosciences, Lung, Adolescent, Adult, Aged, Biomarkers, Cardiopulmonary Resuscitation, Death, Sudden, Electroencephalography, Female, Humans, Male, Middle Aged, Prospective Studies, Sleep Apnea, Central, Statistics, Nonparametric, Video Recording, Young Adult, Clinical Sciences, Cognitive Sciences, Neurology & Neurosurgery
Abstract

ObjectiveTo characterize peri-ictal apnea and postictal asystole in generalized convulsive seizures (GCS) of intractable epilepsy.MethodsThis was a prospective, multicenter epilepsy monitoring study of autonomic and breathing biomarkers of sudden unexpected death in epilepsy (SUDEP) in patients ≥18 years old with intractable epilepsy and monitored GCS. Video-EEG, thoracoabdominal excursions, nasal airflow, capillary oxygen saturation, and ECG were analyzed.ResultsWe studied 148 GCS in 87 patients. Nineteen patients had generalized epilepsy; 65 had focal epilepsy; 1 had both; and the epileptogenic zone was unknown in 2. Ictal central apnea (ICA) preceded GCS in 49 of 121 (40.4%) seizures in 23 patients, all with focal epilepsy. Postconvulsive central apnea (PCCA) occurred in 31 of 140 (22.1%) seizures in 22 patients, with generalized, focal, or unknown epileptogenic zones. In 2 patients, PCCA occurred concurrently with asystole (near-SUDEP), with an incidence rate of 10.2 per 1,000 patient-years. One patient with PCCA died of probable SUDEP during follow-up, suggesting a SUDEP incidence rate 5.1 per 1,000 patient-years. No cases of laryngospasm were detected. Rhythmic muscle artifact synchronous with breathing was present in 75 of 147 seizures and related to stertorous breathing (odds ratio 3.856, 95% confidence interval 1.395-10.663, p = 0.009).ConclusionsPCCA occurred in both focal and generalized epilepsies, suggesting a different pathophysiology from ICA, which occurred only in focal epilepsy. PCCA was seen in 2 near-SUDEP cases and 1 probable SUDEP case, suggesting that this phenomenon may serve as a clinical biomarker of SUDEP. Larger studies are needed to validate this observation. Rhythmic postictal muscle artifact is suggestive of post-GCS breathing effort rather than a specific biomarker of laryngospasm.

Published
2019

16. Protocol for the development of an international Core Outcome Set for treatment trials in adults with epilepsy: the EPilepsy outcome Set for Effectiveness Trials Project (EPSET)

Author

Mitchell, James W., Noble, Adam, Baker, Gus, Batchelor, Rachel, Brigo, Francesco, Christensen, Jakob, French, Jacqueline, Gil-Nagel, Antonio, Guekht, Alla, Jette, Nathalie, Kälviäinen, Reetta, Leach, John Paul, Maguire, Melissa, O’Brien, Terence, Rosenow, Felix, Ryvlin, Philippe, Tittensor, Phil, Tripathi, Manjari, Trinka, Eugen, Wiebe, Samuel, Williamson, Paula R., and Marson, Tony

Published
2022
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20. Coffee consumption and seizure frequency in patients with drug-resistant focal epilepsy

Author

Bourgeois-Vionnet, Julie, Ryvlin, Philippe, Elsensohn, Mad-Hélénie, Michel, Véronique, Valton, Luc, Derambure, Philippe, Frazzini, Valerio, Hirsch, Edouard, Maillard, Louis, Bartolomei, Fabrice, Biberon, Julien, Petit, Jerôme, Biraben, Arnaud, Crespel, Arielle, Thomas, Pierre, Lemesle-Martin, Martine, Convers, Philippe, Leclercq, Mathilde, Boulogne, Sébastien, Roy, Pascal, and Rheims, Sylvain

Published
2022
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21. Comprehensive scoping review of fenfluramine's role in managing generalized tonic–clonic seizures in developmental and epileptic encephalopathies.

Author

Gil‐Nagel, Antonio, Cross, J. Helen, Devinsky, Orrin, Ceulemans, Berten, Lagae, Lieven, Knupp, Kelly, Schoonjans, An‐Sofie, Ryvlin, Philippe, Thiele, Elizabeth A., Polega, Shikha, Lothe, Amélie, and Nabbout, Rima

Subjects
EARLY death, MEDICAL care costs, PATIENTS' attitudes, EPILEPSY, DEVELOPMENTAL delay
Abstract

Developmental and epileptic encephalopathies (DEEs) are characterized by pharmacoresistant seizures and developmental delay. Patients with DEEs experience multiple seizure types, including tonic–clonic seizures (TCS) that can be generalized tonic–clonic (GTCS) or focal evolving to bilateral tonic–clonic (FBTCS). Fenfluramine (FFA) has demonstrated efficacy in reduction of TCS in patients with Dravet syndrome (DS), Lennox–Gastaut syndrome (LGS), and other DEEs. Using the PRISMA‐ScR (Preferred Reporting Items for Systematic Review and Meta‐Analyses extension for Scoping Review) guidelines, we performed a scoping review to describe changes in TCS in patients treated with FFA. A comprehensive search of five literature databases was conducted up to February 14, 2023. Studies were included if they reported change in GTCS or TCS (but not FBTCS) after treatment with FFA in patients with DEEs. Duplicate patients and studies with unclear efficacy data were excluded. Fourteen of 422 studies met the eligibility criteria. Data extracted and evaluated by expert clinicians identified 421 unique patients with DS (in nine studies), CDKL5 deficiency disorder, SCN8A‐related disorder, LGS, SCN1B‐related disorder, and other DEEs. The median percent reduction in GTCS or TCS from baseline was available in 10 studies (n = 328) and ranged from 47.2% to 100%. Following FFA treatment, 10 studies (n = 144) reported ≥50% reduction in GTCS or TCS from baseline in 72% of patients; in nine of those (n = 112), 54% and 29% of patients achieved ≥75% and 100% reduction in GTCS or TCS from baseline, respectively. Overall, this analysis highlighted improvements in GTCS or TCS frequency when patients were treated with FFA regardless of the DEE evaluated. Future studies may confirm the impact of FFA on TCS reduction and on decreased premature mortality risk (including sudden unexpected death in epilepsy), improvement in comorbidities and everyday executive function, decreased health care costs, and improvement in quality of life. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Long‐term evaluation of anterior thalamic deep brain stimulation for epilepsy in the European MORE registry.

Author

Kaufmann, Elisabeth, Peltola, Jukka, Colon, Albert J., Lehtimäki, Kai, Majtanik, Milan, Mai, Jürgen K., Bóné, Beata, Bentes, Carla, Coenen, Volker, Gil‐Nagel, Antonio, Goncalves‐Ferreira, Antonio J., Ryvlin, Philippe, Taylor, Rod, Brionne, Thomas C., Gielen, Frans, Song, Shannon, and Boon, Paul

Subjects
DEEP brain stimulation, PARTIAL epilepsy, EPILEPSY surgery, NEURAL stimulation, EPILEPSY, BRAIN stimulation, TEMPORAL lobectomy
Abstract

Objective: Short‐term outcomes of deep brain stimulation of the anterior nucleus of the thalamus (ANT‐DBS) were reported for people with drug‐resistant focal epilepsy (PwE). Because long‐term data are still scarce, the Medtronic Registry for Epilepsy (MORE) evaluated clinical routine application of ANT‐DBS. Methods: In this multicenter registry, PwE with ANT‐DBS were followed up for safety, efficacy, and battery longevity. Follow‐up ended after 5 years or upon study closure. Clinical characteristics and stimulation settings were compared between PwE with no benefit, improvers, and responders, that is, PwE with average monthly seizure frequency reduction rates of ≥50%. Results: Of 170 eligible PwE, 104, 62, and 49 completed the 3‐, 4‐, and 5‐year follow‐up, respectively. Most discontinuations (68%) were due to planned study closure as follow‐up beyond 2 years was optional. The 5‐year follow‐up cohort had a median seizure frequency reduction from 16 per month at baseline to 7.9 per month at 5‐year follow‐up (p <.001), with most‐pronounced effects on focal‐to‐bilateral tonic–clonic seizures (n = 15, 77% reduction, p =.008). At last follow‐up (median 3.5 years), 41% (69/170) of PwE were responders. Unifocal epilepsy (p =.035) and a negative history of epilepsy surgery (p =.002) were associated with larger average monthly seizure frequency reductions. Stimulation settings did not differ between response groups. In 179 implanted PwE, DBS‐related adverse events (AEs, n = 225) and serious AEs (n = 75) included deterioration in epilepsy or seizure frequency/severity/type (33; 14 serious), memory/cognitive impairment (29; 3 serious), and depression (13; 4 serious). Five deaths occurred (none were ANT‐DBS related). Most AEs (76.3%) manifested within the first 2 years after implantation. Activa PC depletion (n = 37) occurred on average after 45 months. Significance: MORE provides further evidence for the long‐term application of ANT‐DBS in clinical routine practice. Although clinical benefits increased over time, side effects occurred mainly during the first 2 years. Identified outcome modifiers can help inform PwE selection and management. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Neurologist–patient communication about epilepsy in the United States, Spain, and Germany

Author

Stern, John M, Cendes, Fernando, Gilliam, Frank, Kwan, Patrick, Ryvlin, Philippe, Sirven, Joseph, Smith, Brien, Adomas, Aleksandra, and Walter, Lauren

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Research, Health Services, Neurodegenerative, Epilepsy, Brain Disorders, Neurological, Good Health and Well Being
Abstract

BackgroundEffective communication between patients and their health care providers is recognized as critically important to improve the quality of health services for individuals with epilepsy. We aimed to describe in-office neurologist-patient conversations about epilepsy and focus on disease identification, shared decision-making, and care planning.MethodsTranscripts and audio recordings of conversations between patients and neurologists in the United States, Spain, and Germany were analyzed linguistically in the topic areas of epilepsy identification and diagnosis, disease education, treatments, and care planning. Analyses included word-level assessments, topic switching, strategies of information elicitation, identification of topics discussed, quantification of questions asked, and assessment of types of questions asked.ResultsConversations of 17 neurologists in the United States, 12 in Spain, and 6 in Germany, with 50, 20, and 16 patients, respectively, were analyzed. Neurologists tended to utilize an event-based, patient-friendly vocabulary to refer to seizures, and in the United States, they avoided using the term "epilepsy." Regardless of who initiated the treatment discussion, the neurologists in all 3 countries were unilaterally responsible for the treatment decision and choice of medication. When describing a new medication, neurologists most often discussed potential side effects but did not review potential benefits. Neurologists rarely defined seizure control and did not ask patients what seizure control meant to them.ConclusionsWe identified opportunities related to vocabulary, decision-making, and treatment goal setting that could be targeted to improve neurologist-patient communication about epilepsy, and ultimately, the overall treatment experience and outcomes for patients.

Published
2018

27. Residual Partial Least Squares Learning: Brain Cortical Thickness Simultaneously Predicts Eight Non-pairwise-correlated Behavioural and Disease Outcomes in Alzheimer's Disease

Author

Chen, Oliver Y., primary, Vu, Duy Thanh, additional, Diaz, Christelle Schneuwly, additional, Bodelet, Julien S., additional, Phan, Huy, additional, Allali, Gilles, additional, Nguyen, Viet-Dung, additional, Cao, Hengyi, additional, He, Xingru, additional, Muller, Yannick, additional, Zhi, Bangdong, additional, Shou, Haochang, additional, Zhang, Haoyu, additional, He, Wei, additional, Wang, Xiaojun, additional, Munafo, Marcus, additional, Trung, Nguyen Linh, additional, Nagels, Guy, additional, Ryvlin, Philippe, additional, and Pantaleo, Giuseppe, additional

Published
2024
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28. Grading system for assessing the confidence in the epileptogenic zone reported in published studies: A Delphi consensus study

Author

Ryvlin, Philippe, primary, Barba, Carmen, additional, Bartolomei, Fabrice, additional, Baumgartner, Christoph, additional, Brazdil, Milan, additional, Fabo, Daniel, additional, Fahoum, Firas, additional, Frauscher, Birgit, additional, Ikeda, Akio, additional, Lhatoo, Samden, additional, Mani, Jayanti, additional, McGonigal, Aileen, additional, Metsahonkala, Eeva‐Liisa, additional, Mindruta, Ioana, additional, Nguyen, Dang Khoa, additional, Rheims, Sylvain, additional, Rocamora, Rodrigo, additional, Rydenhag, Bertil, additional, Schuele, Stephan, additional, Schulze‐Bonhage, Andreas, additional, Surges, Rainer, additional, Vulliemoz, Serge, additional, and Beniczky, Sandor, additional

Published
2024
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32. The coming decade of digital brain research: A vision for neuroscience at the intersection of technology and computing

Author

Amunts, Katrin, primary, Axer, Markus, additional, Banerjee, Swati, additional, Bitsch, Lise, additional, Bjaalie, Jan G., additional, Brauner, Philipp, additional, Brovelli, Andrea, additional, Calarco, Navona, additional, Carrere, Marcel, additional, Caspers, Svenja, additional, Charvet, Christine J., additional, Cichon, Sven, additional, Cools, Roshan, additional, Costantini, Irene, additional, D'Angelo, Egidio Ugo, additional, De Bonis, Giulia, additional, Deco, Gustavo, additional, DeFelipe, Javier, additional, Destexhe, Alain, additional, Dickscheid, Timo, additional, Diesmann, Markus, additional, Düzel, Emrah, additional, Eickhoff, Simon B., additional, Einevoll, Gaute, additional, Eke, Damian, additional, Engel, Andreas K., additional, Evans, Alan C., additional, Evers, Kathinka, additional, Fedorchenko, Nataliia, additional, Forkel, Stephanie J., additional, Fousek, Jan, additional, Friederici, Angela D., additional, Friston, Karl, additional, Furber, Stephen, additional, Geris, Liesbet, additional, Goebel, Rainer, additional, Güntürkün, Onur, additional, Hamid, Aini Ismafairus Abd, additional, Herold, Christina, additional, Hilgetag, Claus C., additional, Hölter, Sabine M., additional, Ioannidis, Yannis, additional, Jirsa, Viktor, additional, Kashyap, Sriranga, additional, Kasper, Burkhard S., additional, d’Exaerde, Alban de Kerchove, additional, Kooijmans, Roxana, additional, Koren, István, additional, Kotaleski, Jeanette Hellgren, additional, Kiar, Gregory, additional, Klijn, Wouter, additional, Klüver, Lars, additional, Knoll, Alois C., additional, Krsnik, Zeljka, additional, Kämpfer, Julia, additional, Larkum, Matthew E, additional, Linne, Marja-Leena, additional, Lippert, Thomas, additional, Abdullah, Jafri Malin, additional, Maio, Paola Di, additional, Magielse, Neville, additional, Maquet, Pierre, additional, Mascaro, Anna Letizia Allegra, additional, Marinazzo, Daniele, additional, Mejias, Jorge, additional, Meyer-Lindenberg, Andreas, additional, Migliore, Michele, additional, Michael, Judith, additional, Morel, Yannick, additional, Morin, Fabrice O., additional, Muckli, Lars, additional, Nagels, Guy, additional, Oden, Lena, additional, Palomero-Gallagher, Nicola, additional, Panagiotaropoulos, Fanis, additional, Paolucci, Pier Stanislao, additional, Pennartz, Cyriel, additional, Peeters, Liesbet M., additional, Petkoski, Spase, additional, Petkov, Nicolai, additional, Petro, Lucy S., additional, Petrovici, Mihai A., additional, Pezzulo, Giovanni, additional, Roelfsema, Pieter, additional, Ris, Laurence, additional, Ritter, Petra, additional, Rockland, Kathleen, additional, Rotter, Stefan, additional, Rowald, Andreas, additional, Ruland, Sabine, additional, Ryvlin, Philippe, additional, Salles, Arleen, additional, Sanchez-Vives, Maria V., additional, Schemmel, Johannes, additional, Senn, Walter, additional, de Sousa, Alexandra A., additional, Ströckens, Felix, additional, Thirion, Bertrand, additional, Uludağ, Kâmil, additional, Vanni, Simo, additional, van Albada, Sacha Jennifer, additional, Vanduffel, Wim, additional, Vezoli, Julien, additional, Vincenz-Donnelly, Lisa, additional, Walter, Florian, additional, and Zaborszky, Laszlo, additional

Published
2024
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33. Neurologie: ce qui a changé en 2023

Author

Pizzarotti, Beatrice, primary, Buhler-Heintze, Amélie, additional, Allali, Gilles, additional, Rouaud, Olivier, additional, Bally, Julien, additional, Michel, Patrik, additional, Hirt, Lorenz, additional, Théaudin, Marie, additional, Pot Kreis, Caroline, additional, Ryvlin, Philippe, additional, Sokolov, Arseny, additional, Rossetti, Andrea, additional, Novy, Jan, additional, and Du Pasquier, Renaud, additional

Published
2024
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37. <sc>BrainFuseNet</sc>: Enhancing Wearable Seizure Detection Through EEG-PPG-Accelerometer Sensor Fusion and Efficient Edge Deployment

Author

Ingolfsson, Thorir Mar, Wang, Xiaying, Chakraborty, Upasana, Benatti, Simone, Bernini, Adriano, Ducouret, Pauline, Ryvlin, Philippe, Beniczky, Sandor, Benini, Luca, and Cossettini, Andrea

Abstract

This paper introduces BrainFuseNet, a novel lightweight seizure detection network based on the sensor fusion of electroencephalography (EEG) with photoplethysmography (PPG) and accelerometer (ACC) signals, tailored for low-channel count wearable systems. BrainFuseNet utilizes the Sensitivity-Specificity Weighted Cross-Entropy (SSWCE), an innovative loss function incorporating sensitivity and specificity, to address the challenge of heavily unbalanced datasets. The BrainFuseNet-SSWCE approach successfully detects $93.5\%$ seizure events on the CHB-MIT dataset ($76.34\%$ sample-based sensitivity), for EEG-based classification with only four channels. On the PEDESITE dataset, we demonstrate a sample-based sensitivity and false positive rate of $60.66\%$ and $1.18$ FP/h, respectively, when considering EEG data alone. Additionally, we demonstrate that integrating PPG signals increases the sensitivity to $61.22\%$ (successfully detecting $92\%$ seizure events) while decreasing the number of false positives to $1.0$ FP/h. Finally, when ACC data are also considered, the sensitivity increases to $64.28\%$ (successfully detecting $95\%$ seizure events) and the number of false positives drops to only $0.21$ FP/h for sample-based estimations, with less than one false alarm per day when considering event-based estimations. BrainFuseNet is resource-friendly and well-suited for implementation on low-power embedded platforms, and we evaluate its performance on GAP9, a state-of-the-art parallel ultra-low power (PULP) microcontroller for tiny Machine Learning applications on wearables. The implementation on GAP9 achieves an energy efficiency of $21.43$ GMAC/s/W, with an energy consumption per inference of only $0.11$ mJ at high performance ($412.54$ MMAC/s). The BrainFuseNet-SSWCE method demonstrates effective and accurate seizure detection on heavily imbalanced datasets while achieving state-of-the-art performance in the false positive rate and being well-suited for deployment on energy-constrained edge devices.

Published
2024
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46. Diagnostic accuracy of interictal source imaging in presurgical epilepsy evaluation: A systematic review from the E-PILEPSY consortium

Author

Mouthaan, Brian E., Rados, Matea, Boon, Paul, Carrette, Evelien, Diehl, Beate, Jung, Julien, Kimiskidis, Vasilios, Kobulashvili, Teia, Kuchukhidze, Giorgi, Larsson, Pål G., Leitinger, Markus, Ryvlin, Philippe, Rugg-Gunn, Fergus, Seeck, Margitta, Vulliémoz, Serge, Huiskamp, Geertjan, Leijten, Frans S.S., Van Eijsden, Pieter, Trinka, Eugen, and Braun, Kees P.J.

Published
2019
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49. Focal electroclinical features in generalized tonic–clonic seizures: Decision flowchart for a diagnostic challenge.

Author

Vlachou, Maria, Ryvlin, Philippe, Armand Larsen, Sidsel, and Beniczky, Sándor

Subjects
*SEIZURES (Medicine), *EPILEPSY, *PARTIAL epilepsy, *PEOPLE with epilepsy, *PROGNOSIS, *EPILEPTIFORM discharges
Abstract

Objective: Bilateral tonic–clonic seizures with focal semiology or focal interictal electroencephalography (EEG) can occur in both focal and generalized epilepsy types, leading to diagnostic errors and inappropriate therapy. We investigated the prevalence and prognostic values of focal features in patients with idiopathic generalized epilepsy (IGE), and we propose a decision flowchart to distinguish between focal and generalized epilepsy in patients with bilateral tonic–clonic seizures and focal EEG or semiology. Methods: We retrospectively analyzed video‐EEG recordings of 101 bilateral tonic–clonic seizures from 60 patients (18 with IGE, 42 with focal epilepsy). Diagnosis and therapeutic response were extracted after ≥1‐year follow‐up. The decision flowchart was based on previous observations and assessed concordance between interictal and ictal EEG. Results: Focal semiology in IGE was observed in 75% of seizures and 77.8% of patients, most often corresponding to forced head version (66.7%). In patients with multiple seizures, direction of head version was consistent across seizures. Focal interictal epileptiform discharges (IEDs) were observed in 61.1% of patients with IGE, whereas focal ictal EEG onset only occurred in 13% of seizures and 16.7% of patients. However, later during the seizures, a reproducible pattern of 7‐Hz lateralized ictal rhythm was observed in 56% of seizures, associated with contralateral head version. We did not find correlation between presence of focal features and therapeutic response in IGE patients. Our decision flowchart distinguished between focal and generalized epilepsy in patients with bilateral tonic–clonic seizures and focal features with an accuracy of 96.6%. Significance: Focal semiology associated with bilateral tonic–clonic seizures and focal IEDs are common features in patients with IGE, but focal ictal EEG onset is rare. None of these focal findings appears to influence therapeutic response. By assessing the concordance between interictal and ictal EEG findings, one can accurately distinguish between focal and generalized epilepsies. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Association of peri-ictal brainstem posturing with seizure severity and breathing compromise in patients with generalized convulsive seizures

Author

Vilella, Laura, Lacuey, Nuria, Hampson, Johnson P., Zhu, Liang, Omidi, Shirin, Ochoa-Urrea, Manuela, Tao, Shiqiang, Rani, M. R. Sandhya, Sainju, Rup K., Friedman, Daniel, Nei, Maromi, Strohl, Kingman, Scott, Catherine, Allen, Luke, Gehlbach, Brian K., Hupp, Norma J., Hampson, Jaison S., Shafiabadi, Nassim, Zhao, Xiuhe, Reick-Mitrisin, Victoria, Schuele, Stephan, Ogren, Jennifer, Harper, Ronald M., Diehl, Beate, Bateman, Lisa M., Devinsky, Orrin, Richerson, George B., Ryvlin, Philippe, Zhang, G. Q., and Lhatoo, Samden D.

Published
2020
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