Literature DB >> 30790780

Early seizure detection for closed loop direct neurostimulation devices in epilepsy.

M Dümpelmann1.   

Abstract

Current treatment concepts for epilepsy are based on continuous drug delivery or electrical stimulation to prevent the occurrence of seizures, exposing the brain and body to a mostly unneeded risk of adverse effects. To address the infrequent occurrence and short duration of epileptic seizures, intelligent implantable closed-loop devices are needed which are based on a refined analysis of ongoing brain activity with highly specific and fast detection algorithms to allow for timely, ictal interventions. Since the development and FDA approval of a first closed loop neurostimulation device relying on simple threshold-based approaches, machine learning approaches became widely available, probably outperformed in the near future by deep convolutional neural networks, which already showed to be extremely successful in pattern recognition in images and partly in signal analysis. Handcrafted features or rules defined by experts become replaced by systematic feature selection procedures and systematic hyperparameter search approaches. Training of these classifiers augments the need of large databases with intracranial EEG recordings, which is partly given by existing databases but potentially can be replaced by continuously transferring data from implanted devices and their publication for research purposes. Already in early design states, the final target hardware must be taken into account for algorithm development. Size, power consumption and, as a consequence, limited computational resources given by low power microcontrollers, FPGAs and ASICS limit the complexity of feature computation, classifier complexity, and the numbers and complexity of layers of deep neuronal networks. Novel approaches for early seizure detection will be a key module for new generations of closed-loop devices together with improved low power implant hardware and will provide together with more efficient intervention paradigms new treatment options for patients with difficult to treat epilepsy.

Entities:  

Year:  2019        PMID: 30790780     DOI: 10.1088/1741-2552/ab094a

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  6 in total

Review 1.  Intelligent automated drug administration and therapy: future of healthcare.

Authors:  Richa Sharma; Dhirendra Singh; Prerna Gaur; Deepak Joshi
Journal:  Drug Deliv Transl Res       Date:  2021-01-14       Impact factor: 4.617

Review 2.  Bypassing the Blood-Brain Barrier: Direct Intracranial Drug Delivery in Epilepsies.

Authors:  Manuela Gernert; Malte Feja
Journal:  Pharmaceutics       Date:  2020-11-24       Impact factor: 6.321

3.  Time-Series Generative Adversarial Network Approach of Deep Learning Improves Seizure Detection From the Human Thalamic SEEG.

Authors:  Bhargava Ganti; Ganne Chaitanya; Ridhanya Sree Balamurugan; Nithin Nagaraj; Karthi Balasubramanian; Sandipan Pati
Journal:  Front Neurol       Date:  2022-02-16       Impact factor: 4.003

4.  Epilepsy Personal Assistant Device-A Mobile Platform for Brain State, Dense Behavioral and Physiology Tracking and Controlling Adaptive Stimulation.

Authors:  Tal Pal Attia; Daniel Crepeau; Vaclav Kremen; Mona Nasseri; Hari Guragain; Steven W Steele; Vladimir Sladky; Petr Nejedly; Filip Mivalt; Jeffrey A Herron; Matt Stead; Timothy Denison; Gregory A Worrell; Benjamin H Brinkmann
Journal:  Front Neurol       Date:  2021-07-29       Impact factor: 4.003

5.  A Comparison of Energy-Efficient Seizure Detectors for Implantable Neurostimulation Devices.

Authors:  Farrokh Manzouri; Marc Zöllin; Simon Schillinger; Matthias Dümpelmann; Ralf Mikut; Peter Woias; Laura Maria Comella; Andreas Schulze-Bonhage
Journal:  Front Neurol       Date:  2022-03-04       Impact factor: 4.003

6.  Regulatory Mechanism for Absence Seizures in Bidirectional Interactive Thalamocortical Model via Different Targeted Therapy Schemes.

Authors:  Hudong Zhang; Xiaolong Tan; Yufeng Pan; Yuan Chai
Journal:  Neural Plast       Date:  2021-09-16       Impact factor: 3.599

  6 in total

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