CLOSED-LOOP INTERVENTION IN EPILEPSY DURING PRESURGICAL MONITORING IN EPILEPSY CENTER: SETUP OF THE TECHNICAL ENVIRONMENT
Relevant for Research Area
This project aimed at a realization of closed-loop interventions in a clinical setup with patients undergoing intracranial long-term EEG recordings. Main milestones were the implementation of seizure detection algorithms in an online fashion, integration of detections in an online access system and realization of triggered electrical stimulations in the zone of seizure onset, assessment of sensitivity and specificity of online detections and first experiences with stimulations at the site of seizure onset.
Results show that highly specific seizure detection was possible in almost all patients who agreed to participate, both in training EEG recordings obtained during the clinical investigation and in test datasets during the last hours of recordings when clinical information was complete. Aside from seizure detection-based interventions, also an assessment of stimulation effects on excitability were used based on synchronization measures. Triggered interventions using high frequency stimulation could not be assessed statistically for its efficacy to suppress ongoing ictal epileptic activity due to insufficient seizure numbers during the available intervention periods, but proved to be a safe approach. The project resulted in an implementation of a closed-loop seizure detection triggering interventions not only on standard computers but also on an energy-efficient microcontroller, allowing for a transfer of the results to implants. Overall, these results were highly successful and provide the basis for future assessments of optimal stimulation paradigms based on long-term intracranial implants in the human.
Journal (peer reviewed)
1. Schulze-Bonhage A, Somerlik K, Dümpelmann M. Closed-Loop Stimulation zur Epilepsietherapie. Z Epileptologie 2014; 27: 55-59.
2. Dümpelmann M, Jacobs J, Schulze-Bonhage A. Temporal and spatial characteristics of high frequency oscillations as a new biomarker in epilepsy. Epilepsia 2015 56(2): 197–206.
3. Donos C, Dümpelmann M, Schulze-Bonhage A. Early Seizure Detection Algorithm Based on Intracranial EEG and Random Forest Classification. International Journal of Neural Systems 2015 Vol. 25, No. 5 1550023
4. Meisel C, Schulze-Bonhage A, Freestone D, Cook MJ, Achermann P, Plenz D. Intrinsic excitability measures track antiepileptic drug action and uncover increasing/decreasing excitability over the wake/sleep cycle. Proc Natl Acad Sci 2015 112(47):14694–9
5. Meisel C, Plenz D, Schulze-Bonhage A, Reichmann H. Quantifying antiepileptic drug effects using intrinsic excitability measures. Epilepsia 2016 57(11):e210–e215
6. Bruder JC, Dümpelmann M, Lachner D, Mader M, Schulze-Bonhage A, Jacobs-Le Van J. Physiological Ripples Associated with Sleep Spindles Differ in Waveform Morphology from Epileptic Ripples. International Journal of Neural Systems 2017; 27: 1750011.
7. Zijlmans M, Worrell GA, Dümpelmann M, Stieglitz T, Barborica A, Heers M, Ikeda A, Usui N, Le Van Quyen M. How to record high frequency oscillations in epilepsy: a practical guideline. 2017 Epilepsia 58(8):1305-1315.
8. Cosandier-Rimélé D, Ramantani G, Zentner J, Schulze-Bonhage A, Dümpelmann M. A realistic multimodal modeling approach for the evaluation of distributed source analysis: application to sLORETA. Journal of Neural Engineering, 2017 Oct;14:056008.
9. Schulze-Bonhage A: Brain stimulation as a neuromodulatory epilepsy therapy. Seizure-eur J Epilep, 2017; 44: 169-175
10. Lachner Piza D, Epitashvili N, Schulze-Bonhage A, Stieglitz T, Jacobs J, Dümpelmann M. A single channel sleep-spindle detector based on multivariate classification of EEG epochs: MUSSDET. Journal of Neuroscience Methods 2018: 297:31-43.
11. Donos C, Malîia M, Dümpelmann M, Schulze-Bonhage A. Seizure onset predicts its type. Epilepsia 2018 59(3):650–660.
12. Schulze-Bonhage A. Long-term outcome in neurostimulation of epilepsy. Epil Behav 2019, in press.
13. Manzouri F, Heller S, Dümpelmann M, Woias P, Schulze-Bonhage A. A comparison of machine learning classifiers for energy-efficient implementation of seizure detection. Frontiers in Systems Neuroscience. 2018;12:43
Conference proceedings (peer reviewed)
1. Somerlik-Fuchs KH, Christ O, Dümpelmann M, Hofmann UG, Stieglitz T, Schulze-Bonhage A. Development of a Closed Loop Stimulation System for Epilepsy Therapy. Proceedings of 6th European Conference of the International Federation for Medical and Biological Engineering (MBEC 2014), 2014
2. Dümpelmann M, Cosandier-Rimélé D, Ramantani G, Schulze-Bonhage A. A Novel Approach for Multiscale Source Analysis and Modeling of Epileptic Spikes. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2015
3. Lachner Piza D, Bruder JC, Jacobs J, Schulze-Bonhage A, Stieglitz T, Dümpelmann M. Differentiation of spindle associated hippocampal HFOs based on a correlation analysis. 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2016
4. Lachner Piza D, Schulze-Bonhage A, Stieglitz T, Jacobs J, Dümpelmann M. Depuration and augmentation of training data for supervised learning based detectors of EEG patterns. 8th International IEEE/EMBS Conference on Neural Engineering (NER), 2017, pp 497 - 500.
5. Hügle, M, Heller S, Watter M, Blum M, Manzouri F, Dümpelmann M, Schulze-Bonhage A, Woias P, Boedecker J. Early Seizure Detection with an Energy-Efficient Convolutional Neural Network on an Implantable Microcontroller. 2018 International Joint Conference on Neural Networks, Rio de Janeiro Brazil, 2018.
6. Heller S, Hügle M, Nematollahi I, Manzouri F, Dümpelmann M, Schulze-Bonhage A, Bödecker J, Woias P. Early Hardware Implementation of a Performance and Energy-Optimized Convolutional Neural Network for Seizure Detection. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Honolulu , USA, 2018
Abstracts
1. Ewing S, Blum M, Rostek R, Woias P, Riedmiller M, Schulze-Bonhage A, Dümpelmann M. Factors affecting early automated epileptic seizure detection for online evaluation. 6th International Workshop on Seizure Prediction, San Diego, CA, USA, 2013
2. Blum M, Ewing S, Rostek R, Woias P, Riedmiller M, Schulze-Bonhage A, Dümpelmann M. Automatic seizure detection for closed loop devices by simple time domain features and machine learning methods. Abstract 52. Jahrestagung der Deutschen Gesellschaft für Epileptologie. Zeitschrift für Epileptologie • Supplement 1, 2014: 13.
3. Donos C, Schulze-Bonhage A, Dümpelmann, M. Low voltage fast activity early seizure detection algorithm for closed loop stimulation applications in patients with refractory epilepsy. Abstract 2014 Annual Meeting of the American Epilepsy Society
4. Dümpelmann M, Ewing S, Blum M, Rostek R, Woias P, Riedmiller M, Schulze-Bonhage A. Investigation of low complexity seizure detection algorithm for closed loop devices in epilepsy treatment. Abstract 30th International Congress of Clinical Neurophysiology. Clinical Neurophysiology 125, Supplement 1 (2014): 80.
5. Somerlik-Fuchs KH, Christ O, Dümpelmann M, Hofmann UG, Stieglitz T, Schulze-Bonhage A. Smart Devices In Epilepsy Therapy: Animal Research For Closed-Loop Stimulation Devices. 48. Annual Conference of the DGBMT. Biomed Tech 2014; 59 (s1): 1095
6. Donos C, Maliia MD, Dümpelmann, M. Schulze-Bonhage A. Early seizure type prediction based on single intracranial EEG channel - A proof of concept. 7th International Workshop on Seizure Prediction, Melbourne, Australia, 2015
7. Lachner D, Bruder JC, Jacobs J, Schulze-Bonhage A, Stieglitz T, Dümpelmann M. Recognition of pathological hippocampal-ripples based on ripple-spindle correlation analysis. 2nd International Workshop on High Frequency Oscillations in Epilepsy, 2016, Freiburg, Germany
8. Lachner D, Schulze-Bonhage A, Stieglitz T, Jacobs J, Dümpelmann M. An automatic HFO-Ripple detector based on depurated training data and a support vector machine. International Conference for Technology and Analysis of Seizures, 2017, Minneapolis, USA.
9. Lachner D, Schulze-Bonhage A, Stieglitz T, Jacobs J, Dümpelmann M. Independent detection of intracranial interictal epileptic Spikes and High Frequency Oscillations. 52nd DGBMT Annual Conference, 2018, Aachen, Germany.
10. Lachner D, Schulze-Bonhage A, Stieglitz T, Jacobs J, Dümpelmann M. Recognition of epileptic HFO on the long-term, intra-cranial EEG. 10th Latin American Epilepsy Congress, 2018, San Jose, Costa Rica.
11. Lachner D, Bruder JC, Schulze-Bonhage A, Stieglitz T, Dümpelmann M and Jacobs J. Automatic detection of HFO and Spikes: measuring the biomarker value of true-HFO, Spikes and Spike-associated-HFO. 2018 American Epilepsy Society Annual Meeting, New Orleans, USA.
12. Dümpelmann M, Cosandier-Rimélé D, Schulze Bonhage A. Constraining the source space systematically diminishes localization errors of intracranial EEG based sLORETA reconstruction. 8th International IEEE EMBS Neural Engineering Conference, Shanghai, China, 2017