OBJECTIVE: The goal of this work is to objectively evaluate the effectiveness of neuromodulation therapies, specifically, Vagus nerve stimulation (VNS) in reducing the severity of seizures in patients with medically refractory epilepsy. METHODS: Using novel quantitative features obtained from combination of electroencephalographic (EEG) and electrocardiographic (ECG) signals around seizure events in 16 patients who underwent implantation of closed-loop VNS therapy system, namely AspireSR, we evaluated if automated delivery of VNS at the time of seizure onset reduces the severity of seizures by reducing EEG spatial synchronization as well as the duration and magnitude of heart rate increase. Unsupervised classification was subsequently applied to test the discriminative ability and validity of these features to measure responsiveness to VNS therapy. RESULTS: Results of application of this methodology to compare 105 pre-VNS treatment and 107 post-VNS treatment seizures revealed that seizures that were acutely stimulated using VNS had a reduced ictal spread as well as reduced impact on cardiovascular function compared to the ones that occurred prior to any treatment. Furthermore, application of an unsupervised fuzzy-c-mean classifier to evaluate the ability of the combined EEG-ECG based features to classify pre and post-treatment seizures achieved a classification accuracy of 85.85%. CONCLUSION: These results indicate the importance of timely delivery of VNS to reduce seizure severity and thus help achieve better seizure control for patients with epilepsy. SIGNIFICANCE: The proposed set of quantitative features could be used as potential biomarkers for predicting long-term response to VNS therapy.
OBJECTIVE: The goal of this work is to objectively evaluate the effectiveness of neuromodulation therapies, specifically, Vagus nerve stimulation (VNS) in reducing the severity of seizures in patients with medically refractory epilepsy. METHODS: Using novel quantitative features obtained from combination of electroencephalographic (EEG) and electrocardiographic (ECG) signals around seizure events in 16 patients who underwent implantation of closed-loop VNS therapy system, namely AspireSR, we evaluated if automated delivery of VNS at the time of seizure onset reduces the severity of seizures by reducing EEG spatial synchronization as well as the duration and magnitude of heart rate increase. Unsupervised classification was subsequently applied to test the discriminative ability and validity of these features to measure responsiveness to VNS therapy. RESULTS: Results of application of this methodology to compare 105 pre-VNS treatment and 107 post-VNS treatment seizures revealed that seizures that were acutely stimulated using VNS had a reduced ictal spread as well as reduced impact on cardiovascular function compared to the ones that occurred prior to any treatment. Furthermore, application of an unsupervised fuzzy-c-mean classifier to evaluate the ability of the combined EEG-ECG based features to classify pre and post-treatment seizures achieved a classification accuracy of 85.85%. CONCLUSION: These results indicate the importance of timely delivery of VNS to reduce seizure severity and thus help achieve better seizure control for patients with epilepsy. SIGNIFICANCE: The proposed set of quantitative features could be used as potential biomarkers for predicting long-term response to VNS therapy.
Authors: Eugenijus Kaniusas; Stefan Kampusch; Marc Tittgemeyer; Fivos Panetsos; Raquel Fernandez Gines; Michele Papa; Attila Kiss; Bruno Podesser; Antonino Mario Cassara; Emmeric Tanghe; Amine Mohammed Samoudi; Thomas Tarnaud; Wout Joseph; Vaidotas Marozas; Arunas Lukosevicius; Niko Ištuk; Sarah Lechner; Wlodzimierz Klonowski; Giedrius Varoneckas; Jozsef Constantin Széles; Antonio Šarolić Journal: Front Neurosci Date: 2019-07-24 Impact factor: 4.677
Authors: Arjune Sen; Ryan Verner; James P Valeriano; Ricky Lee; Muhammad Zafar; Rhys Thomas; Katarzyna Kotulska; Ellen Jespers; Maxine Dibué; Patrick Kwan Journal: BMJ Neurol Open Date: 2021-12-23