VNS Seizure Prediction Algorithm Patents 2026 — PatSnap Eureka
VNS Seizure Prediction Algorithm Patents 2026
Closed-loop vagus nerve stimulation is converging with AI-driven seizure forecasting to address the ~50% non-responder rate in drug-resistant epilepsy. This dataset maps 60+ patent and literature records spanning core algorithm architectures, biomarker modalities, and key assignees through 2025.
Convergence of VNS Neuromodulation and Seizure Prediction Algorithms
The VNS seizure prediction field integrates three technical domains: implantable and non-invasive VNS hardware, seizure prediction algorithms trained on physiological biomarkers, and closed-loop feedback architectures coupling prediction outputs directly to stimulation parameter control. Primary signal modalities include scalp and intracranial EEG, vagal electroneurogram (VENG), ECG-derived HRV, MEG-based network topology, and multimodal combinations.
Core algorithmic approaches span classical machine learning (support vector machines, autocorrelation-based predictors, XGBoost classifiers), deep neural networks (CNNs, LSTMs, RNNs, dual-model deep learning pipelines), and signal-processing hybrids including variational mode decomposition combined with classification, chaos/bifurcation theory, and phase-locking value analysis. Hardware ranges from fully implanted cervical VNS leads to non-invasive transcutaneous auricular VNS devices.
Two distinct prediction objectives appear across the dataset: pre-implantation VNS responder prediction — identifying which patients will benefit from surgery before device implantation — and real-time seizure onset prediction, detecting the preictal state to trigger on-demand or adaptive stimulation. Both objectives are increasingly addressed within a single closed-loop system architecture, with recent filings from 2022–2025 marking a decisive shift toward on-nerve sensing.
In retrieved records, filings cluster markedly in 2019–2025, indicating a field transitioning from proof-of-concept to clinical-grade closed-loop implementation. Beijing Pins Medical Co., Ltd, Rune Labs, and Stanford together represent the most concentrated multi-record assignees in this dataset, while the VENG-sensing cluster is led by smaller independent inventor and startup structures such as Armstrong/Nuxcel2.
Algorithm Clusters and Filing Trends in Retrieved VNS Prediction Records
Among retrieved patent records, four primary technology clusters account for the majority of filings: dual-model deep neural networks, VENG-based on-nerve ML, HRV autonomic biomarker prediction, and closed-loop temporal optimization. Filing activity in this dataset accelerated sharply from 2019 onward.
Patent Records by Technology Cluster (Dataset Snapshot)
HRV-based VNS efficacy prediction and dual-model deep learning together account for the largest share of assignee-filed records in this dataset, with closed-loop temporal prediction and VENG-based sensing representing the most recent emerging clusters.
↗ Click bars to exploreVNS Prediction Patent Filing Activity by Period (Dataset Snapshot)
In this dataset, filing activity rose sharply from a small foundational base (2000–2012) through moderate ML integration (2013–2021) to peak concentration in the closed-loop and vagal sensing phase (2022–2025), reflecting the field’s shift toward clinical-grade implantable AI.
↗ Click bars to exploreKey Clinical and Technology Application Areas in VNS Seizure Prediction
Retrieved records span five distinct application domains, from drug-resistant epilepsy as the dominant clinical target to emerging wearable telemedicine platforms. Each domain exhibits distinct biomarker modalities and algorithm architectures.
Drug-Resistant Epilepsy
The dominant application across retrieved records, targeting the ~50% of VNS-implanted patients who remain non-responders. Patents from IBM, Nuxcel2, Armstrong, and the University of Minnesota all target real-time seizure suppression in this population. Pre-implantation responder prediction approaches include the PRECISE study, Pre-X-Stim statistical model, and CONNECTiVOS connectomic protocol using EEG connectivity and MEG network topology.
NeuromodulationPediatric Epilepsy VNS Prediction
Retrieved literature studies specifically address children and adolescents with drug-resistant epilepsy using MEG-based somatosensory evoked fields and synchronization biomarkers (PLI, wPLI, PLV) as prediction features. The CONNECTiVOS prospective study and an SVM-based model trained on 88 children using 25 clinical and 18 synchronization features represent the clinical research infrastructure for this sub-population.
Pediatric NeurologyMigraine and Cluster Headache
Non-invasive transcutaneous VNS (gammaCore) is documented across multiple retrieved literature records as an acute and preventive treatment for cluster headache and episodic migraine, including the PRESTO and PREMIUM trials. Algorithm development for this indication focuses on autonomic biomarker monitoring rather than EEG-based seizure detection, representing an adjacency to the core epilepsy patent landscape.
Non-Invasive VNSAI VNS Wearable Telemedicine
The 2025 IN filing from NIMS University Rajasthan and the 2025 US patent from Korea University Research and Business Foundation describe fully non-invasive AI-driven VNS devices with real-time multi-sensor biosignal fusion (EEG, HRV, GSR), closed-loop AI backends, and telemedicine platforms with cloud connectivity and mobile apps, indicating a consumer-grade device trajectory parallel to the implantable closed-loop track.
Wearable TelemedicineLeading Assignees in VNS Seizure Prediction — Dataset Snapshot
In this dataset, Beijing Pins Medical Co., Ltd and Rune Labs, Inc. each hold 4 retrieved records and represent the most concentrated single-assignee filing clusters. Together with Stanford University (4 records) and IBM (2 active US patents), these entities account for the majority of multi-record filings in retrieved records; however, major device companies such as LivaNova and Boston Scientific appear with single filings only.
Top Assignees by Filing Count in Retrieved Records (Dataset Snapshot)
↗ Click bars to exploreBeijing Pins Medical Co., Ltd
Beijing Pins Medical Co., Ltd holds 4 active US patents in this dataset, filed from 2019 to 2023, covering time-domain, frequency-domain, and nonlinear HRV indices extracted from 24-hour ECG for predicting VNS treatment efficacy. A separate modeling patent covers surgical candidate screening using multiscale entropy. All retrieved US records show active legal status, representing the most concentrated single-assignee filing cluster in retrieved records.
ChinaRune Labs, Inc.
Rune Labs, Inc. holds 4 retrieved records spanning US active (2021), WO (2021), CA pending (2021), and US active continuation (2024), all covering neuromodulation therapy simulation that enables algorithmic pre-deployment simulation of stimulation parameters. The family entered multiple jurisdictions including the US, WO, and Canada, indicating a broad international protection strategy for pre-deployment simulation methods.
United StatesFrontier Directions in Closed-Loop VNS and Seizure Prediction (2024–2025)
The most recent filings in this dataset (2024–2025) converge on five distinct frontier directions, from vagal electroneurogram-based self-contained implants to AI-native non-invasive wearables with cloud feedback loops.
Vagal Electroneurogram as First-Class Sensing Modality
The Nuxcel2/Armstrong WO filings (2024) signal a paradigm shift: these systems sense directly from the implanted VNS cuff electrode rather than relying on scalp EEG or peripheral HRV. Patient-specific ML models trained on VENG data detect ictal activity or predict imminent seizures, then trigger responsive stimulation from the same implanted system. This creates a fully self-contained bidirectional implant with no external sensor dependency — only two WO records in this dataset explicitly claim this architecture.
Global Temporal Optimization for Anticipatory Stimulation
The University of Minnesota’s 2025 US pending continuation introduces biomarker occurrence timing prediction — moving beyond binary preictal state detection to precise time-point forecasting. This enables anticipatory stimulation delivery before the biomarker appears, rather than reactive detection after it. The architecture constructs a predictive model of biomarker occurrence timing from baseline neural signal data, then uses that temporal model to optimize stimulation delivery timing in real time.
VENG On-Nerve ML vs. HRV Biomarker Prediction: Architecture Comparison
Click any row to explore further.
| Dimension | VENG On-Nerve ML (Nuxcel2/Armstrong) | HRV Biomarker Prediction (Beijing Pins) |
|---|---|---|
| Prediction Objective | Real-time ictal activity detection and imminent seizure prediction | Pre-implantation VNS responder identification |
| Signal Modality | Vagal electroneurogram (VENG) recorded directly from implanted VNS cuff electrode | ECG-derived HRV — time-domain, frequency-domain, and nonlinear indices from 24-hour ECG |
| Algorithm Type | Patient-specific machine learning models trained on VENG patterns | Multiscale entropy, HRV complexity indices for responder vs. non-responder classification |
| External Sensor Dependency | None — fully self-contained bidirectional implant | Requires external 24-hour ECG recording device pre-operatively |
| Timing of Use | Post-implantation, continuous real-time closed-loop operation | Pre-implantation surgical decision support |
| Filing Status in Dataset | 2 WO records (2024), pending international phase | 4 active US patents (2019–2023) |
| IP Concentration | Narrow — only 2 WO records in dataset; described as underexploited white space | Dense — multi-continuation US family with active legal status |
| Assignee Type | Independent inventor / startup structure (Armstrong, Nuxcel2 LLC) | Medical device company (Beijing Pins Medical Co., Ltd) |
Frequently Asked Questions: VNS Seizure Prediction Algorithm Patents
Retrieved patent records span classical machine learning (SVMs, autocorrelation-based predictors, XGBoost), deep neural networks (CNNs, LSTMs, RNNs, dual-model deep learning pipelines), and signal-processing hybrids including variational mode decomposition combined with classification, chaos/bifurcation theory, and phase-locking value analysis.
In this dataset, Beijing Pins Medical Co., Ltd holds 4 active US patents (filed 2019–2023) covering HRV-based VNS treatment efficacy prediction and surgical candidate screening using multiscale entropy, representing the most concentrated single-assignee filing cluster in retrieved records.
Pre-implantation responder prediction identifies which patients will benefit from VNS surgery before device implantation, using biomarkers such as HRV complexity or MEG network topology. Real-time seizure prediction detects the preictal state during device operation to trigger adaptive or on-demand stimulation. Both objectives are increasingly addressed within a single closed-loop system architecture.
The VENG is a neural signal recorded directly from the implanted VNS cuff electrode on the vagus nerve. Patents from Armstrong/Nuxcel2 (2024, WO) use patient-specific ML models trained on VENG data to detect ictal activity and predict seizures, creating a self-contained bidirectional implant with no dependency on external sensors such as scalp EEG or ECG. Only two WO records in this dataset explicitly claim this architecture.
The US is the dominant jurisdiction in this dataset, with the majority of active and pending records as US filings. PCT/WO filings have significant presence for newer closed-loop and VENG-based filings. India shows three pending filings (NIMS University Rajasthan, Shah/Sudhir, Londhe), and China has two records from Huazhong University of Science and Technology. EP, CA, AU, and KR are present in isolated filings.
The primary unmet need is the approximately 50% non-responder rate among drug-resistant epilepsy patients implanted with VNS devices. Multiple retrieved patents and literature studies target pre-implantation identification of likely responders and real-time adaptive stimulation to improve outcomes in patients who receive the device. The first entity to translate a validated preoperative prediction algorithm into a cleared SaMD product could capture value across an estimated 4,000–8,000 annual VNS implantations in the US and EU markets.
Data and insights on this page are based on a limited patent and literature dataset and are for reference only. Figures may not represent the complete technology landscape.