Evidence map›Paper›PMID 38083730›Full record

ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2023

An embedded intracranial seizure monitor for objective outcome measurements and rhythm identification.

John E Fleming, Moaad Benjaber, Robert Toth, Mayela Zamora, Kei Landin, Ali Kavoosi, Jonathan Ottoway, Tom Gillbe, Rory J Piper, Tara Noone and 6 more

Open access · goldAbstract read
In one paragraph

Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 28% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Development and Evaluation of a Real-Time Phase-Triggered Stimulation Algorithm for the CorTec Brain Interchange.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 5 institutions in 1 country.

John E Fleming
Moaad Benjaber
Robert Toth
Mayela Zamora
Kei Landin
Ali Kavoosi
Jonathan Ottoway
Tom Gillbe
Rory J Piper
Tara Noone
Hannah Campbell
Ivor Gillbe
Marios Kaliakatsos
Martin Tisdall
Antonio Valentin
Timothy Denison
Bioinduction (United Kingdom) · GBMRC Brain Network Dynamics Unit · GBUniversity College London · GBUniversity of Oxford · GBKing's College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Providing clinicians with objective outcomes of neuromodulation therapy is a key unmet need, especially in emerging areas such as epilepsy and mood disorders. These diseases have episodic behavior and circadian/multidien rhythm characteristics that are difficult to capture in short clinical follow-ups. This work presents preliminary validation evidence for an implantable neuromodulation system with integrated physiological event monitoring, with an initial focus on seizure tracking for epilepsy. The system was developed to address currently unmet requirements for patients undergoing neuromodulation therapy for neurological disorders, specifically the ability to sense physiological data during stimulation and track events with seconds-level granularity. The system incorporates an interactive software tool to enable optimal configuration of the signal processing chain on an embedded implantable device (the Picostim-DyNeuMo Mk-2) including data ingestion from the device loop recorder, event labeling, generation of filter and classification parameters, as well as summary statistics. When the monitor parameters are optimized, the user can wirelessly update the system for chronic event tracking. The simulated performance of the device was assessed using an in silico model with human data to predict the real-time device performance at tracking recorded seizure activity. The in silico performance was then compared against its performance in an in vitro model to capture the full environmental constraints such as sensing during stimulation at the tissue electrode interface. In vitro modeling demonstrated comparable results to the in silico model, providing verification of the software tool and model. This study provides validation evidence of the suitability of the proposed system for tracking longitudinal seizure activity. Given its flexibility, the event monitor can be adapted to track other disorders with episodic and rhythmic symptoms represented by bioelectrical behavior.Clinical relevance-An implantable neuromodulation system is presented that enables chronic tracking of physiological events in disease. This physiological monitor provides the basis for longitudinal assessments of therapy outcomes for patients, such as those with epilepsy where objective identification of patient seizure activity and rhythms might help guide therapy optimization. The system is configurable for other disease states such as Parkinson's disease and mood disorders.

Indexed as

EpilepsyHumansMonitoring, PhysiologicProstheses and ImplantsSeizuresSignal Processing, Computer-Assisted

Identifiers

PMID38083730
PMCPMC7615373
OpenAlexW4389542534

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.