ArticleEpilepsia2021
The power of ECG in multimodal patient-specific seizure monitoring: Added value to an EEG-based detector using limited channels.
Article in Epilepsia, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Validation Study for Monitoring of Focal Onset Epileptic Seizures With a Wearable Seizure Monitoring Device, EpiCare@Home
Real-time Seizure Detection, Classification, and Prediction Using a Low-Cost Low-Burden Ear-worn System
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Artificial intelligence in electroencephalography analysis for epilepsy diagnosis and management.Frontiers in neurology · 2025Pooled it
- A multicenter, video-EEG-based validation of a multimodal wearable device for focal seizure detection in adults: The SeizeIT2 study.Epilepsia open · 2026Trial
- Recent Progress in In-Ear EEG Technology and Its Emerging Real-World Applications: A Review.Micromachines · 2026Review
- ECG-Based Detection of Epileptic Seizures in Real-World Wearable Settings: Insights from the SeizeIT2 Dataset.Sensors (Basel, Switzerland) · 2025Article
- Development and Validation of a Model to Predict Secondary Arrhythmia in Patients With Epilepsy.CNS neuroscience & therapeutics · 2025Article
- Wearable Artificial Intelligence for Epilepsy: Scoping Review.Journal of medical Internet research · 2025Article
- SeizeIT2: Wearable Dataset Of Patients With Focal Epilepsy.Scientific data · 2025Article
- Seizure Detection Devices.Journal of clinical medicine · 2025Review
- Improving epilepsy monitoring using long-term, in-home-bi-modal seizure monitoring device: clinical utilities and obstacles from a pilot study.Frontiers in neurology · 2025Article
- Neural network-based method for measuring the impacts of epileptic brain activities on cardiac cycles.Frontiers in neurology · 2025Article
- Article
- Mapping of Neuro-Cardiac Electrophysiology: Interlinking Epilepsy and Arrhythmia.Journal of cardiovascular development and disease · 2023Review
- Electrocardiogram Changes in the Postictal Phase of Epileptic Seizure: Results from a Prospective Study.Journal of clinical medicine · 2023Article
- Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review.Sensors (Basel, Switzerland) · 2023Review
- Seizure detection with reduced electroencephalogram channels: research trends and outlook.Royal Society open science · 2023Review
- The Power of ECG in Semi-Automated Seizure Detection in Addition to Two-Channel behind-the-Ear EEG.Bioengineering (Basel, Switzerland) · 2023Article
- Out-of-hospital multimodal seizure detection: a pilot study.BMJ neurology open · 2023Article
- The power of ECG in multimodal patient-specific seizure monitoring: Added value to an EEG-based detector using limited channels.Epilepsia · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWearable seizure detection devices could provide more reliable seizure documentation outside the hospital compared to seizure self-reporting by patients, which is the current standard. Previously, during the SeizeIT1 project, we studied seizure detection based on behind-the-ear electroencephalography (EEG). However, the obtained sensitivities were too low for practical use, because not all seizures are associated with typical ictal EEG patterns. Therefore, in this paper, we aim to develop a multimodal automated seizure detection algorithm integrating behind-the-ear EEG and electrocardiography (ECG) for detecting focal seizures. In this framework, we quantified the added value of ECG to behind-the-ear EEG.
methodsThis study analyzed three multicenter databases consisting of 135 patients having focal epilepsy and a total of 896 seizures. A patient-specific multimodal automated seizure detection algorithm was developed using behind-the-ear/temporal EEG and single-lead ECG. The EEG and ECG data were processed separately using machine learning methods. A late integration approach was applied for fusing those predictions.
resultsThe multimodal algorithm outperformed the EEG-based algorithm in two of three databases, with an increase of 11% and 8% in sensitivity for the same false alarm rate. SIGNIFICANCE: ECG can be of added value to an EEG-based seizure detection algorithm using only behind-the-ear/temporal lobe electrodes for patients with focal epilepsy.
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Registered trials
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.