ArticleFrontiers in physiology2017
Early Seizure Detection Based on Cardiac Autonomic Regulation Dynamics.
Article in Frontiers in physiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 67 citations in OpenAlex.
- Feasibility of cardiac-based seizure detection and prediction: A systematic review of non-invasive wearable sensor-based studies.Epilepsia open · 2024Pooled it
- Ictal autonomic changes as a tool for seizure detection: a systematic review.Clinical autonomic research : official journal of the Clinical Autonomic Research Society · 2019Pooled it
- Explainable multi-modal approach for uncovering key predictors of stroke-risk from ECG, EMG, blood pressure, and respiratory signals.Scientific reports · 2026Article
- ECG-Based Detection of Epileptic Seizures in Real-World Wearable Settings: Insights from the SeizeIT2 Dataset.Sensors (Basel, Switzerland) · 2025Article
- ECG-based epileptic seizure prediction: Challenges of current data-driven models.Epilepsia open · 2025Article
- Seizure Detection Devices.Journal of clinical medicine · 2025Review
- Heart Rate Variability as a Tool for Seizure Prediction: A Scoping Review.Journal of clinical medicine · 2024Article
- ECG-Based Semi-Supervised Anomaly Detection for Early Detection and Monitoring of Epileptic Seizures.International journal of environmental research and public health · 2023Article
- Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals.Sensors (Basel, Switzerland) · 2022Article
- The power of ECG in multimodal patient-specific seizure monitoring: Added value to an EEG-based detector using limited channels.Epilepsia · 2021Article
- Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy.Scientific reports · 2021Article
- The Challenging Path to Developing a Mobile Health Device for Epilepsy: The Current Landscape and Where We Go From Here.Frontiers in neurology · 2021Review
- Seizure Susceptibility Prediction in Uncontrolled Epilepsy.Frontiers in neurology · 2021Article
- Noninvasive detection of focal seizures in ambulatory patients.Epilepsia · 2020Review
- Wearable Epileptic Seizure Prediction System with Machine-Learning-Based Anomaly Detection of Heart Rate Variability.Sensors (Basel, Switzerland) · 2020Article
- Heart Rate Variability Parameters During Psychogenic Non-epileptic Seizures: Comparison Between Patients With Pure PNES and Comorbid Epilepsy.Frontiers in neurology · 2020Article
- Identifying signal-dependent information about the preictal state: A comparison across ECoG, EEG and EKG using deep learning.EBioMedicine · 2019Article
- A Brain-Heart Biomarker for Epileptogenesis.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2018Article
- Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis.PloS one · 2018Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epilepsy is a neurological disorder that causes changes in the autonomic nervous system. Heart rate variability (HRV) reflects the regulation of cardiac activity and autonomic nervous system tone. The early detection of epileptic seizures could foster the use of new treatment approaches. This study presents a new methodology for the prediction of epileptic seizures using HRV signals. Eigendecomposition of HRV parameter covariance matrices was used to create an input for a support vector machine (SVM)-based classifier. We analyzed clinical data from 12 patients (9 female; 3 male; age 34.5 ± 7.5 years), involving 34 seizures and a total of 55.2 h of interictal electrocardiogram (ECG) recordings. Data from 123.6 h of ECG recordings from healthy subjects were used to test false positive rate per hour (FP/h) in a completely independent data set. Our methodological approach allowed the detection of impending seizures from 5 min to just before the onset of a clinical/electrical seizure with a sensitivity of 94.1%. The FP rate was 0.49 h
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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.