ArticleSensors (Basel, Switzerland)2020
Wearable Epileptic Seizure Prediction System with Machine-Learning-Based Anomaly Detection of Heart Rate Variability.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled 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.
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.
Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Feasibility of cardiac-based seizure detection and prediction: A systematic review of non-invasive wearable sensor-based studies.Epilepsia open · 2024Pooled it
- Medical Technology: A Systematic Review on Medical Devices Utilized for Epilepsy Prediction and Management.Current neuropharmacology · 2022Pooled it
- A systematic review of non-invasive biomarkers for seizure forecasting in pediatric epilepsy patients.Frontiers in neurologyPooled it
- Machine-learning methods for epilepsy diagnosis and therapeutic prevention: advances, setbacks, and opportunities.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Review
- Autonomic biosignals, seizure detection, and forecasting.Epilepsia · 2025Review
- Flexible Wearable Heart Rate Monitoring System and Low-Power Design: A Review.Sensors (Basel, Switzerland) · 2025Review
- Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications.Journal of clinical medicine · 2025Review
- Neural network-based method for measuring the impacts of epileptic brain activities on cardiac cycles.Frontiers in neurology · 2025Article
- Blunted tachycardia and cardiac sympathetic denervation in isolated rapid eye movement sleep behavior disorder.BMC neurology · 2024Article
- Review
- Heart Rate Variability as a Tool for Seizure Prediction: A Scoping Review.Journal of clinical medicine · 2024Article
- Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review.Sensors (Basel, Switzerland) · 2023Review
- Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review.Sensors (Basel, Switzerland) · 2023Review
- Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals.Sensors (Basel, Switzerland) · 2022Article
- Seizure-related differences in biosignal 24-h modulation patterns.Scientific reports · 2022Article
- Validation of Continuous Monitoring System for Epileptic Users in Outpatient Settings.Sensors (Basel, Switzerland) · 2022Article
- A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy.Frontiers in neuroinformatics · 2022Article
- Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis.Sensors (Basel, Switzerland) · 2021Article
- Forecasting Seizure Likelihood With Wearable Technology.Frontiers in neurology · 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
10 authors.
Funding
Abstract
A warning prior to seizure onset can help improve the quality of life for epilepsy patients. The feasibility of a wearable system for predicting epileptic seizures using anomaly detection based on machine learning is evaluated. An original telemeter is developed for continuous measurement of R-R intervals derived from an electrocardiogram. A bespoke smartphone app calculates the indices of heart rate variability in real time from the R-R intervals, and the indices are monitored using multivariate statistical process control by the smartphone app. The proposed system was evaluated on seven epilepsy patients. The accuracy and reliability of the R-R interval measurement, which was examined in comparison with the reference electrocardiogram, showed sufficient performance for heart rate variability analysis. The results obtained using the proposed system were compared with those obtained using the existing video and electroencephalogram assessments; it was noted that the proposed method has a sensitivity of 85.7% in detecting heart rate variability change prior to seizures. The false positive rate of 0.62 times/h was not significantly different from the healthy controls. The prediction performance and practical advantages of portability and real-time operation are demonstrated in this study.
Indexed as
Identifiers
What OpenQuestion holds
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.