Publication Details
ECG and EEG Analysis of Brain-Heart Interactions During Seizure Episodes
Heart Rate Variability (HRV), EEG Entropy, Brain-Heart Interaction, Seizure
Disorders, Neurological Health, Autonomic Nervous System
Seizure disorders, such as epilepsy, profoundly im- pact brain and autonomic
nervous system function, disrupting physiological interactions between the brain
and heart. This study investigates these brain-heart interactions by analyzing
Heart Rate Variability (HRV) and Electroencephalography (EEG) en- tropy during
seizure and non-seizure states. Utilizing a dataset from patients with
intractable seizures, key HRV parameters (e.g., SDNN, RMSSD, LF/HF ratio, etc.)
and EEG entropy measures (Approximate Entropy) were compared across seizure
episodes and baseline periods. Results indicate a significant reduction in HRV
metrics, such as SDNN and RMSSD, during seizures, suggesting autonomic imbalance
and heightened sympa- thetic activity. Additionally, the ECG signal exhibited
a marked increase in the T/R amplitude ratio during seizures, further reflecting
the heightened cardiac response and autonomic dys- regulation associated with
these episodes. EEG entropy analysis revealed a marked decrease in signal
complexity during seizures, indicating less dynamic brain activity. The combined
findings of reduced HRV increased T/R amplitude ratio and decreased EEG entropy
underscore the value of these biomarkers for assessing Brain-heart interaction
disruptions in seizure disorders. Our results highlight the potential for HRV,
T/R amplitude ratio, and EEG entropy as a non-invasive clinical tool to monitor
seizures activity and evaluate neurological health, paving the way for more
refined diagnostic and therapeutic approaches.
This paper investigates the coupled dynamics of autonomic and cortical activity during epileptic seizures by concurrently analyzing heart rate variability (HRV) metrics and EEG entropy measures. Using the CHB-MIT pediatric seizure dataset, the authors extract standard time-domain (SDNN, RMSSD, pNN50), frequency-domain (LF, HF, LF/HF), and nonlinear (Poincaré SD1/SD2) HRV parameters from ECG, alongside approximate entropy of EEG signals. Their results demonstrate a significant reduction in HRV (e.g., a 16.7% drop in RMSSD) and EEG entropy during seizure episodes, coupled with an elevated T/R amplitude ratio-indicative of sympathetic overdrive and diminished neural complexity. The study's strengths lie in its multimodal approach and rigorous statistical analysis (paired t-tests, p<0.05), which together underscore the potential of these biomarkers for non-invasive seizure monitoring. However, its reliance on a single pediatric cohort and the absence of adult or chronic epilepsy subjects limit generalizability. Nevertheless, this work provides a valuable framework for integrating ECG and EEG features in wearable seizure-detection systems and informs future research on personalized, real-time neurological health assessment.
@inproceedings{BUT193361,
author="HUSSAIN, Y. and AMNA, R. and MALIK, A.",
title="ECG and EEG Analysis of Brain-Heart Interactions During Seizure Episodes",
booktitle="2025 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)",
year="2025",
pages="1--6",
publisher="IEEE Computer Society",
address="Chemnitz",
doi="10.1109/I2MTC62753.2025.11079108",
isbn="979-8-3315-0500-4",
url="https://ieeexplore.ieee.org/document/11079108"
}