ECG and EEG Analysis of Brain-Heart Interactions During Seizure Episodes

ECG and EEG Analysis of Brain-Heart Interactions During Seizure Episodes

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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.

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.

Heart Rate Variability (HRV), EEG Entropy, Brain-Heart Interaction, Seizure Disorders, Neurological Health, Autonomic Nervous System

Heart Rate Variability (HRV), EEG Entropy, Brain-Heart Interaction, Seizure Disorders, Neurological Health, Autonomic Nervous System

HUSSAIN, Y.; AMNA, R.; MALIK, A.

18.07.2025

IEEE Computer Society

Chemnitz

979-8-3315-0500-4

2025 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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https://ieeexplore.ieee.org/document/11079108