Cardiorespiratory cross-frequency coupling biomarker for sudden unexpected death in epilepsy

Area of Science:

• Cardiology
• Neurology
• Biomedical Engineering

Background:

• Sudden unexpected death in epilepsy (SUDEP) is a critical concern, often linked to cardiorespiratory dysfunction during seizures.
• Current monitoring methods for SUDEP risk are limited, especially during sleep.
• Electrocardiogram (ECG) signals contain information about cardiorespiratory function, even with noise.

Purpose of the Study:

• To investigate the utility of ictal ECG-based cross-frequency phase-amplitude coupling (PAC) as a biomarker for SUDEP.
• To compare cardiorespiratory disruptions between SUDEP and non-SUDEP epilepsy cohorts using PAC analysis.
• To assess the potential of PAC analysis for noninvasive SUDEP risk stratification.

Main Methods:

• Analysis of ictal ECG and EEG recordings from 21 SUDEP cases and 21 non-SUDEP controls.
• Application of continuous wavelet transformation to compute PAC between respiratory and cardiac frequencies during seizures.
• Development of a logistic regression model using derived PAC features to differentiate SUDEP from non-SUDEP seizures.

Main Results:

• Elevated ictal PAC at higher respiratory and cardiac frequencies was observed in SUDEP patients, indicating cardiorespiratory dysfunction.
• The logistic regression model achieved high accuracy (91% AUC) in distinguishing SUDEP from non-SUDEP seizures.
• SUDEP seizures and patients demonstrated significantly higher propensity scores, reinforcing the model's predictive capability.

Conclusions:

• Ictal ECG-based PAC analysis serves as a promising noninvasive biomarker for SUDEP risk.
• This method effectively captures cardiorespiratory dysregulation occurring during seizures.
• Integration with wearable ECG devices could facilitate real-time SUDEP risk assessment and inform timely clinical interventions.