Key result
Machine learning analysis of cardiac metrics detected convulsive seizures with 94% sensitivity and 89% specificity, and nonconvulsive seizures with 83% sensitivity and 82% specificity.
Why the study?
The study aimed to identify the most suitable cardiac metrics and machine learning algorithms for an electrocardiography-based seizure detection device.
Can machine learning analysis of heart rate and heart rate variability metrics accurately detect epileptic seizures in adult inpatients?
Observational (n=129)
Yes
Can machine learning analysis of heart rate and heart rate variability metrics accurately detect epileptic seizures in adult inpatients?
Machine learning analysis of cardiac metrics provides good sensitivity and specificity for detecting both convulsive and nonconvulsive seizures, though the false alarm rate remains a challenge.
May support ECG-based seizure detectors in limited-activity inpatients; leaves open prospective validation before clinical use.
OBJECTIVE: We aimed to determine the most suitable cardiac metrics and machine learning algorithms (MLa) for an electrocardiography-based seizure detection device. METHODS: In a multicenter, prospective study of adult inpatients, with limited physical activity, 24-hour video-electroencephalogram recordings including ≥ 1 seizure were analyzed. Heart Rate (HR) and Heart Rate Variability (HRV) metrics were calculated continuously from the corresponding electrocardiogram. HR and HRV time series were segmented into 5-min epochs. MLa were used to classify the epochs as containing seizures or not for the whole dataset; then for convulsive and nonconvulsive seizures only, without focusing on individual results. The sensitivity, specificity and False Alarm Rate (FAR) were calculated. RESULTS: We included 129 patients and 313 seizures (255 nonconvulsive). The most discriminant metrics were the signal quality, maximum cardiac sympathetic index, maximum heart rate, and minimum high frequency variability index. The sensitivity, specificity and FAR were respectively 94%, 89% and 0.6 for convulsive seizures (extremely randomized trees), and 83%, 82% and 1.13 for nonconvulsive seizures (random forest). CONCLUSIONS: In the largest unselected patient cohort study of this topic to date, seizure detection with ML analyses of cardiac metrics provides good results - even for nonconvulsive seizures. SIGNIFICANCE: The high FAR suggests to combine HR and HRV analysis with other metrics to increase specificity.
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Nouboue et al. (2025) conducted an observational in Epileptic seizures (n=129). Machine learning analysis of heart rate and heart rate variability metrics was evaluated on Sensitivity, specificity and False Alarm Rate (FAR) for seizure detection. Machine learning analysis of cardiac metrics detected convulsive seizures with 94% sensitivity and 89% specificity, and nonconvulsive seizures with 83% sensitivity and 82% specificity.
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