Automated detection of focal seizures using subcutaneously implanted electrocardiographic device: A proof-of-concept...

Area of Science:

• Neurology
• Cardiology
• Biomedical Engineering

Background:

• Previous studies utilized heart rate variability (HRV) algorithms for focal seizure detection in patients with autonomic ictal changes.
• Wearable electrocardiographic (ECG) devices present challenges like frequent electrode changes and skin irritation.

Purpose of the Study:

• To evaluate the feasibility and accuracy of automated seizure detection using a subcutaneously implantable cardiac monitor (ICM).
• To assess the performance of a previously developed HRV algorithm with ICM data for focal seizure detection.

Main Methods:

• A proof-of-concept study involving six patients undergoing long-term video-electroencephalographic monitoring.
• Collection of 15-minute epochs of ECG signals during seizures and control periods, both in-hospital and at home (1-8 months).
• Offline analysis of ICM signals using a validated HRV algorithm.

Main Results:

• The algorithm achieved 92.6% sensitivity in detecting 50 out of 54 focal seizures.
• In-hospital seizure detection sensitivity was 92.3% (12/13 seizures).
• Out-of-hospital seizure detection sensitivity was 92.7% (38/41 seizures) with a false alarm rate of 2.7/24 hours.

Conclusions:

• Automated seizure detection using a long-term, subcutaneous ICM is feasible and accurate for patients with focal seizures and autonomic ictal changes.
• This technology offers a promising alternative to wearable ECG devices for continuous epilepsy monitoring.