O-45 Automated seizure detection for epilepsy patients using wearable ECG-device

Background So far, only generalized tonic-clonic seizures can be reliably detected with non-invasive wearable devices. We aimed to develop an automated seizure detection algorithm using a wearable ECG-device for detecting both GTC and focal seizures. Material and methods We recorded ECG using a dedicated wearable device (ePatch®) during long-term video-EEG monitoring. In this phase-2 clinical study, 100 patients were prospectively recruited; 43 of the patients had 126 seizures (108 focal, 18 GTC) of >20 s duration during recording (941 h training data, 2238 h test data). We analyzed 20 heart rate variability (HRV)-parameters and 6 combinations of these using either 50 or 100 R-R intervals sliding window with maximum overlapping. Each HRV-parameters cut-off value for seizure-alarm was set to 105% of the highest non-seizure period during training data of the same patient. Positive responders of seizure detection were defined, for each HRV-parameter, as patients with >66% of seizures detected. Results In total, 53.5% of the patients were responders for the best performing algorithm. In these patients, the method achieved a sensitivity of 93.1% and false detection rate of 1.1/day. An average of >50 beats/minute HR increase or decrease during seizure(s) is a positive predictor of being a responder of seizure detection (PPV: 87.0%, NPV: 90.0%), making it easy to define for which patients a reliable seizure alarm is feasible. Conclusions High sensitivity and low false positive alarm rates can be achieved with our algorithm analyzing ECG-signals using the wearable device in persons with average HR changes >50 beats/min during seizures.