Out-of-hospital multimodal seizure detection: a pilot study

BACKGROUND: Out-of-hospital seizure detection aims to provide clinicians and patients with objective seizure documentation in efforts to improve the clinical management of epilepsy.

In-patient studies have found that combining different modalities helps improve the seizure detection accuracy.

In this study, the objective was to evaluate the viability of out-of-hospital seizure detection using wearable ECG, accelerometry and behind-the-ear electroencephalography (EEG).

Furthermore, we examined the signal quality of out-of-hospital EEG recordings.

METHODS: Seventeen patients were monitored for up to 5 days.

A support vector machine based seizure detection algorithm was applied using both in-patient seizures and out-of-hospital electrographic seizures in one patient.

To assess the content of noise in the EEG signal, we compared the root-mean-square (RMS) of the recordings to a reference threshold derived from manually categorised segments of EEG recordings.

RESULTS: In total 1427 hours of continuous EEG was recorded.

In one patient, we identified 15 electrographic focal impaired awareness seizures with a motor component.

After training our algorithm on in-patient data, we found a sensitivity of 91% and a false alarm rate (FAR) of 18/24 hours for the detection of out-of-hospital seizures using a combination of EEG and ECG recordings.

We estimated that 30.1% of the recorded EEG signal was physiological EEG, with an RMS value within the reference threshold.

CONCLUSION: We found that detection of out-of-hospital focal impaired awareness seizures with a motor component is possible and that applying multiple modalities improves the diagnostic accuracy compared with unimodal EEG.

However, significant challenges remain regarding a high FAR and that only 30.1% of the EEG data represented usable signal.