Evelien E. Geertsema

Gevaarlijke epileptische aanvallen automatisch detecteren zonder apparaten en draden aan of bij de persoon? Het kan met een simpele videocamera en een algoritme, of rekenprogramma, dat de beelden analyseert. In een recente studie lieten onderzoekers hun algoritme los op lange, ononderbroken video-opnames met daarin een groot aantal epileptische aanvallen die in de thuissituatie werden geregistreerd. Ze lieten zien dat het algoritme gevaarlijke epileptische aanvallen in de nacht kan detecteren en mogelijk de veiligheid van mensen met deze aanvallen kan vergroten.

Summary: People with epilepsy need assistance and are at risk of sudden death when having convulsive seizures (CS). Automated real‐time seizure detection systems can help alert caregivers, but wearable sensors are not always tolerated. We determined algorithm settings and investigated detection performance of a video algorithm to detect CS in a residential care setting. The algorithm calculates power in the 2‐6 Hz range relative to 0.5‐12.5 Hz range in group velocity signals derived from video‐sequence optical flow. A detection threshold was found using a training set consisting of video‐electroencephalogaphy (EEG) recordings of 72 CS. A test set consisting of 24 full nights of 12 new subjects in residential care and additional recordings of 50 CS selected randomly was used to estimate performance. All data were analyzed retrospectively. The start and end of CS (generalized clonic and tonic–clonic seizures) and other seizures considered desirable to detect (long generalized tonic, hyperkinetic, and other major seizures) were annotated. The detection threshold was set to the value that obtained 97% sensitivity in the training set. Sensitivity, latency, and false detection rate (FDR) per night were calculated in the test set. A seizure was detected when the algorithm output exceeded the threshold continuously for 2 seconds. With the detection threshold determined in the training set, all CS were detected in the test set (100% sensitivity). Latency was ≤10 seconds in 78% of detections. Three/five hyperkinetic and 6/9 other major seizures were detected. Median FDR was 0.78 per night and no false detections occurred in 9/24 nights. Our algorithm could improve safety unobtrusively by automated real‐time detection of CS in video registrations, with an acceptable latency and FDR. The algorithm can also detect some other motor seizures requiring assistance.

Aim. Epilepsy is difficult to diagnose using routine EEG recordings of short duration in patients who have low seizure frequency. Long‐term EEG may be useful but is impractical in an out‐of‐hospital setting. We investigated whether single‐channel scalp EEG placed behind the earlobe is suitable for seizure identification during prolonged EEG monitoring.Methods. Scalp EEG samples were selected from subjects over 15 years of age, and comprised two segments of either background followed by seizure or background followed by background. Bipolar EEG derivations in three directions (F8‐T8, C4‐T8 and T8‐P8) were evaluated for the presence of a seizure by two experienced reviewers. For each EEG segment containing a seizure, one pair of electrodes was oriented towards the suspected region of seizure onset, while two pairs of electrodes were oriented elsewhere.Results. The EEG data contained five frontally localized seizures, five parietal, five temporal, two occipital, and four primary or secondary generalized seizures. The sensitivity and specificity for recognition of seizures was 86% and 95% for Reviewer 1, and 79% and 99% for Reviewer 2, respectively. When identifying a seizure with the lead orientation towards the region of seizure onset, both reviewers identified 20 out of 21 seizures (95%). When the lead was not oriented towards the region of seizure onset, the reviewers identified 34 and 30 out of 42 ictal records correctly, respectively.Conclusions. These results suggest that it is possible to identify epileptic seizures by bipolar EEG derivation using only two scalp electrodes. Lead orientation towards the suspected region of seizure onset is important for optimal detection sensitivity.