Comparison and combination of electrocardiogram, electromyogram and accelerometry for tonic-clonic seizure detection in children

Automated real-time detection of tonic-clonic seizures in a home environment can be done using different modalities. The algorithms from the literature have not been evaluated nor compared on the same dataset. In this study, 3 seizure detection algorithms using electrocardiogram, electromyogram and accelerometers are evaluated on a single dataset. In this dataset, 7 pediatric patients with tonic-clonic seizures are monitored during 224 hours using 7 sensors in total. All unimodal algorithms are evaluated and compared to each other using this dataset. The different unimodal algorithms are also combined using a late integration approach. The best unimodal algorithm was found to be the algorithm using the right wrist accelerometer, leading to 95.5% sensitivity and 0.70 false alarms per hour. The best combination of sensors was found to be electrocardiogram with the right ankle accelerometer with a sensitivity of 90.9% and 0.08 false alarms per hour. The results show that all combinations of multimodal sensors lead to at least 75% less false alarms, showing that such multimodal algorithms should be used in practice for tonic-clonic seizure detection.