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Comparison and Combination of Electrocardiogram, Electromyogram and Accelerometry for Tonic-Clonic Seizure Detection in Children

Book Contribution - Book Chapter Conference Contribution

© 2018 IEEE. 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.
Book: Proc. of the IEEE Biomedical and Health Informatics
Pages: 438 - 441