Automated Detection of Tonic-Clonic Seizures using 3D Accelerometry and Surface Electromyography in Pediatric Patients

Epileptic seizure detection is traditionally done using video/electroencephalography monitoring, which is not applicable for long-term home monitoring. In recent years, attempts have been made to detect the seizures using other modalities. In this study we investigated the application of four accelerometers (ACM) attached to the limbs and surface electromyography (sEMG) electrodes attached to upper arms for the detection of tonic-clonic seizures. sEMG can identify the tension during the tonic phase of tonic-clonic seizure, while ACM is able to detect rhythmic patterns of the clonic phase of tonic-clonic seizures. Machine learning techniques, including feature selection and least-squares support vector machine classification, were employed for detection of tonic-clonic seizures from ACM and sEMG signals. In addition, the outputs of ACM and sEMGbased classifiers were combined using a late integration approach. The algorithms were evaluated on 1998.3 hours of data recorded nocturnally in 56 patients of which 7 had 22 tonic-clonic seizures. A multimodal approach resulted in a more robust detection of short and non-stereotypical seizures (91%), while the number of false alarms increased significantly compared with the use of single sEMG modality (0.28 to 0.5/12h). This study also showed that the choice of the recording system should be made depending on the prevailing pediatric patient-specific seizure characteristics and non-epileptic behavior.