Multimodal Detection of Tonic–Clonic Seizures Based on 3D Acceleration and Heart Rate Data from an In-Ear Sensor

Abstract

Patients with epilepsy suffer from recurrently occurring seizures. To improve diagnosis and treatment as well as to increase patients’ safety and quality of life, it is of great interest to develop reliable methods for automated seizure detection. In this work, we evaluate a first trial of a multimodal approach combining 3D acceleration and heart rate data acquired with a mobile In-Ear sensor as part of the project EPItect. For the detection of tonic–clonic seizures (TCS), we train different classification models (Naive Bayes, K-Nearest-Neighbor, linear Support Vector Machine and Adaboost.M1) and evaluate cost-sensitive learning as a measure to address the problem of highly imbalanced data. To assess the performance of our multimodal approach, we compare it to a unimodal approach, which only uses the acceleration data. Experiments show that our method leads to a higher sensitivity, lower detection latency and lower false alarm rate compared to the unimodal method.