A Two-Layer Ensemble Method for Detecting Epileptic Seizures Using a Self-Annotation Bracelet With Motor Sensors

Abstract

Background: Using monitoring devices could help avoid injuries and even death. Currently, wearable sensors such as motion sensors and other sensors are used to detect when the patient is having a seizure and alarm their caregivers. However, the development phase of these devices requires labor-intensive work on labeling the collected data, resulting in difficulties in developing wearable monitoring devices. Thus, a more automated auxiliary method of labeling seizure data and a wearable device to detect seizures for daily monitoring use are necessary. Methods: We collected data from epileptics outside the hospital with our proposed bracelet. The subjects were asked to press the mark button after they had seizures. We also presented an automatically extraction and annotation of moving segments (EAMS) algorithm to exclude non-moving segments. Then we used a two-layer ensemble model (TLEM) using machine learning methods to classify seizures and non-seizure moving segments, which was designed to deal with imbalanced data set. Then we build two individual TLEM models separately for the overall (all day and night) seizure detection case and the night seizure detection case, owing to different imbalance of these data sets. Results: The EAMS algorithm exclude 93.9% raw inactive data. The TLEM model achieved 76.84% sensitivity and 97.28% accuracy for the overall case and achieved 94.57% sensitivity and 91.37% accuracy for the night case. Conclusion: These results indicate that this bracelet can capture seizures efficiently, and our proposed twolayer ensemble model has higher sensitivity and accuracy than single-layer machine learning models.