Onorati, Francesco & Regalia, Giulia & Caborni, Chiara & Picard, Rosalind. (2016). Improving convulsive seizure detection by exploiting data from outpatient settings using the Embrace wristband.

Abstract

Purpose: Embrace (http://www.empatica.com/product-embrace) is a convulsive seizure detector wristband which relies on traditional accelerometer sensors, and skin conductance sensors, which detect the electrodermal activity triggered by the sympathetic autonomic response during a seizure. We show the effectiveness of using home subjects’ data to dramatically improve the performances of the detector, compare to rely solely on Epilepsy Monitoring Unit (EMU) dataset. Method: Three (3) classifiers, namely EMP0, EMP1 and EMP1+ have been tested. EMP0, based on the original classifier (Poh et al. Epilepsia 2012,53(5),93-7), and EMP1, an improved version of the classifier, were trained on an EMU dataset consisting of 55 generalized convulsive seizures (GCSs) from 69 patients (5,918 hours). EMP1+ is based on EMP1, but was trained on a larger dataset, consisting of home subjects’ data during potentially misleading activities, and of 84 GCSs from 93 patients (6,495 hours) recorded through the Embrace and alert system, which are being evaluated in an IRB-approved clinical trial. The performances were evaluated on a separate testing set with both clinical and home subjects’ data (55 GCSs from 37 patients – 2,210 hours). The recordings not including GCS represents misleading activities. The performances have been evaluated in terms of Sensitivity (Sens) and False Alarm Rate (FAR), i.e. false alarms per 24 hours. Results: EMP1+ outperforms both EMP1 and EMP0. Only EMP1+ classifier can reach Sens=100%, at cost of FAR=5.72. For low (Sens=85%) mid (Sens=90%) and high (Sens=95%) sensitivity, EMP1+ shows respectively FAR=0.85, FAR=1.05 and FAR=2, while EMP1 shows respectively FAR=4.5, FAR=4.85 and FAR=6.1, and EMP0 shows respectively FAR=10.37, FAR=11.35 and FAR=20.48. Conclusion: In this contribution we have demonstrated that having access to home subjects’ data can dramatically improve the performances of a convulsive seizure detector, taking advantage of a more comprehensive pool of human daily activities which can affect the performance of a classifier trained only on EMU data.