1. Rationale M easurement of wrist acceleration (ACM) by means of wearable devices has been exploited to automatically detect ongoing motor seizures in patients with epilepsy [1] [2]. Nevertheless, such seizure detectors show too high false alarm rates, which might hinder their use in daily life. Electrodermal activity (EDA) is a physiological signal reflecting sympathetic nervous system activity. Large ipsilateral EDA responses are elicited via direct stimulation of several subcortical regions such as the amygdala and hippocampus [3]. Measuring a combination of EDA and ACM has been previously shown to enhance specificity, i.e. to reduce the false alarm rate, in detection of secondary generalized tonic-clonic seizures (GTCS) [4]. However, the aforementioned approach requires further improvements in generalization capability and in further reducing false alarm rate. In this work we report the performances of four ACM and EDA- based seizure detectors fed with different feature sets, trained on 20 GTCS, improving results over previous work [4]. An improved wrist-worn convulsive seizure detector based on accelerometry and electrodermal activity sensors G Regalia 1, F Onorati 1, M Migliorini 1, R Picard 1,2 * 1. Empatica, Inc, Cambridge, MA and Milan, Italy, www.empatica.com 2. MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA *emails: picard@media.mit.edu or gr@empatica.compicard@media.mit.edugr@empatica.com References [1] J Lockman et al., “Detection of seizure-like movements using a wrist accelerometer”, Epilepsy and Behavior, v. 20, no. 4, pp. 638-641, 2011. [2] S. Beniczky et al., “Detection of generalized tonic–clonic seizures by a wireless wrist accelerometer: A prospective, multicenter study”, Epilepsia, v. 54, no. 4, pp. e58–e61, 2013. [3] C A Mangina and J H Beuzeron-Mangina, “Direct electrical stimulation of specific human brain structures and bilateral electrodermal activity,” Int J of Psychophysiology, v. 22, no. 1–2, pp. 1–8, 1996. [4] M-Z. Poh et al., “Convulsive seizure detection using a wrist-worn electrodermal activity and accelerometry biosensor,” Epilepsia, v. 53, no. 5, pp. e93–e97, 2012. Fig 1. (a) Exemplary 3-axis acceleration (ACM) and electrodermal activity (EDA) signals recorded during a generalized tonic-clonic seizure (GTCS) with a wrist device. (b) Schematic flow of the GTCS detector. Methods D ata were collected during clinical video EEG monitoring, and consist of 31 recordings taken from 9 patients wearing a wrist device monitoring EDA and three-axis ACM signals. EDA and ACM recordings were analyzed off-line using proprietary software to clean the data and extract four different feature sets to be employed for GTCS detection (Figure 1). The properties of each feature set are summarized in Table I. Each set of features was used to train a Support Vector Machine classifier. A leave-one-patient-out approach was employed in order to evaluate the sensitivity (Se) and the false alarm rate (FAR) of the classifier. The model was further improved by maximizing the cost function of a receiver operating characteristic. Results T he 9 patients’ recordings included 20 GTCS over a total of 738 hours. For all feature sets, the classifier was able to detect 19 out of 20 seizures (Se: 95%). Feature sets 2, 3 and 4 show a significantly lower false alarm rate than Feature set 1, the features previously published [4]. See Fig 2 for results. We reduced the false alarm rate (FAR) from 2.02 to 0.48 using feature set 3 instead of the original features, i.e. an overall reduction by a factor of 4.2. Conclusions A n automated seizure detection system was improved based on combining ACM and EDA information. The use of the new features preserved the current high sensitivity and significantly reduced the false alarm rate to less than one false alarm every 48 hours. After having selected the final feature set, the classifier can be integrated in a hardware platform to provide reliable real-time seizure alarms for caregivers. Fig 2. Number of false alarms per 24 hours, provided by the classifiers at 95% sensitivity. Patients were allowed to be active, up and about, and were not confined to bed, despite being in the EMU. Feature set Number of ACM features Number of EDA features 1163 2106 3306 4406 Table I. Characteristics of the different feature sets tested in this work. With gratitude to Drs. Dan Friedman, Tobias Loddenkemper, Claus Reinsberger, Rani Sarkis, and their teams at NYU, Boston Childrens Hospital, and Brigham & Womens Hospital, Boston for their generous support collecting sensor data and labeling Video-EEGs. FEATURE EXTRACTION GTCS DETECTOR ACM EDA FEATURE SET NO GTCS GTCS (ALARM) (a) (b)