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Embrace, a wearable convulsive seizure detection and alert system: First performance report of a case study in real-life settings



RATIONALE: Empatica ( is working on the development of an automated comfortably wearable convulsive seizure (CS) detection system relying on accelerometer (ACC) and electrodermal activity (EDA) data (Epilepsia 2012, 53, 93-7). Using machine learning algorithms trained on generalized tonic-clonic seizures (GTCS) gathered inside Epilepsy Monitoring Units (EMUs), the system has achieved sensitivity (Se) of 92-100% and false alarm (FA) rates ranging from 0.48 to 2.02 false alarms per day (Regalia et al. 2015, American Epilepsy Society Annual Meeting; Onorati et al. 2016, Epilepsy Pipeline Conference, Caborni et al. 2016, Partners Against Mortality in Epilepsy). However, EMU settings do not mimic the real-life environment where the system is intended for use. In real-life settings people are engaged in very different physical activities, such as sports and physical labor, which may result in higher FA rates. Moreover, the dynamic and semiology of CSs occurring outside EMUs might be different, influencing their detection. In this work we present a case study from a real-life setting. METHODS: Embrace is a wrist-worn device and smartphone-based alert system which analyses 3-axis acceleration and EDA data from the patient and provides an alert to designated caregivers when an unusual event is detected. The Embrace and alert system are being evaluated in an IRB-approved clinical trial. This case study is of a patient with Dravet Syndrome (14 y.) enrolled in the trial of Embrace in the outpatient setting. No data from this patient was used in training the system. In order to evaluate performance, the patient’s caregiver was asked to meticulously annotate the occurrence of each CS and any activity that generated an alert. The number of FA’s was obtained by subtracting the number of correctly recognized CSs from the total alerts fired by the device. The Se was the percentage of CSs that automatically triggered an alert. RESULTS: Over a period of 113 days, the patient wore the device for 82 days (i.e., 1973 hours, average hours per day: 17.2). The system detected 22 out of 24 CSs (Se=92%). The 2 missed seizures were characterized by a mild motor component and brief duration ( < 50 sec). Figure 1 depicts the distribution of the patient’s seizures according to the hour of the day. The total number of FA was 39, for a FA rate of 0.48 per day worn. FAs were generated by activities such as hands clapping/shaking, car transport and dancing. CONCLUSIONS: In this work, we have reported the performance of an unobtrusive CS detector used by a patient for a period of more than 3 months in a real-life setting, where none of the patient’s data had been used in training the system. The performance, both Se and FA rates, were in the same range as those for data gathered in best-case clinical settings. In ongoing research with more patients, we are seeing similar results. At AES, and in future publications, we will present more detailed evaluations with other patients and healthy subjects engaged in diverse real-life activities.
Embrace, a wearable convulsive seizure detection and alert system
First performance report of a case study in real-life settings
R. Picard1,2, G. Regalia1, C. Caborni1, M. Migliorini1, F. Onorati1
1 Empatica, Inc, Cambridge, MA and Milan, Italy; 2 MIT Media Lab, Massachusetts Institute of Technology, Cambridge (MA), US
Empatica’s Embrace is acomfortable wristband wearable convulsive seizure (CS) detection system (Figure 1).
It is based on accelerometer (ACC) and electrodermal activity (EDA) data and works with asmartphone app, Alert.
Embrace with Alert app is amedical device (CE) in the European Union.Alert is available only through aclinical trial in the
In EMU settings, the system has achieved Sens =92-100%and false alarm rate (FAR) = 0.15-2.02 false alarms per
EMU settings may differ from the real-life environment.
We have recently shown that adding outpatient data to the EMU-based training set allows to attain better performance2.
1. Apatient with Dravet Syndrome (14y) wore the Embrace 113 days for 1,973 hours (daily worn time =17.2±5 hrs/day).
2. This patient’s data were not used to train the classifier.
3. As aground truth the patient’s caregiver was asked to meticulously annotate the occurrence of each CS.Alert without
aCS were labeled as afalse alarm (FA).
The system detected 22 out of 24 recorded CSs (Sens =92%), with adelay from 15-64 seconds (38 sec avg)between
the onset and the alert, and 6-57 seconds (11 sec avg)between the alert and call.
The 2missed seizures were characterized by alow EDA and amild brief clonic component (Figure 3A).
FAR = 0.35 false alarms per day worn,with atotal of 39 FAs.In 88 days out of 113 there were no FAs.In only 2days
out of 113 were there more than 2FAs (Figure 2B).
We reported the performance of Embrace+Alert system used for 3months in real-life setting.
The performance,both Sens and FAR, mirrored the results obtained in EMU settings.
All seizures during sleep were detected (Figure 2A).No FA occurred during night.
Caregiver reported that FAs were generated by activities like hands shaking,car transport and dancing.
To present the first case study about long-term Embrace recordings and alerts in real-life settings.
Francesco Onorati:
Rosalind Picard: ,
Figure 3. EDA (top) and ACM (bottom) for two CSs recorded with Embrace.The violet areas mark the seizures.Left(A):missed event.Right(B):detected
Figure 1. Schematics of Embrace convulsive seizure (CS) detection and alert system: a wristband detects an event, transmits an alert to the smartphone,
which generates acall via acloud-based service to designated caregivers.Bottom:Empatica’s Embrace.
Figure 2. Left (A):Distribution of patient’s seizures based on hour of the day.Right (B):Histogram of FAR over the 113 days of recording.
1 Regalia et al. "An improved wrist-worn convulsive seizure detector based on accelerometry and electrodermal
activity sensors”, 69th AES, December 4-8, 2015, Philadelphia, PA.
2 Onorati et al. "Improving convulsive seizure detection by exploiting data from outpatient settings using the
Embrace wristband”,12th European Congress on Epileptology, September 11-15, 2016, Prague.
... Seizures involving the muscles can be well detected with acceleration data. [1,17] Many present sensors are used for the detection of motor seizures, using acceleration data. However, there are also nonmotor seizures that cannot be detected with acceleration data. ...
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Documentation of epileptic seizures plays an essential role in planning medical therapy. Solutions for automated epileptic seizure detection can help improve the current problem of incomplete and erroneous manual documentation of epileptic seizures. In recent years, a number of wearable sensors have been tested for this purpose. However, detecting seizures with subtle symptoms remains difficult and current solutions tend to have a high false alarm rate. Seizures can also affect the patient's arterial blood pressure, which has not yet been studied for detection with sensors. The pulse transit time (PTT) provides a noninvasive estimate of arterial blood pressure. It can be obtained by using to two sensors, which are measuring the time differences between arrivals of the pulse waves. Due to separated time chips a clock drift emerges, which is strongly influencing the PTT. In this work, we present an algorithm which responds to alterations in the PTT, considering the clock drift and enabling the noninvasive monitoring of blood pressure alterations using separated sensors. Furthermore we investigated whether seizures can be detected using the PTT. Our results indicate that using the algorithm, it is possible to detect seizures with a Random Forest. Using the PTT along with other signals in a multimodal approach, the detection of seizures with subtle symptoms could thereby be improved.
... A real-life longitudinal analysis was made on one patient affected by Dravet syndrome, monitored for 113 days. In this outpatient, 22 out of 24 convulsive seizures were detected (Sens=92%), with FAR=0.35(Picard et al, 2016). Results from three users monitored for more than 1.5 years/each, with a total of 331 GTCS, achieved Sens=99.4% with FAR=0.18(Onorati et al, 2018). ...
Wearable automated seizure detection devices offer a high potential to improve seizure management, through continuous ambulatory monitoring, accurate seizure counts, and real-time alerts for prompt intervention. More importantly, these devices can be a life-saving help for people with a higher risk of sudden unexpected death in epilepsy (SUDEP), especially in case of generalized tonic-clonic seizures (GTCS). The Embrace and E4 wristbands (Empatica) are the first commercially available multimodal wristbands that were designed to sense the physiological hallmarks of ongoing GTCS: while Embrace only embeds a machine learning-based detection algorithm, both E4 and Embrace devices are equipped with motion (accelerometers, ACC) and electrodermal activity (EDA) sensors and both the devices received medical clearance (E4 from EU CE, Embrace from EU CE and US FDA). The aim of this contribution is to provide updated evidence of the effectiveness of GTCS detection and monitoring relying on the combination of ACM and EDA sensors. A machine learning algorithm able to recognize ACC and EDA signatures of GTCS-like events has been developed on E4 data, labeled using gold-standard video-EEG examined by epileptologists in clinical centers, and has undergone continuous improvement. While keeping an elevated sensitivity to GTCS (92-100%), algorithm improvements and growing data availability led to lower false alarm rate (FAR) from the initial ˜2 down to 0.2-1 false alarms per day, as showed by retrospective and prospective analyses in inpatient settings. Algorithm adjustment to better discriminate real-life physical activities from GTCS, has brought the initial FAR of ˜6 on outpatient real life settings, down to values comparable to best-case clinical settings (FAR < 0.5), with comparable sensitivity. Moreover, using multimodal sensing, it has been possible not only to detect GTCS but also to quantify seizure-induced autonomic dysfunction, based on automatic features of abnormal motion and EDA. The latter biosignal correlates with the duration of post-ictal generalized EEG suppression, a biomarker observed in 100% of monitored SUDEP cases.
... • Comparable performance has been found in real-life settings using diary annotations as ground truth [3] [4], as well as in a longitudinal analysis of Embrace data collected from one patient with epilepsy (PWE) for almost a full year [5]. ...
Conference Paper
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Purpose The Embrace smartwatch combines accelerometer and electrodermal activity sensors to alert to generalized tonic-clonic seizures (GTCSs). While a multi-site clinical study validated 94.5% sensitivity (Se) of the GTCS detector with a false alarm rate (FAR) typically less than 0.2/day (Onorati et al, Epilepsia 2017), longitudinal analysis of Embrace performance is needed to understand its reliability and robustness in outpatient settings. A first analysis on ~1 year of data from one user showed Se=98% and FAR per day worn=0.14 (Regalia et al, Mobil 2017, Copenhagen). Here, we report on new longitudinal data from three users. Method One male (P1, 29y) and two female (P2 and P3, 15y) patients used Embrace from 04/2016 to 12/2017 in real-life. Each Embrace alarm generated a call to patients’ caregivers, and it was logged on an online database. Patients and caregivers were instructed to carefully mark any false alarms and any missed seizures. Two seizure-data experts independently inspected all the recordings to verify the accuracy of users’ self-reports. Results Overall, 618 (P1), 437 (P2) and 554 (P3) recording days were collected (average hours per day=23, 19 and 21, respectively). For P1, all 201 GTCSs were detected (Se=100%), with FAR=0.11. For P2, all 64 GTCSs were detected (Se=100%), with FAR=0.32. For P3, 63 out 65 GTCSs were detected (Se=97%), with FAR=0.12. Nocturnal seizures represented 87%, 92% and 43%, respectively, of each patients’ GTCS and were all detected except for one GTCS experienced by P3. No false alarms happened during sleep. Conclusion For the first time, the real-life long-term performance of a wearable GTCSs detector, i.e. Embrace, was evaluated for multiple users, each collecting data for more than 1.5 years, while diligently cross-checking each alarm with self-reports. Embrace provided an excellent trade-off between Se and FAR for all the testers. Analyses on more patients are underway.
Conference Paper
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Purpose: 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.
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Measurement of wrist acceleration (ACM) by means of wearable devices has been exploited to automatically detect ongoing motor seizures in patients with epilepsy (Epilepsy and Behav 2011, 20, 638-641; Epilepsia 2013, 54(4), e58-e61). Nevertheless, such seizure detectors can show high false alarm rates in active patients, which might hinder their use in daily life. Electrodermal activity (EDA) is a physiological signal reflecting sympathetic activity. Large ipsilateral EDA responses are elicited via direct stimulation of several subcortical regions (Int J Psychopysiol 1996, 22, 1-8). 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) (Epilepsia 2012, 53(5), 93-7). Nevertheless, the aforementioned approach requires further improvements in generalization capability and in further reducing false alarm rate for use in the widest variety of daily activities. Accordingly, in this contribution we report the performances of four ACM and EDA-based seizure detectors fed with different feature sets, trained on a higher number of seizures than in our previous work (Epilepsia 2012, 53(5), 93-7). See more at: