G. Regalia1, C. Caborni1, M. Migliorini1,R.Picard1,2, F. Onorati1
Materials and Methods
is an FDA-cleared wrist-worn device that senses
(EDA) to detect and
generalized tonic-clonic seizures
settings, Embrace’s machine-learning algorithm has shown
higher than 90%
false alarm rates
(FAR) as low as
settings, algorithm adjustments taking into account
lowered the FAR to levels
for the majority of patients and caregivers
Doctors, patients and families can also benefit from an automated offline GTCSs detection algorithm for
, for example to accurately and automatically fill in a
offline medical review
For this application, where high specificity is necessary, we show that it is possible
to increase the specificity
reducing the FAR,
by leveraging on
AIM: To quantify the increase in specificity attainable by a GTCS detection algorithm optimized
for offline applications, such as diagnostics or treatment management.
For the first time, the performance of GTCS detection algorithms
optimized for real-time alerting, have been compared with a new GTCS
detection algorithm optimized for
, optimized for
Increasing the quantity and the quality of the information, it was
reduce the FAR by 63%
data, with respect to
the FDA cleared online/embedded DA2 (
At Sens = 91%, the new DA3 achieved 0 false alerts for 136 out of 159
inpatients (85%). This is an improvement over the FDA-cleared DA2
that achieved zero FA for 108 out of 159 (68%) of patients (
, a metric related to the specificity of the algorithm,
also improves, droppping to 1 (DA3at Sens = 91%on TS1) and below 0.5
(0.44,DA3on TS2) (
will focus on improving the offline algorithm to reach
FAR = 0
,in order to embed the algorithm into a diagnostics and
treatment management framework.
were conducted on
,labeled as follows:
GTCSs were labeled by three independent epileptologists by video EEG inspection.
User reported GTCSs were reviewed by 2 data experts. Real-life activities prone to
generate false alarms are numerous in this dataset.
(DAs) were used for performance comparisons:
-an online DA, which has showed promising ambulatory performances
-an online DA, cleared by FDA for monitoring GTCSs during rest for patients
ages 6 and up
- a novel offline DA.
Improvement of generalized tonic-clonic seizure detection by Embrace wristband
on inpatient and outpatient data
1. Empatica, Inc, Boston, MA, US; 2. Massachusetts Institute of Technology, Cambridge, MA, US.
Regalia et al. (2019). Multimodal wrist-worn devices for seizure detection and
advancing research: Focus on the Empatica wristbands. Epilepsy Res. 153:79-82.
Onorati et al. (2018) Performance of awrist-worn multimodal seizure detection
system for more than a year in real-life settings. European Congress on Epileptology.
510(k) Premarket Notification Embrace 12/20/2018.(n.d.). Retrieved July 28,2019,
#GTCS DA1 DA2 DA3
10,368 159 32 56 X X
1,502 32 8 53 X X X
Testing sets evaluated using three detection algorithms.
false alert related metrics
between DA2 (green) and
DA3 (purple) on TS1
- histograms of
number of subjects with each
range of FAR;
histograms of number of
subjects with each range of
number of false alarms
experienced (#FA) and
average recording hours of
recording for each bin.
-FAR, #FA and
average recording hours per
bin for DA2 (light green) and
DA3 (light blue) with Sens =
bottom row -
and average recording hours
per bin for DA2(dark green)
and DA3(dark blue) with
Sens = 95%.
DA1 DA2 DA3
51 53 51 53
115 246 51 127
48 48 48
1.30 0.83 0.31
88 56 21
1.83 1.16 0.44
Performance comparisons on TS1(DA2 and DA3
only) and TS2(DA1, DA2 and DA3), at Sens = 91% - 95% and
at Sens = 91%, respectively (#SZ = number of seizures
detected, #FA = number of false alerts).
Diagram of a diagnostic/treatment management system based on an offline GTCS detection algorithm.
Sensitivity and FAR for the 3 detection algorithms (DAs) on the 2
separate testing sets used for performance evaluation. In orange, DA1, in green
DA2, in purple DA3. Circle markers for test set TS1, square markers for TS2.