Richard (Jie) cui1, Louis Faust2, Boney Joseph3, Divyanshu Dubey3, Benjamin Brinkmann1
1Neurology, 2Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, 3Mayo Clinic
Objective:
This study sought to assess whether faciobrachial dystonic seizures (FBDS) can be reliably identified and differentiated from normal nocturnal arousals using a wrist-worn device.
Background:
Detection of FBDS in patients with Leucine-rich glioma inactivated-1 (LGI1-IgG) associated autoimmune encephalitis is challenging due to high event frequency and lack of a robust EEG correlate. Wearable devices may provide a convenient, noninvasive means to measure these events and provide an objective seizure diary.
Design/Methods:
Two LGI1-IgG seropositive patients (P1 and P2) and four control subjects, wearing Empatica E4 devices, were recruited for this study. Two eight-hour sleep periods of each patient: pre- and post-treatment, were analyzed. An algorithm was developed based on accelerometry (ACC) signals to detect events of interest. After event identification, we calculated the ACC magnitude, duration, and electrodermal activity (EDA) characteristics of detected events. Presently, we are actively expanding the patient cohort and are investigating machine learning methods to improve the accuracy of event detection.
Results:
Statistically significant differences were found in device signals pre- and post-treatment, and between FBDS events in LGI1-IgG and nocturnal arousals in control subjects.
The frequency of actigraphy events was higher in both patients (P1: pre-treatment 13.93±3.82 ev/h, post-treatment 9.66±3.37 ev/h; P2: pre-treatment 26.21±13.16 ev/h, post-treatment 29.06±9.79 ev/h), than controls (3.47±2.77 ev/h).
For ACC magnitude and event duration, the medians were lower post-treatment and significantly lower for ACC magnitude in P2. Post-treatment, significant differences were only observed between controls and P1.
For EDA activity, tonic and phasic components for controls were significantly lower than for patients pre-treatment. Additionally, means and medians decreased post-treatment in both patients.
Conclusions:
Robust characteristics were identified in actigraphy and EDA signals from a wrist-worn device during FBDS events. This evidence suggests an accurate monitor can be developed to automatically detect FBDS, providing objective measurements for treatment guidance and clinical decisions.