Reliable detection of generalized convulsive seizures using an off-the-shelf digital watch: A multisite phase 2 study.

OBJECTIVE: The aim of this study was to develop a machine learning algorithm using an off-the-shelf digital watch, the Samsung watch (SM-R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy. METHODS: This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty-eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of (1) leave-one-patient-out cross-validation (LOPO CV) on GCS patients, (2) false alarm rate (FAR) testing on nonseizure patients, and (3) "fixed-and-frozen" prospective testing on a prospective patient cohort. Balanced accuracy, precision, sensitivity, and FAR were used to quantify the performance of the algorithm. Seizure onsets and offsets were determined by using video-electroencephalographic (EEG) monitoring. Feature importance was calculated as the mean decrease in Gini impurity during the LOPO CV testing. RESULTS: LOPO CV results showed balanced accuracy of .93 (95% confidence interval [CI] = .8-.98), precision of .68 (95% CI = .46-.85), sensitivity of .87 (95% CI = .62-.96), and FAR of .21/24 h (interquartile range [IQR] = 0-.90). Testing the algorithm on patients without seizure resulted in an FAR of .28/24 h (IQR = 0-.61). During the "fixed-and-frozen" prospective testing, two patients had three GCS, which were detected by the algorithm, while generating an FAR of .25/24 h (IQR = 0-.89). Feature importance showed that heart rate-based features outperformed accelerometer/gyroscope-based features. SIGNIFICANCE: Commercially available wearable digital watches that reliably detect GCS, with minimum false alarm rates, may overcome usage adoption and other limitations of custom-built devices. Contingent on the outcomes of a prospective phase 3 study, such devices have the potential to provide non-EEG-based seizure surveillance and forecasting in the clinical setting.

Use of noninvasive induction techniques in the diagnosis of PNES.

The diagnosis of psychogenic nonepileptic seizures (PNES) remains challenging. In the correct clinical setting with prolonged electroencephalography (EEG) monitoring, the specificity of provocative techniques to distinguish induced epileptic event from a nonepileptic event approaches 90%. We report our epilepsy monitoring unit (EMU) experience with the use of noninvasive verbal suggestion (VS) during hyperventilation (HV), photic stimulation (PS) as induction technique in making the diagnosis of PNES. In total, 189/423 patients were diagnosed with PNES during the EMU evaluation. Of the 189, 20 had mixed disorder and 169 patients had only PNES, 80 patients (47.3%) had a PNES with induction, and the remaining 89 of 169 patients (52.7%) had a spontaneous PNES episode that did not require induction. Verbal suggestion during HV and PS confirmed the diagnosis of PNES in 47% of the patients who otherwise did not have spontaneous events. Within the group who was diagnosed with PNES following induction, antiepileptic drugs (AEDs) were stopped in 53% of the patients. We believe that this is a large proportion of patients that would possibly remain undiagnosed if no induction were performed.