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

Abstract 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.