The research, drawn from two prospective clinical trials conducted at specialized Dutch epilepsy centers, followed 65 children aged 3 to 16, roughly half of them girls, who each experienced at least one nocturnal major motor seizure per week. The children wore the NightWatch system, a wearable armband that triggers alerts based on movement and heart rate, for a median of 47 nights each, either at home or in residential care units. Continuous video monitoring served as the gold standard against which every alarm was judged. An alarm was counted as true if a major motor seizure, encompassing generalized tonic–clonic seizures, focal-to-bilateral tonic–clonic events, and prolonged tonic or clonic seizures, occurred within a six-minute window around the trigger. Every other alarm, including those caused by minor seizures, was labeled false. In total, the researchers analyzed 2,876 alarms, of which 742, or 25.8 percent, were genuine seizures.

To test whether true and false alarms followed different temporal rhythms, the team turned to a statistical framework known as generalized estimating equations, or GEE. Each alarm was treated as a single observation, with alarm type as the binary outcome and the clock hour of occurrence as the predictor. Because some children generated far more alarms than others, the model used an exchangeable working correlation structure keyed to each child’s identity, preventing the heaviest alarm producers from dominating the results. The researchers also normalized hourly alarm counts by the cumulative device-hours within each clock-hour bin, so that variations in bedtimes and wake-up times across the cohort could not distort the picture. A permutation test compared the skewness of the two alarm distributions, asking whether either true or false alarms showed greater temporal asymmetry across the fourteen-hour monitoring window running from 19:00 to 09:00.

The answer was unambiguous. The temporal occurrence of true and false alarms differed significantly, with the GEE model yielding a p-value below 0.001. True alarms were spread across the nocturnal period with moderate peaks around 21:00 to 22:00 and again around 05:00 to 06:00, roughly tracking the natural ebb and flow of seizure likelihood during sleep. False alarms told a very different story. They were heavily skewed toward the morning transition, and the permutation test confirmed a significant divergence in skewness between the two distributions, with the false alarm skew measuring 4.12 standard deviations from the null distribution. Fully 30.2 percent of all false alarms fell within the 06:00 to 09:00 interval. Under a uniform distribution, any three-hour block within the fourteen-hour window would be expected to contain only 21.4 percent of events, meaning the morning cluster represents a relative enrichment of more than 40 percent above baseline.

The cumulative picture is perhaps the most striking way to visualize the divergence. While true alarms accumulated steadily through the night, false alarms remained sparse during the early and middle hours before rising sharply after 06:00, as if the device’s error rate woke up with the children. The hourly ratio of false to true alarms made the pattern clinically concrete. In the early evening, between 20:00 and 22:00, the normalized ratios sat well below the nightly mean, at 0.67 and 0.52 respectively, indicating hours in which the device was comparatively trustworthy. Between 06:00 and 09:00, those ratios climbed to 1.66, 1.70, and finally 2.56, with confidence intervals that never dipped below 1.2. In other words, the last three hours of the monitoring window carried a false alarm burden more than two and a half times the nightly average in the final hour.

Why would a seizure monitor fail most often just before dawn? The researchers point to wakefulness and morning arousal as the likely culprits. As children transition from sleep toward waking, they stretch, shift position, and experience surges of autonomic activity, precisely the signals the armband is designed to interpret as danger. Notably, because the NightWatch automatically suppresses alerts when the user leaves a recumbent position, the morning surge reflects in-bed detection of increased motor or autonomic activity rather than simple removal of the device. The early evening pattern offers a mirror image: between 19:00 and 22:00, false alarms were relatively scarce and true alarms relatively abundant, possibly because arousals and awakenings that trigger false detections are less likely at that hour, or because seizures themselves are more prone to occur as children settle into sleep.

The implications reach well beyond one device. Current detection algorithms generally operate under a static sensitivity model, assuming that physiological conditions remain uniform throughout the night. This study demonstrates that the assumption is fundamentally flawed. A one-size-fits-all threshold performs well during deep nocturnal sleep, when the noise associated with wakefulness is minimal, but stumbles badly during the waking transition. The authors argue that their findings make the case for dynamic detection models, in which thresholds shift with time of night or with the wearer’s inferred sleep state. Such adaptive algorithms could tighten sensitivity during the high-risk early morning window while relaxing it during the quiet hours when false detections proliferate, potentially easing the alarm fatigue that drives caregivers to ignore alerts or abandon devices altogether.

Yet the path to smarter thresholds is fraught with clinical risk. True seizures also populate the early morning hours, and any modification that dampens sensitivity during the 06:00 to 09:00 window could cause the device to miss a genuine, potentially life-threatening event. The authors are careful to stress that time- or state-dependent thresholds will require prospective validation before they can be deployed. They also acknowledge limitations in their own analysis. The GEE framework estimates population-averaged effects, which means it identified cohort-wide temporal hotspots but may have obscured individual patterns that vary from child to child, and the cohort size was not designed to characterize person-specific periodicities. The team relied on video recordings without electroencephalography, so some alarms labeled false may actually have stemmed from minor seizures rather than innocent movements, introducing physiological heterogeneity that complicates any effort to eliminate false detections entirely.

Even with those caveats, the clinical relevance of the timing finding stands. Most caregivers report that their primary goal is to be alerted to major, dangerous seizures that demand immediate intervention, and the burden of false alarms falls unevenly across the night in ways that matter for real families. Because many false alarms occur near typical awakening times, they may prove less disruptive than those piercing deep sleep, when caregiver vigilance is at its lowest and the stakes of a missed seizure are highest. Conversely, the concentration of false alarms in the morning may compound fatigue precisely when parents are already groggy and less able to discriminate between a device error and an emergency. By mapping when monitoring devices fail, this study transforms alarm fatigue from an vague complaint into a measurable, time-stamped phenomenon, and hands algorithm designers a concrete target: teach the machine to know the difference between a child waking up and a child in danger.

Subject of Research: Temporal patterns of true versus false alarms in wearable pediatric nocturnal seizure detection

Article Title: Temporal Divergence of True and False Alarms in Pediatric Nocturnal Seizure Monitoring

Article References: Shahbakhti, M., Bosch, A. T., van der Palen, J., Leijten, F. S., van Dijk, J. P., Lazeron, R. H. C., & Thijs, R. D. (2026). Temporal Divergence of True and False Alarms in Pediatric Nocturnal Seizure Monitoring. Annals of Clinical and Translational Neurology, Article acn3.70530. https://doi.org/10.1002/acn3.70530

Image Credits: AI Generated

DOI: 10.1002/acn3.70530

Keywords: epilepsy, seizure detection devices, nocturnal seizures, false alarms, alarm fatigue, wearable technology, pediatric neurology, tonic-clonic seizures, sudden unexpected death in epilepsy, detection algorithms, sleep monitoring, adaptive thresholds