I have used the Data Sharing (the Open Seizure Database) data to assess the false alarm rate, so new users have an idea what to expect from OpenSeizureDetector. I have analysed one particular user who I know wears the watch every night so it gives us a good idea of the overnight false alarm rate.
I have taken all the false alarms (ie not genuine seizures) and calculated the weekly total for each week in 2023, up to 05 August 2023. The weekly totals are divided by 7 to give us the average daily false alarm rate. The results are shown below:
From the graph above you can see that our false alarm rate is between 1 and 2 per day on average, but it does vary quite a lot. This variation is behaviour related, because the seizure detector settings were constant throughout this period (Alarm Threshold=900, Alarm Ratio Threshold=57). Note also that this user does not use the heart rate alarm functions, only the ‘OSD’ algorithm to analyse movement.
The database does not contain much information on what behaviour caused the false alarms – some of it is repetitive autistic movements, such as pointing or sorting, and quite a lot is just fidgeting when asleep.
While we could reduce this false alarm rate by increasing the Alarm Threshold setting (so the system has to detect more movement before it activates the seizure detection algorithm), or the alarm period (so we need to see seizure-like movement for longer before it alarms), both of these would reduce the seizure detection sensitivity. The carers of this particular user are happy to tolerate the false alarm rate because the system has successfully woken them up in the night for genuine seizures.
Note also that this user only wears the watch when alone in his room, so maybe around 12-14 hours per day, and he does not generally wear it when brushing teeth etc. We know that the following activities would generate false alarm if the user is wearing the watch and does not use the ‘mute’ function on the app screen to inhibit alarms: Brushing Teeth, Brushing Hair, Cooking (e.g. chopping or stirring), cleaning (scrubbing).
The next major development for OpenSeizureDetector will be to develop a Machine Learning (“Artificial Intelligence”) seizure detector algorithm that will distinguish between genuine seizure movements and the common false alarm movements more effectively. We have a promising one that has been trained on the data in the Data Sharing system that I hope to release for testing soon. We are also releasing the Open Seizure Database that we have developed for researchers to use in the hope that they will identify a better way of detecting seizures with fewer false alarms.
So please keep contributing to the Data Sharing system, and most importantly mark genuine seizures that occur when OpenSeizureDetector is in use, so we can use these to train the new systems and test them.
Graham (graham@openseizuredetector.org.uk)