Patterns in time, like rhythms and seasonality, are found everywhere. From the beats in music, to the seasons coming and going each year. In my research, I will investigate the symptoms and severity of seizures in terms of rhythms that happen over hours, days, months, and even years.
As an analogy, severe events such as droughts may only happen rarely, but when they do happen there is a seasonality to it – they mainly happen in summer. We can think of seizures in a similar way. Severe seizures may only happen occasionally, but when they do occur, they are at certain times of the day or month, and this is different and unique for each patient. Similarly, less severe seizures may also occur too, but at other times.
If we can understand this ‘seasonality of severity’ of epileptic seizures, we can hope to better identify the drivers, or causes of severe seizures, and interfere with these drivers to deliver more timely and targeted treatments.
To do this analysis, huge amounts of data is required from many patients recorded over long periods of time. Without these large datasets, it would be impossible to identify any patterns. Imagine trying to find out if it is summer with only temperature measurements from 2 days – there is simply not enough information.
Although some previous research has been carried out in this area, researchers have been massively limited by the amount of data used. In addition, the computer methods to analyse such datasets have also since advanced substantially. During my UKRI Fellowship, I will work with some of the largest epilepsy centres across the globe to gather and analyse these large datasets.
This is a really exciting time for epilepsy research. We have more data about our patients and their seizures than ever before, and the computers to analyse that data are incredibly powerful. Huge companies like Amazon use seasonality and ‘big data’ to influence our shopping habits – I believe we can do similar research to influence seizures and make them less debilitating.
The hope is that by understanding the seasonality of severity, we can forecast this like weather. This could allow preventative actions like increasing medication levels at particular times or reducing levels when not needed. It could also enable entirely new ways of intervention, for example changing habits and lifestyle through feedback based on a mobile app.
For the past decades, we have thought about seizures as isolated, often disruptive and devastating events. It’s time we investigate the rhythms and seasons that dictate their severity in each patient and use them for better treatments.
