ArticleiScience2021
Artificial neural network trained on smartphone behavior can trace epileptiform activity in epilepsy.
Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 22 citations in OpenAlex.
- Neural network-based method for measuring the impacts of epileptic brain activities on cardiac cycles.Frontiers in neurology · 2025Article
- Real-world smartphone data can trace the behavioural impact of epilepsy: A case study.European journal of neurology · 2024Article
- Age-related behavioral resilience in smartphone touchscreen interaction dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Common multi-day rhythms in smartphone behavior.NPJ digital medicine · 2023Article
- Temporal clusters of age-related behavioral alterations captured in smartphone touchscreen interactions.iScience · 2022Article
- A model of healthy aging based on smartphone interactions reveals advanced behavioral age in neurological disease.iScience · 2022Article
- Neuromechanical Biomarkers for Robotic Neurorehabilitation.Frontiers in neurorobotics · 2021Review
- Cortical integration of tactile inputs distributed across timescales.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
Abstract
A range of abnormal electrical activity patterns termed epileptiform discharges can occur in the brains of persons with epilepsy. These epileptiform discharges can be monitored and recorded with implanted devices that deliver therapeutic neurostimulation. These continuous recordings provide an opportunity to study the behavioral correlates of epileptiform discharges as the patients go about their daily lives. Here, we captured the smartphone touchscreen interactions in eight patients in conjunction with electrographic recordings (accumulating 35,714 h) and by using an artificial neural network model addressed if the behavior reflected the epileptiform discharges. The personalized model outputs based on smartphone behavioral inputs corresponded well with the observed electrographic data (R: 0.2-0.6, median 0.4). The realistic reconstructions of epileptiform activity based on smartphone use demonstrate how day-to-day digital behavior may be converted to personalized markers of disease activity in epilepsy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.