Study: Wearable sensors and machine learning may well (one day) help detect a broad range of epileptic seizures

Conclusion: Automatic epileptic seizure detection using machine learning and wearables data is feasible. Preliminary results show better-than-chance seizure detection across a broad range of epileptic seizures. Future improvements may consider clinical chrono-epileptological variables, such as seizure duration, semiology, and etiology or syndrome, and alternative data balancing, pre- and post-processing, fusion and ensemble learning methods. Thus, while findings suggest feasibility, performance following future adjustments may improve further. News in Context: Special Issue: Seizure detection and mobile health devices in epilepsy Study: Brain scans mapping language and memory areas can help guide epilepsy-related surgeries Seizure detection at home: Do devices on the market match the needs of people living with epilepsy and their caregivers?. From the Abstract: … Currently marketed devices are focused primarily on the recording of non–electroencephalography (EEG) signals associated with tonic?clonic seizures, whereas the detection of focal seizures without major motor features remains a clear evidence gap. Moreover, there is paucity of evidence coming from real?life settings. A joint effort of clinical and nonclinical experts, patients, and caregivers is required to ensure an optimal level of acceptability and usability, which are key aspects for a successful continuous monitoring aimed at seizure detection at home. How to intervene early: Examples in depression, ep...
Source: SharpBrains - Category: Neuroscience Authors: Tags: Brain/ Mental Health Technology & Innovation acceptability deep learning EEG epilepsy mHealth seizure detection wearables Source Type: blogs