Commit d6a4bde3 authored by Boris Baldassari's avatar Boris Baldassari
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Update aice aura demonstrator following fza review.

parent 84e6e387
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There are a myriad of different forms and origins of epilepsy and epileptic seizures. The symptoms and physical signs broadly differ according to each patient: research has been conducted on electroencephalograms (EEGs), electrocardiograms (ECGs), movement detection, electrodermal activity, and even using dogs. As a result it is impossible — as of today at least — to draw a generic-purpose diagnostic or prediction method.
However machine learning (ML) methods have been extensively used in the recent years to draw viable seizure detection and forecasting, and tackle the variability across patients. Neurophysiologists use a visualisation tool like Grafana to enter the annotations and define time ranges as either normal activity (noise) or epileptic activity (seizures).
Machine Learning (ML) algorithms have been extensively used in the recent years to tackle this variability across patients and draw viable seizure detection and forecasting. Generally speaking, ML methods rely on a (large) set of examples, in our case datasets of ECGs with their associated seizure annotations, to predict specific outputs (epileptic seizures) on new input data (e.g. live ECGs). Neurophysiologists use a visualisation tool like Grafana to enter the annotations and define time ranges as either normal activity (noise) or epileptic activity (seizures), and store them in dedicated (`.tse_bi`) files.
These annotations are used as a reference dataset for training various ML models. Available datasets are usually split so as to set one part for the training and another one to verify the trained model. A typical workflow is to then try to predict epileptic seizures according to an ECG signal and check if the human annotations confirm the seizure.
More information:
* Methods for seizure detection: https://en.aura.healthcare/analyse-des-données
* Methods for seizure detection: <https://en.aura.healthcare/analyse-des-données>
* Seizure dogs: https://www.epilepsy.com/living-epilepsy/seizure-first-aid-and-safety/seizure-dogs
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