Commit 0935f837 authored by Boris Baldassari's avatar Boris Baldassari
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Update aice aura demonstrator following fza review.

parent d6a4bde3
......@@ -47,13 +47,13 @@ More information:
### Existing workflow
We started from the workflow already developed by the AURA data scientists. As usual the first step is to prepare the data before using it (cleaning, selection and extraction of features, and formatting). The resulting dataset is subsequently fed to a Random Forest model to predict future seizures.
We started from the workflow already developed by the AURA data scientists. As usual the first step is to prepare the data before using it (cleaning, selection and extraction of features, and formatting). The resulting dataset is subsequently fed to a ML model to predict future seizures.
The data inputs of the workflow come from two different file types:
* The raw ECG signal data stored as European Data Format (EDF).
* The annotations, which describe if the signal pattern is actually an epileptic seizure or normal activity, are stored in `.tse_bi` files with a 1-to-1 association with the EDF signal files.
The data preparation step is achieved through a series of Python scripts developed by the AURA scientists, which extract the rr-intervals (i.e. the time laps between two heart beats), cardiac features and annotations, then build a simplified dataset that can be used to train a Random-Forest algorithm:
The data preparation step is achieved through a series of Python scripts developed by the AURA scientists, which extract the rr-intervals (i.e. the time between two heart beats), cardiac features and annotations, then build a simplified dataset that can be used to train a Random-Forest algorithm:
{{< grid/div isMarkdown="false" >}}
<img src="/images/articles/aice_aura_demonstrator/ecg_workflow.png" alt="The AURA AI process - before" class="img-responsive">
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