Data science

Aura NGO dedicates one of its work areas to the collection and analysis of physiological data related to epilepsy. These data are the entry point for the development and evaluation of systems for the detection or even forecasting of seizures in real life.

You can find all our most recent productions below.


ECG signal quality analysis library

2020 - 2021

The evaluation of the ECG signal quality is essential for real life monitoring as it allows to anticipate the loss, the bad positioning of an electrode or an anomaly that would make the seizure detection system inoperative. We have developed an open source software brick that monitors this signal quality in real time.


Exploration of different Machine Learning & Deep Learning models for seizure detection


As part of their final year project, several groups of students from the Jehda datascience course, under the guidance of mentors, evaluated models for the detection of epileptic seizures based on heart rate variability. This work rely on the open data database of Temple University.


Benchmark of QRS complex detection algorithms on the ECG signal


The detection of QRS complexes is an essential step to calculate the heart rate of an individual from an ECG signal. We evaluated 15 different open source implementations of QRS detectors based on 6 ECG databases labelled by cardiologists.


Automatic search for epileptic seizures in a video-EEG database

October 2019

Automatic search for epileptic seizures in a video-EEG database

October 2019

« les Journées Françaises de l'Épilepsie», (the annual French conference of neurologists specializing in epilepsy) were held in Paris at the beginning of October 2019 and we took this opportunity to present the first results of our work. This first poster concerns the search for epileptic seizures in a video-EEG database based on annotations.


Heart rate variability analysis library

October 2018

A number of scientific publications mention the cardiac signal as a relevant physiological marker for detecting or even forecasting seizures. In particular, the variability of the heart rate seems to be of particular interest. We have developed a Python library for the analysis of heart signal variability.


Physiological data collection platform


AURA aims to help patients in their everyday life. It is therefore necessary, in addition to studies conducted in the hospital, to collect data in real life, referred to as "ecological environment" by the medical profession. We have developed a real life data collection and visualization platform, based on open source components.