Determination of FBNS and activated regions.
During an fMRI experiment and while the subject performs a set of tasks responding to external stimuli (task-related fMRI) or no tasks (resting-state fMRI), a series of 3-D brain images is acquired.
The localization of the activated brain areas is a challenging Blind Source Separation (BSS) problem, in which the sources consist of a combination of spatial maps (areas activated) and time-courses (timings of activation).
During a task-related fMRI acquisition (for example), our brain is not activated only from the task information we process, but also from external and (sometimes) even unconscious stimuli (e.g. heart beating, breathing, thinking of personal and family issues not connected to the task, etc.), hence the separation of the activation directly connected to the hand in task is a difficult and complex problem.
We can perform different types of analysis in order to study the activated areas in single or multi-subject studies.
As mentioned above, the use of the activated areas of subjects can be used in different fields, from psychological analysisto marketing purposes.
Press to see the activated regions of the brain
Epilepsy Prediction / Detection
Automated seizure diary
Currently, treatment evaluation relies on manual seizure diaries maintained by the patient itself, which have shown to have an accuracy under 50%. An objective and automated offline seizure diary could lead to better patient treatment due to more reliable information for the clinician.
Automated seizure detection in hospital environment
In order to help neurologists decrease the time needed to review and annotate full-day recordings from a large number of patients. We offer ML algorithms than can pre-classify segments with high probability of being a seizure. The neurologist only needs to review and annotate only such segments instead of going through long recordings. Algorithms with high sensitivity are a perquisite for this application.
Atrial Fibrillation Prediction
AINIGMA technologies is developing AI-based applications for the prediction of the development of Atrial Fibrillation (AF). We use demographics, medical history, physical activity, simple clinical and laboratory data and also ECG recordings to train our algorithms. We aim to provide a sensitive and user friendly application that will be able to predict which patients are at high risk of developing AF in the near future, and unmask which of them could already have subclinical AF. Early risk assessment of AF will lead to closer medical attention and more aggressive screening, thus early diagnosis and treatment of AF.
Sepsis Prediction / Detection
Early sepsis prediction and detection
AINIGMA technologies develops AI-enabled algorithms for early prediction and detection of sepsis incidents in the hospital setting. The algorithm is trained by processing real-world clinical and laboratory data and their variations in all-cause hospital admissions (excluding the admissions where sepsis is the initial diagnosis). We aim to develop a sensitive tool that will predict the risk of sepsis in admitted patients and detect sepsis incidents early in the course of the disease, hence raising the awareness of healthcare providers, to intervene as soon as possible.