Juan Sebastian Rojas Velandia
Generative Adversarial Network for short-term EEG signals compression.
Rel. Edoardo Patti, Santa Di Cataldo, Alessandro Aliberti. Politecnico di Torino, Master of science program in Ict For Smart Societies, 2022
Abstract
Medical signals obtained through EEG (Electroencephalography) are commonly used to diagnose and treat different illnesses such as epilepsy and seizure disorders. These signals are very complex due to their sensitivity to noise and their difficult understanding. Additionally, they tend to have a big size which makes difficult their processing and utilization. The EEG signals consist of multiple channels depending on the number of electrodes that are used to measure brain activity. This means that the size of the signals is a function of the measurement time and the number of channels. Based on these premises, compression is needed to facilitate the transport and storage of the signals.
However, compression is a challenging task because there can be information losses in the process
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