Electrocardiography (ECG) usually requires a contact device, which is uncomfortable for patients. This thesis explores a novel pipeline that combines different methods for reconstructing ECGs from videos recorded using webcams or smartphone cameras via remote photoplethysmographic (rPPG) signals. We used the LGI-PPGI database and the Pulse Rate Detection (PURE) dataset, containing 1-minute videos of faces during four and six different tasks, respectively. We used three traditional methods to extract rPPG signals from the videos based on an RGB time series. These rPPG signals plus the RGB time series have been used to train a model based on Bidirecional LSTM (BiLSTM) neural network, to obtain a good-quality measure of the rPPG.