A computer implemented method for delineation and multi-label classification of a cardiac signal having a plurality of time points, said method comprising: - receiving the cardiac signal, the cardiac signal generated by a plurality of electrocardiogram leads sensing over a plurality of heartbeats; - applying a first convolutional neural network to said cardiac signal, wherein the first convolutional neural network: • reads each time point of the plurality of time points assigned to the cardiac signal; • analyzes spatio-temporally each time point of the cardiac signal; • assigns to each time point of the cardiac signal a plurality of first scores indicative of the presence of a plurality of types of waves comprising at least the following types of waves: P-waves, QRS complexes, T-waves; • computes an onset and an offset of each type of wave of the plurality of types of waves in the cardiac signal based on the plurality of first scores assigned to each time point; • computes a plurality of global measurements based on the onsets and the offsets; and - applying a second convolutional neural network to said cardiac signal, wherein the second convolutional neural network: • reads each time point of the cardiac signal and the plurality of global measurements; • analyzes each time point of the cardiac signal and analyzes the plurality of global measurements; • computes a plurality of second scores for a time window aggregating at least two time points; and • allots to the time window a plurality of labels which each have a score higher than at least one predetermined threshold. - receiving the cardiac signal, the cardiac signal generated by a plurality of electrocardiogram leads sensing over a plurality of heartbeats; - applying a first convolutional neural network to said cardiac signal, wherein the first convolutional neural network: • reads each time point of the plurality of time points assigned to the cardiac signal; • analyzes spatio-temporally each time point of the cardiac signal; • assigns to each time point of the cardiac signal a plurality of first scores indicative of the presence of a plurality of types of waves comprising at least the following types of waves: P-waves, QRS complexes, T-waves; • computes an onset and an offset of each type of wave of the plurality of types of waves in the cardiac signal based on the plurality of first scores assigned to each time point; • computes a plurality of global measurements based on the onsets and the offsets; and • reads each time point of the plurality of time points assigned to the cardiac signal; • analyzes spatio-temporally each time point of the cardiac signal; • assigns to each time point of the cardiac signal a plurality of first scores indicative of the presence of a plurality of types of waves comprising at least the following types of waves: P-waves, QRS complexes, T-waves; • computes an onset and an offset of each type of wave of the plurality of types of waves in the cardiac signal based on the plurality of first scores assigned to each time point; • computes a plurality of global measurements based on the onsets and the offsets; and - applying a second convolutional neural network to said cardiac signal, wherein the second convolutional neural network: • reads each time point of the cardiac signal and the plurality of global measurements; • analyzes each time point of the cardiac signal and analyzes the plurality of global measurements; • computes a plurality of second scores for a time window aggregating at least two time points; and • allots to the time window a plurality of labels which each have a score higher than at least one predetermined threshold. • reads each time point of the cardiac signal and the plurality of global measurements; • analyzes each time point of the cardiac signal and analyzes the plurality of global measurements; • computes a plurality of second scores for a time window aggregating at least two time points; and • allots to the time window a plurality of labels which each have a score higher than at least one predetermined threshold.