Abstract: A method based on pulse wave analysis for monitoring cardiovascular vital signs, including: measuring a photoplethysmography (PPG) signal during a measurement time period such as to obtain a time series of PPG pulses; and during the measurement time period, identifying individual PPG pulses in the PPG signal, each PPG pulse corresponding to a PPG pulse cycle. For each PPG pulse, the method uses a pulse-wave analysis technique to determine, within the pulse cycle, at least one of: a time-related feature comprising a time duration and a normalized amplitude-related pulse-related feature and a SNR-related pulse-related. For each PPG pulse, a machine learning model is used in combination with the determined time-related, normalized amplitude-related and SNR-related features, to classify each PPG pulse in the pre-processed PPG signal as “normal”, “pathological” or “non-physiological” such as to output a time series of pulse classes.