An electronic system for estimating a subject's blood pressure, comprising: a feature extraction module configured for receiving a subject's photoplethysmogram signal, detecting a plurality of signal characteristic points (F1 to FN, U1 to UN, PP1 to PPN, DN1 to DNN, SP1 to SPN) on said received photoplethysmogram signal, calculating a plurality of distances in both time and amplitude between any two of said detected photoplethysmogram signal characteristic points, and providing a feature information signal comprising information about said calculated distances; and a blood pressure calculation module configured for receiving said photoplethysmogram signal, said feature information signal and anthropometric characteristics of the subject, and the blood pressure calculation module comprising - a first estimation module configured for calculating systolic and diastolic blood pressure values (SBP, DBP) of the subject based on said received feature information signal and anthropometric characteristics, and wherein the first estimation module is configured to use a machine-learning regression model for calculating said systolic and diastolic blood pressure values (SBP, DBP), characterised in that the system further comprises: - a second estimation module configured for calculating continuous mean blood pressure values (CMBP) of the subject based on said calculated systolic and diastolic blood pressure values (SBP, DBP) and said photoplethysmogram signal; wherein the second estimation module is configured to use a Hilbert-Huang transform and an empirical mode decomposition process for calculating said continuous mean blood pressure values (CMBP). a feature extraction module configured for receiving a subject's photoplethysmogram signal, detecting a plurality of signal characteristic points (F1 to FN, U1 to UN, PP1 to PPN, DN1 to DNN, SP1 to SPN) on said received photoplethysmogram signal, calculating a plurality of distances in both time and amplitude between any two of said detected photoplethysmogram signal characteristic points, and providing a feature information signal comprising information about said calculated distances; and a blood pressure calculation module configured for receiving said photoplethysmogram signal, said feature information signal and anthropometric characteristics of the subject, and the blood pressure calculation module comprising - a first estimation module configured for calculating systolic and diastolic blood pressure values (SBP, DBP) of the subject based on said received feature information signal and anthropometric characteristics, and wherein the first estimation module is configured to use a machine-learning regression model for calculating said systolic and diastolic blood pressure values (SBP, DBP), characterised in that the system further comprises: - a second estimation module configured for calculating continuous mean blood pressure values (CMBP) of the subject based on said calculated systolic and diastolic blood pressure values (SBP, DBP) and said photoplethysmogram signal; wherein the second estimation module is configured to use a Hilbert-Huang transform and an empirical mode decomposition process for calculating said continuous mean blood pressure values (CMBP). - a first estimation module configured for calculating systolic and diastolic blood pressure values (SBP, DBP) of the subject based on said received feature information signal and anthropometric characteristics, and wherein the first estimation module is configured to use a machine-learning regression model for calculating said systolic and diastolic blood pressure values (SBP, DBP), - a second estimation module configured for calculating continuous mean blood pressure values (CMBP) of the subject based on said calculated systolic and diastolic blood pressure values (SBP, DBP) and said photoplethysmogram signal;