1. An electronic system configured to estimate a subject's blood pressure, comprising: a feature extraction module configured to receive a subject's photoplethysmogram signal, detect a plurality of signal characteristic points on the received photoplethysmogram signal, calculate a plurality of distances in both time and amplitude between any two of the detected photoplethysmogram signal characteristic points, and provide a feature information signal comprising information about the calculated distances; and a blood pressure calculation module configured to receive the photoplethysmogram signal, the feature information signal, and anthropometric characteristics of the subject, wherein the blood pressure calculation module includes: a first estimation module configured to calculate systolic and diastolic blood pressure values of the subject based on the received feature information signal and anthropometric characteristics; and a second estimation module configured to calculate continuous mean blood pressure values of the subject based on the calculated systolic and diastolic blood pressure values and the photoplethysmogram signal; and wherein the first estimation module uses a machine-learning regression model to calculate the systolic and diastolic blood pressure values, and the second estimation module uses a Hilbert-Huang transform and an empirical mode decomposition process to calculate the continuous mean blood pressure values.