A method of determining a stiffness index of an arterial network of a human subject, comprising the steps of: a) using a pressure sensor of an arterial stiffness index measuring device to measure a radial pulse waveform y(t) related to said arterial network of the human subject, said pressure sensor being configured to transfer the measured radial pulse waveform y(t) to a microprocessor; b) using said microprocessor to model an approximated aortic pulse x(t) based on an inflexion point of a first rising phase of the measured radial pulse waveform y(t), the inflexion point being characterized by a value B and a slope S of a bell-shaped function, where B is the value of the inflexion point of a first rising phase of the measured radial pulse waveform y(t) and S is the slope of the same inflexion point; c) using said microprocessor to reconstruct an impulse response h of the arterial network of the human subject by deconvolving the impulse response h of the arterial network using a least squares method from the measured radial pulse waveform y(t) and the approximated aortic pulse x(t); d) using the microprocessor to segment the impulse response h of the arterial network into at least two segmented components h 1 (t) and h 2 (t) where h 1 (t) is a component of the impulse response h relating to direct propagation of the aortic pulse to the radial artery within the arterial network of the human subject and h 2 (t) is a component of the impulse response h relating to propagation of the aortic pulse from a first reflection site to the radial artery; and e) using the microprocessor to calculate said stiffness index of said arterial network as a ratio of a peak of the second segmented component h 2 (t) to a peak of the first segmented component h 1 (t). a) using a pressure sensor of an arterial stiffness index measuring device to measure a radial pulse waveform y(t) related to said arterial network of the human subject, said pressure sensor being configured to transfer the measured radial pulse waveform y(t) to a microprocessor; b) using said microprocessor to model an approximated aortic pulse x(t) based on an inflexion point of a first rising phase of the measured radial pulse waveform y(t), the inflexion point being characterized by a value B and a slope S of a bell-shaped function, where B is the value of the inflexion point of a first rising phase of the measured radial pulse waveform y(t) and S is the slope of the same inflexion point; c) using said microprocessor to reconstruct an impulse response h of the arterial network of the human subject by deconvolving the impulse response h of the arterial network using a least squares method from the measured radial pulse waveform y(t) and the approximated aortic pulse x(t); d) using the microprocessor to segment the impulse response h of the arterial network into at least two segmented components h 1 (t) and h 2 (t) where h 1 (t) is a component of the impulse response h relating to direct propagation of the aortic pulse to the radial artery within the arterial network of the human subject and h 2 (t) is a component of the impulse response h relating to propagation of the aortic pulse from a first reflection site to the radial artery; and e) using the microprocessor to calculate said stiffness index of said arterial network as a ratio of a peak of the second segmented component h 2 (t) to a peak of the first segmented component h 1 (t).