A computer-implemented method for analysing a pulse wave signal (PWS), obtained from a subject, wherein the pulse wave signal (PWS) comprises pulse wave measurements for a plurality of cardiac cycles of the subject during a first time period, the method comprising: (i) analysing the pulse wave signal (PWS) to identify a plurality of cardiac cycles and a respective reference point for each identified cardiac cycle; characterized in that the method further comprises: (ii) determining a second derivative with respect to time of the pulse wave signal (PWS); (iii) determining a normalised second derivative by, for each part of the second derivative corresponding to a respective identified cardiac cycle, normalising said part of the second derivative with respect to the amplitude of the second derivative at the identified reference point for said cardiac cycle; (iv) for a first lag time value, determining an n -th order polynomial fit for a first set of values of the normalised second derivative, wherein the first set of values of the normalised second derivative comprises the values of the normalised second derivative occurring the first lag time value from the reference point of each identified cardiac cycle, wherein n is equal to or greater than 1; (v) performing one or more further iterations of step (iv) for one or more further lag time values to determine respective further n -th order polynomial fits for respective sets of values of the normalised second derivative, wherein a respective set of values of the normalised second derivative comprises the values of the normalised second derivative that occur the respective further lag time value from the reference point of each identified cardiac cycle; and (vi) forming a first average cardiac cycle waveform for a first time point in the first time period, wherein the first average cardiac cycle waveform is formed from values of the plurality of n-th order polynomial fits at the first time point. (i) analysing the pulse wave signal (PWS) to identify a plurality of cardiac cycles and a respective reference point for each identified cardiac cycle; (ii) determining a second derivative with respect to time of the pulse wave signal (PWS); (iii) determining a normalised second derivative by, for each part of the second derivative corresponding to a respective identified cardiac cycle, normalising said part of the second derivative with respect to the amplitude of the second derivative at the identified reference point for said cardiac cycle; (iv) for a first lag time value, determining an n -th order polynomial fit for a first set of values of the normalised second derivative, wherein the first set of values of the normalised second derivative comprises the values of the normalised second derivative occurring the first lag time value from the reference point of each identified cardiac cycle, wherein n is equal to or greater than 1; (v) performing one or more further iterations of step (iv) for one or more further lag time values to determine respective further n -th order polynomial fits for respective sets of values of the normalised second derivative, wherein a respective set of values of the normalised second derivative comprises the values of the normalised second derivative that occur the respective further lag time value from the reference point of each identified cardiac cycle; and (vi) forming a first average cardiac cycle waveform for a first time point in the first time period, wherein the first average cardiac cycle waveform is formed from values of the plurality of n-th order polynomial fits at the first time point. <EOS>