A computer-implemented method of monitoring a human or animal subject, comprising: (a) receiving measurement data representing a time series of measurements on a subject performed by a sensor system ; (b) processing the measurement data to represent the measurement data as a mathematical expansion comprising a plurality of expansion components and a corresponding plurality of expansion coefficients representing the respective strength of each expansion component; (c1) forming a first partial reconstruction of the measurement data using a first subset of the expansion components and corresponding expansion coefficients; (c2) forming a second partial reconstruction of the measurement data using a second subset of the expansion components and corresponding expansion coefficients, the second subset being different from the first subset; (d1) performing a first spectral analysis on the first partial reconstruction to determine a first dominant frequency, the first dominant frequency representing a frequency component of largest amplitude in the first partial reconstruction; (d2) performing a second spectral analysis on the second partial reconstruction to determine a second dominant frequency, the second dominant frequency representing a frequency component of largest amplitude in the second partial reconstruction; and (e) providing an output representing a frequency of a periodic physiological process based on either or both of the first dominant frequency and the second dominant frequency, wherein: the method further comprises determining a degree of similarity between the first dominant frequency and the second dominant frequency; and the output representing the frequency of the periodic physiological process is selectively provided depending on the determined degree of similarity. (a) receiving measurement data representing a time series of measurements on a subject performed by a sensor system ; (b) processing the measurement data to represent the measurement data as a mathematical expansion comprising a plurality of expansion components and a corresponding plurality of expansion coefficients representing the respective strength of each expansion component; (c1) forming a first partial reconstruction of the measurement data using a first subset of the expansion components and corresponding expansion coefficients; (c2) forming a second partial reconstruction of the measurement data using a second subset of the expansion components and corresponding expansion coefficients, the second subset being different from the first subset; (d1) performing a first spectral analysis on the first partial reconstruction to determine a first dominant frequency, the first dominant frequency representing a frequency component of largest amplitude in the first partial reconstruction; (d2) performing a second spectral analysis on the second partial reconstruction to determine a second dominant frequency, the second dominant frequency representing a frequency component of largest amplitude in the second partial reconstruction; and (e) providing an output representing a frequency of a periodic physiological process based on either or both of the first dominant frequency and the second dominant frequency, wherein: the method further comprises determining a degree of similarity between the first dominant frequency and the second dominant frequency; and the output representing the frequency of the periodic physiological process is selectively provided depending on the determined degree of similarity. the method further comprises determining a degree of similarity between the first dominant frequency and the second dominant frequency; and the output representing the frequency of the periodic physiological process is selectively provided depending on the determined degree of similarity. <EOS>