Sampling rate and the estimation of ensemble variability for repetitive signals

The measurement of ensemble variability in time-aligned event signals is st udied in relation to sampling rate requirements. The theoretical analysis i s based on statistical modelling of time misalignment in which the time res olution is limited by the length of the sampling interval. For different si gnal-to-noise ratios (SNRs), the sampling rate is derived which limits the misalignment effect to less than 10% of the noise effect. Each signal is as sumed to be corrupted by additive noise. Using a normal QRS complex with a high SNR (similar or equal to 30 dB), a sampling rate of approximately 3 kH z is needed for accurate ensemble variability measurements. This result is surprising since it implies that the Nyquist rate is far too low for accura te variability measurements. The theoretical results are supplemented with results obtained from an ECG database of 94 subjects for which the ensemble variability is computed at different sampling rates using signal interpola tion. The ensemble variability is substantially reduced (40%) when increasi ng the rate from 1 to 3 kHz, thus corroborating the results suggested by th e theoretical analysis.