Heart rate variability (HRV) analysis has recently been incorporated into wearable device application. The data source of HRV is the time series of heart beat intervals extracted from electrocardiogram or photoplethysmogram. These intervals are non-uniformly sampled signals and not suitable for spectral HRV analysis, which usually uses uniformly resampled heart beat intervals before calculating the spectral domain parameters. Such a practice is not applicable to heart rate data obtained from wearable devices that usually display and export the beat per minute (BPM) time series data at 1鈥疕z. The preferred resampling rate to calculate spectral domain parameters is 4鈥疕z. We compare the spectral HRV results with the 1鈥疕z and 4鈥疕z resampling rates in order to validate the use of 1鈥疕z resampled-RRI data to represent wearable devices BPM time series data for HRV analysis. Our results show that, using a specific combination of signal processing techniques, the lowest mean relative error in spectral domain parameters of normalized low-frequency power (LFnu), normalized high-frequency power (HFnu) and the ratio of normalized low-frequency power to normalized high-frequency power (LFnu/HFnu) between 1鈥疕z and 4鈥疕z are 3.7%, 15.3% and 16.4%, respectively. We conclude that using the heart rate data sampled at 1鈥疕z produces a reasonable estimation of sympathetic activity but a poor estimation of parasympathetic activity.