Improving discriminality in heart rate variability analysis using simple artifact and trend removal preprocessors

Heart Rate variability (HRV) is important in characterizing heart functions. However, artifacts and trends are regularly observed to contaminate the HRV sequences. This study proposes a simple and effective preprocessor for the removal of artifacts and trend in the HRV sequences. A thresholding filter is applied to remove artifacts to maintain the HRV sequences in a reasonable range. A wavelet filter proceeds to remove the ultra and very low frequency components determined as trends. As a consequence, more reliable low frequency (LF) and high frequency (HF) components can be calculated, which are believed to be close-related to the autonomic nervous system (ANS) regulation of the heart. The result demonstrates that features calculated from the power spectral density of the preprocessed HRV are more separable in feature space when compared with that from the original HRV. A simple KNN classifier is employed to justify the effects of this preprocessor in differentiating congestive heart failure (CHF) from the normal sinus rhythms (NSR). Using five features calculated from LF and HF, the performance of the KNN classifier shows significant improvement after applying the preprocessors. When compared with the other studies published in the literature, the proposed method outperforms them in CHF recognition with a much simpler scheme.