Lee Ming-Yuan, Yu Sung-Nien
Department of Electrical Engineering, National Chung Cheng University, Taiwan.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:4574-7. doi: 10.1109/IEMBS.2010.5626022.
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.
心率变异性（HRV）对于表征心脏功能很重要。然而，经常观察到伪迹和趋势会污染HRV序列。本研究提出了一种简单有效的预处理器，用于去除HRV序列中的伪迹和趋势。应用阈值滤波器去除伪迹，以使HRV序列保持在合理范围内。接着使用小波滤波器去除确定为趋势的超低频和极低频成分。因此，可以计算出更可靠的低频（LF）和高频（HF）成分，据信它们与心脏的自主神经系统（ANS）调节密切相关。结果表明，与原始HRV相比，从预处理后的HRV功率谱密度计算出的特征在特征空间中更易于区分。使用一个简单的KNN分类器来验证该预处理器在区分充血性心力衰竭（CHF）和正常窦性心律（NSR）方面的效果。利用从LF和HF计算出的五个特征，应用预处理器后，KNN分类器的性能有显著提高。与文献中发表的其他研究相比，该方法在CHF识别方面以更简单的方案胜过它们。
