Wavelet optimization for applying continuous wavelet transform to maternal electrocardiogram component enhancing*Project supported by the National Natural Science Foundation of China (Grant No. 61271079). (October 2017) - Yu 于, Qiong 琼; Guan 管, Qun 群; Li 李, Ping 萍; Liu 刘, Tie-Bing 铁兵; Si 司, Jun-Feng 峻峰; Zhao 肇, Ying 莹; Liu 刘, Hong-Xing 红星; Wang 王, Yuan-Qing 元庆
Abstract : In the procedure of non-invasive fetal electrocardiogram (ECG) extraction, high-quality maternal R wave peak detection demands enhancing the maternal ECG component firstly. Among all the enhancing algorithms, the one based on the continuous wavelet transform (CWT) is very important and its effectiveness depends on the optimization of the used wavelet. However, up to now, there is still no clear conclusion on the optimal wavelet (including type and scale) for CWT to enhance the maternal ECG component of an abdominal ECG signal. To solve this problem, in this paper, we select several common used types of wavelets to carry out our research on what the optimal wavelets are. We first establish big-enough training datasets with different sampling rates and make a maternal QRS template for each signal in the training datasets. Second, for each type of selected wavelets, we find its optimal scale corresponding to each QRS template in a training dataset based on the principle of maximal correlation. Then calculating the average of all optimized wavelet scales results in the mean optimal wavelet of this type for the dataset. We use two original abdominal ECG databases to train and test the optimized mean optimal wavelets. The test results show that, as a whole, the mean optimal wavelets obtained are superior to the wavelets used in other publications for applying CWT to maternal ECG component enhancing.