As shown in Figure 10, our proposed method shows more effective performance in removing the baseline around times such as 4700, 5500, 7500, and 9500. Another major advantage is that the proposed method is computationally faster selleck chemicals 17-AAG than the reference method while achieving the same quality of results. Figure 10Comparison of the proposed method to the reference method.5.3. Further Experimental Analysis of MethodAs mentioned above, in the experiment, multi-channel signals are constructed through a single-channel signal. The multi-channel signals are constructed using sixty signals, which are 10 sample point delayed successions of the original signal. By observation, the number of the constructed signals greatly impacts the success of finding the true baseline wander.

Moreover, the degree of delay has a close relationship with the smoothness of the baseline wander. Experimentally, it can be considered that more channels and smaller delayed signals may achieve better results, meaning that the constructed multi-channel signals may convey enough information in order to accurately extract the baseline wander.In addition, as discussed above, the LBNP dataset shows a significant level of variations in the baseline drift. Therefore, in further analysis of the method, the sub-signals were segmented to verify whether the slow changes in the trend of the baseline wander affect the results of the proposed method in separating the baseline wander. The sub-signals were chosen to be only 10,000 sample points long from the beginning of the original signal in LBNP dataset.

Experimental results showed that the slow changing trend of the baseline wander did not affect the performance of the proposed method in extracting the baseline wander. In other words, the baseline drift with slow changing trends can also be successfully extracted using the proposed method.6. ConclusionWhile using the blind source separation paradigm, the ECG baseline wander or drift may be removed. The present paper demonstrates a hierarchical method utilizing ICA to significantly improve the performance of this process and achieve improved performance. Compared with the existing methods, the proposed method has the following advantages. (1) The proposed method provides more flexibility with regard to parameter estimation and selection.

(2) When following the steps proposed for adjustment of ICA process, the fundamental assumption of baseline noise coming from an independent source can be further verified, which supports the validity Batimastat of using the method for ECG baseline removal. Such an assumption, verified by additional experimental results, would present a chance to remove other types of noise. (3) The filtering process proposed for forming the multi-channel signals provides a highly flexible method to form the input to ICA.