## 3 Results  
Experiments using all mother wavelet candidates revealed that only two mother wavelets gave satisfying results, namely, biorthogonal 3.9 and reverse-biorthogonal 3.9. It confirmed what [PERSON] et al. found in their work [16]. Most other candidates failed to mark the boundary between two individual chest vibrations. As both biorthogonal 3.9 and reverse-biorthogonal 3.9 gave similar results, it is difficult to choose the best one. Figure 6 compares the results from symlet, biorthogonal, and reverse-biorthogonal. In addition, manual AO detection proved challenging, as signals from Group #1 had no ECG signal. [PERSON] et al. have already addressed this challenge, even for adults [15]. They found that SI\({}^{\prime}\) tends to have a higher amplitude than S2\({}^{\prime}\). However, in some children, both can have a similar amplitude, and sometimes the amplitude of S2\({}^{\prime}\) is larger than that of SI\({}^{\prime}\)  
Figure 5: Both signal enhancement and Algorithm #2 presented as a block diagram. The asterisks in the figure represent the detected AO from the algorithm. Figure 7 displays results from _Slice and Detect_. The top plot shows the original signal with its detected peaks (*) and starting points of the cardiac cycle (o), whereas the bottom plot displays the _Approximation_ signal and its lower envelope. Figure 8 presents results from _Detect_, which detects peaks without slicing signals into individual segments based on the cardiac cycle. Tables 1 and 2 show the error rate detection (%) from Group #1 and Group #2, respectively, using both algorithms. Results shown in these tables came from the analysis using biorthogonal 3.9 only, as the performances from both biorthogonal and reverse-biorthogonal showed no significant difference. Signals from three subjects were good  
Figure 6: Marking the start of complex pulses from Algorithm #1: Slice and Detect using symlet, biorthogonal, and reverse-biorthogonal mother wavelets. The green dots show the start of each complex pulse. They also serve as the starting point to search for the AO peak in each cardiac cycle. It shows clearly that symlet missed most of the starting points. Figure 7: Result from _Slice and Detect_. Wavelet transform guides the determination of boundaries between individual chest vibrations by providing a peak from the envelope of the Approximation signal (o). The starting points were used to search for corresponding peaks in the enhanced signal, shown as asterisks (*) on top figure. in terms of low motion artefact level. However, the characteristics of SCG signals were atypical; AC was not always smaller than AO. As the measurements in Group #1 did not include ECG, we have no reference for the real peaks. For this reason, they were marked as N/A (not available). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Subject ID**} & \multicolumn{3}{c}{**Slice and Detect**} & \multicolumn{3}{c}{**Detect**} \\ \cline{2-7}  & **Error 1** & **Error 2** & **Total** & **Error 1** & **Error 2** & **Total** \\ \hline
1 (5) & 3.5 & 1.4 & 4.9 & **2.1** & **1.1** & **3.2** \\
2 (8) & 32.6 & 3.4 & 36.0 & **9.5** & **0.3** & **9.8** \\
3 (26) & N/A & N/A & N/A & N/A & N/A & N/A \\
4 (26) & N/A & N/A & N/A & N/A & N/A & N/A \\
5 (32) & 16.2 & 9.4 & 25.6 & **14.3** & **0.4** & **14.7** \\
6 (53) & **17.6** & 15.3 & 32.9 & 25.8 & **8.1** & 33.9 \\
7 (76) & N/A & N/A & N/A & N/A & N/A & N/A \\ Avg. & 17.5 & 7.4 & 24.9 & **12.9** & **2.5** & **15.4** \\ \hline \hline \end{tabular} N/A = not available, Avg. = average, Error 1 = error due to missing peaks, Error 2 = error due to additional peaks.  
\end{table} 
Table 1: Performance of Both Algorithms on Group #1. Figure 8: Result from _Detect_. Wavelet transform guides the determination of peak candidates (*) (**bottom**). The figure shows corrected peak locations (*) (**top**). It is interesting to note here that _Detect_ can follow variations in the inter-beat interval as shown in the beginning of the beat with a larger interval.