# Dempster-Shafer Fusion Based on a Deep Boltzmann Machine for Blood Pressure Estimation
## 4 Experimental Results and Statistical Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**BP (mmHg)** & **SBP** & **DBP** & **SBP L** & **SBP U** & **DBP L** & **DBP U** \\ \hline category 1 & 81.6 & 45.5 & 79.3 & 92.4 & 42.9 & 49.1 \\ category 2 & 92.8 & 50.5 & 93.0 & 96.4 & 50.2 & 55.7 \\ category 3 & 98.9 & 56.6 & 96.5 & 101.0 & 55.8 & 60.2 \\ category 4 & 103.7 & 60.3 & 101.1 & 105.1 & 60.2 & 63.4 \\ category 5 & 107.9 & 64.1 & 105.2 & 109.6 & 63.5 & 66.7 \\ category 6 & 112 & 68.2 & 109.8 & 115.5 & 66.8 & 69.9 \\ category 7 & 117.7 & 71.0 & 115.6 & 119.6 & 70.0 & 75.3 \\ category 8 & 123.9 & 77.0 & 119.8 & 126.4 & 75.4 & 80.5 \\ category 9 & 130.7 & 81.6 & 126.8 & 134.5 & 80.6 & 85.7 \\ category 10 & 139.8 & 98.2 & 135.5 & 144.8 & 88.3 & 98.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: BP (blood pressure) categories are represented by the \(k\)-medoids algorithm corresponding to cluster locations obtained by nurse BP measurements, where L and U denote lower and upper bounds. observers [27]. Moreover, it classifies the BP measuring device into four classes from A to D, as shown in Table 4. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Accuracy** & **SVM** & \multicolumn{2}{c}{**SVMDS**} & \multicolumn{2}{c}{**DBN**} & \multicolumn{2}{c}{**DBNDS**} & \multicolumn{2}{c}{**DBM**} & \multicolumn{2}{c}{**DBMDS**} \\ \hline
**test** & **SBP** & **DBP** & **SBP** & **DBP** & **SBP** & **DBP** & **SBP** & **DBP** & **SBP** & **DBP** & **SBP** & **DBP** \\ \hline test 1 & 0.64 & 0.67 & 0.67 & 0.70 & 0.67 & 0.69 & 0.71 & 0.74 & 0.67 & 0.70 & 0.73 & 0.76 \\ test 2 & 0.66 & 0.68 & 0.68 & 0.71 & 0.68 & 0.72 & 0.70 & 0.75 & 0.69 & 0.71 & 0.73 & 0.76 \\ test 3 & 0.65 & 0.70 & 0.67 & 0.72 & 0.66 & 0.69 & 0.72 & 0.73 & 0.68 & 0.71 & 0.74 & 0.77 \\ test 4 & 0.66 & 0.69 & 0.68 & 0.71 & 0.65 & 0.72 & 0.71 & 0.73 & 0.68 & 0.74 & 0.73 & 0.76 \\ test 5 & 0.65 & 0.69 & 0.68 & 0.71 & 0.69 & 0.73 & 0.71 & 0.74 & 0.72 & 0.74 & 0.74 & 0.77 \\ \hline avg & 0.65 & 0.69 & 0.68 & 0.71 & 0.67 & 0.71 & 0.71 & 0.74 & 0.69 & 0.72 & 0.73 & 0.76 \\ std & 0.01 & 0.01 & 0.01 & 0.01 & 0.02 & 0.02 & 0.01 & 0.01 & 0.02 & 0.02 & 0.01 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of SBP (systolic blood pressure) and DBP (diastolic blood pressure) with respect to classification rate among SVM (support vector machine) [28], SVM with DS (Dempster-Shafer) fusion (SVMDS), DBN (deep belief network) [10], DBN with DS(DBNDS), DBM (deep Boltzmann machine) [12], and the proposed DBM-based DS fusion (DBMDS) scheme. Figure 4: ROC (receiver operating characteristic) curve based on experimental conditions using the DBMDS (DBM based DS fusion) estimator vs. DBM single estimator for SBP (systolic blood pressure) and DBP (diastolic blood pressure) classification. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**mmHg** & **MAA** & **SVM** & **SVMDS** & **DBN** & **DBNDS** & **DBM** & **DBMDS** \\ \hline
**Test** & **SBP** & **DBP** & **SBP** & **DBP** & **SBP** & **SBP** & **DBP** & **SBP** & **DBP** & **DBP** & **SBP** & **DBP** \\ \hline ME & 0.60 & 1.73 & \(-0.17\) & 0.19 & 0.40 & \(-0.25\) & 0.11 & \(-0.03\) & \(-0.24\) & 0.09 & 0.21 & \(-0.18\) & 0.17 & \(-0.11\) \\ SDE & 9.32 & 7.40 & 7.14 & 5.25 & 6.00 & 5.14 & 6.28 & 5.20 & 5.66 & 4.71 & 5.96 & 4.85 & 5.34 & 4.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of ME (mean error) and SDE (standard deviation of error) relative to the target results obtained with the conventional MAA (maximum amplitude algorithm), SVM [28], SVMDS, DBN [10], DBNDS, DBM [12], and DBMDS scheme, where the results are the mean values for our test data.