# C-MWCAR: Classification Based on Multiple Weighted Class Association Rules
## 4 C-MWCAR Applied to Hypertension Diagnosis
### Data Collection and Preprocessing  
As a typical cardiovascular disease, in recent studies, hypertension is usually diagnosed by analyzing heart rate variability (HRV) [20; 28; 29; 30; 31; 32]. HRV is a useful tool to characterize the fluctuation pattern of the heart-beat interval sequence. On the other hand, as a non-intrusive and easily accessible physiological signal, a ballistocardiogram (BCG) signal [33; 34] can accurately reflect the micro body vibrations caused by heart beats. Therefore, the BCG signal has been widely used to extract heart-beat interval sequence and further analyze HRV. Hence, this study also utilizes the BCG signal to diagnose hypertension. In this study, an off-the-shelf BCG acquisition system named RS-611 [35] is employed to collect the BCG signal. As shown in Figure 2, the main components of RS-611 are a micro-movement sensitive mattress (MSM) with two hydraulic pressure sensors placed in the upper part and lower part of the MSM, respectively, an analog-digital (AD) converter, and a terminal PC. When a subject lies on the MSM, the pressure changes caused by heart beats can be recorded in real time, which is called the BCG signal. Compared with other wearable data acquisition devices such as ECG Holter [36] and smart watch [37], RS-611 is completely unobtrusive and the users do not need to attach any electrodes or devices on their body. This study recruited 128 staff members of our university as subjects. First, their blood pressure was measured by a trained nurse using a standard mercury sphygmomanometer. In particular, blood pressure was measured in the left arm twice after five minutes of rest, with the subjects in seated position, according to the guidelines of the American Society of Hypertension [15]. For each subject, blood pressure was recorded in four separate sessions within three weeks, and the averaged values were then used to represent the final systolic and diastolic blood pressure values. Subjects with blood pressure values greater than 140/90 mmHg were viewed as hypertensive patients, while the rest were regarded as normotensive subjects. For each subject, one night of BCG was collected when they were sleeping, since the physiological indexes are relatively stable and not affected by other factors during sleep. The statistical results of the BCG dataset are summarized in Table 2. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Subject Information** & **Hyperensive** & **Normotensive** \\ \hline Number & 61 & 67 \\ Sex (Male/Female) & 33/38 & 35/32 \\ Age (years & 55.6 \(\pm\) 7.9 & 53.2 \(\pm\) 9.2 \\ Heart Rate (bpm) & 77.1 \(\pm\) 9.2 & 73.6 \(\pm\) 8.3 \\ Body Mass Index (kg/m\({}^{2}\)) & 24.3 \(\pm\) 3.6 & 23.7 \(\pm\) 3.3 \\ Systolic blood pressure (mmHg) & 155.6 \(\pm\) 11.2 & 112.1 \(\pm\) 15.7 \\ Diastolic Blood Pressure (mmHg) & 103.6 \(\pm\) 8.2 & 74.4 \(\pm\) 6.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics of the experimental BCG dataset (mean \(\pm\) standard deviation). Figure 2: The micro-movement sensitive mattress-based BCG signal acquisition system (RS-611). A segment of the BCG signal is shown in Figure 3a, from which we extract heart-beat interval sequence through the following steps. First, we normalize the BCG signal by means of the \(Z\)-score method [33] in order to eliminate the individual error caused by body weights, since different body weights can modulate the amplitude of the BCG to some extent. Next, the frequency band that reflects the heart beats is extracted by designing an elliptic bandpass filter, so as to remove the noise caused by breathing or other reasons. Based on trial and error, the stopband corner frequency and the passband corner frequency are finally set to 13/6 Hz and 5/6 Hz, respectively. The stopband attenuation and the passband ripple are set to 8 and 0.2, respectively. Figure 3b shows the filtered BCG signal. Finally, an overlapping sliding window is used to automatically detect heart beats.