# Respiration Symptoms Monitoring in Body Area Networks
## 6 Tracking Person's Breathing in Typical Environment
### Tracking Abnormal Breathing  
In the prevision section, we described how S-Band sensing technique can be used to track person's normal breathing and compared the measured data with that of a wearable sensor. We now present the measurements obtained for tracking person's abnormal breathing, one of the conditions experienced by a patient suffering from hyperhyreosis disease. We used the typical experimental environment and asked the volunteer to breathe abnormally. Hence, we collected the raw amplitude information for 30 frequencies by retrieving 200 WCI packets over a period of time as shown in Figure 7.  
Figure 6: The breathing pattern obtained using S-Band sensing. (**a**) Variances of raw amplitude information of frequency # 13; (**b**) Filtered breathing pattern of the individual frequency. Figure 7: Raw amplitude information received for tracking abnormal breathing. We follow the same procedure as described earlier, and examine all the frequencies to extract the abnormal breathing. The patient was observing abnormal breathing during the process and we opted the frequency # 13 for data analysis from Figure 8. Thus we examine the same frequency for tracking abnormal breathing and compare the measured results with wearable breathing sensor. Figure 9 shows the data obtained using the wearable sensor to track the person's abnormal breathing pattern. The raw data measured for 60 s show clear signs that the person was experiencing abnormal breathing while filtering out the measured data also indicate the same. A clearly non-periodicity can be observed. We further analyze the data retrieved using S-Band sensing as shown in Figure 10a that present raw breathing patterns and Figure 10b which shows filtered breathing pattern and see whether it is able to detect the abnormal sleeping pattern or not. Figure 8: The CFR sequence of all frequencies obtained for abnormal breathing. Figure 9: Abnormal breathing pattern tracked using Digital Respiratory Sensor. (**a**) The original data measured for abnormal breathing; (**b**) Filtered abnormal breathing pattern obtained. The person in this scenario was asked to breathe abnormally which was finely tracked by the wearable sensor. The primary indicator in the particular case was the breathing sensor which did not present any periodicity. The same principle is applied here. The raw and filtered amplitude information indicates a clear non-periodicity for a period of time which indicates the abnormal breathing when the subject was laying on the back (straight position). _Spearman's Rank Correlation_  
We used [PERSON]'s rank correlation to find out correlation between the breathing signals obtained through wearable sensor and S-Band. The Spearman correlation is based on rank method. The coefficient of correlation is calculated by rank differences. On the basis of rank differences, we find out the correlation between two signals. The advantage of [PERSON]'s correlation coefficient is that the computation is quicker, especially when number of observations is limited. The method is useful for series of data such as rank, scores, etc. The formula for [PERSON]'s correlation coefficient is as written follows:  
\[r_{k}=1-\frac{6\sum D^{2}}{N^{3}-N} \tag{4}\]  
Here \(r_{k}\) is the coefficient of rank correlation, \(D\) denotes the rank differences, and \(N\) is the number of pair observations. The value of \(r_{k}\) lies between \(+1\) and \(-1\). Positive value shows that the direction of rank is the same and negative value shows that the ranks are given in opposite direction. We have compared the two signals obtained using wearable sensor and S-Band sensing as shown in Figures 5b and 6b, respectively. The [PERSON]'s correlation coefficient obtained is 0.89 which employs that the value is positive and close to \(+1\). We then compare the two waveforms for abnormal breathing as in Figures 9b and 10b. The \(r_{k}\) value obtained is 0.86 which is positive that means the direction of rank is same.