**5. Case study 1: driving safely**

**4. EKG-mDFA gadget**

14 Time Series Analysis and Applications

signal, because it often fails to detect R-peaks of EKG.

**Figure 10.** EKG logging and mDFA calculation, a real-time detection and measurement.

**Figure 11.** An example of a screen view of an iPod (lab-made, not for sale).

Health wellness monitoring has been advancing in health care and medical applications [14]. We focus our attention to heartbeat checking. **Figure 10** shows lab-made data logging and mDFA computing devices for a real-time detection and measurement. **Figure 10A** shows electrodes for EKG, commercially available, in-hospital use, using for a prematurely born baby in an incubator, Vitrode V, Nihon Koden, Tokyo, Japan. **Figure 10** shows an EKG amplifier, heartbeat-interval calculator and Bluetooth radio transmitter. This EKG amplifier (**Figure 10B**) receives live-body EKG signal from the two terminals (**Figure 10A** and **B**, any two electrodes, the third one is a spare electrode). **Figure 10C** shows an iPod (Apple, USA), which has a computation program mDFA [4, 5]: We incorporated mDFA into an iPod (not for sale). This system (**Figure 10**) is commercially available except for two items: (1) mDFA program and (2) modified electrode attachment (**Figure 10B**). To us, ready-made goods (**Figure 10B**) have the inconvenience for precision recording of the heartbeat

**Figure 12** shows a practical view of iPod touch screen. To start recording, an operator can touch the button (Rec), and then after completing capture of 2000 beats, it automatically **Figure 12**–**14** show 14 results of consecutive and automated mDFA computation. A volunteer (a male aged 66) drove a car from his home to a town 150 km away to visit his mother-in-law who is hospitalised. He has been driving the road a number of times; thus he is familiar with the road conditions every corner. Furthermore, he drove safely as possible as he can by obeying the speed limit. We recorded his EKGs while driving and computed the scaling exponents (SI) using the device shown in **Figure 10**.

**Figure 12.** An example for EKG monitoring and mDFA results.


**Figure 13.** Fourteen consecutive EKG monitoring and resulting SIs.

The driver's heart rate was monitored by the aforementioned device (**Figure 10**). **Figure 12** shows an example result of mDFA computation. **Figure 12B** represents a 2000-beat recording, that is, an example time series. **Figure 12A** shows an expanded time series of heart rate recording (arrows). Interval signals were transferred to an iPod and stored in it. The iPod has our mDFA program [4, 5]. The program instantaneously computed the scaling exponent (SI) from the heart rate time series, immediately after capturing 2000 heartbeats (**Figure 12C**). **Figure 12D** shows a summary of the characteristics of the data (i.e. the file-name (interval. txt), 37 min and 0.2 s in total recording time for the 2000 beats, R-R interval value and heart rate (beat per min, BPM) for the last heartbeat, i.e. 1046 ms.). **Figure 12C** indicates that driving safely gives a perfect healthy scaling exponent near 1.0. Here, the SI is 0.99.

Anxiety, Worry and Fear: Quantifying the Mind Using EKG Time Series Analysis http://dx.doi.org/10.5772/intechopen.71041 17

**Figure 14.** Two examples of iPod-mDFA. A, corresponding to **Figure 13**, number 10. B, corresponding to **Figure 13**, number 11. A 5-min break of recording between A and B. Driving the car (d), walking into the hospital (e), sitting in the room of the patient (f).

**Figure 13** summarises results of driving-mDFA test. At first, SI showed a low value (SI = 0.84, **Figure 13**, number 1). This can be explained that the driver handled many worries about fuel gas, driving route and so force. After taking the express way, the driver maintained a speed limit (70 km/h) and enjoyed the blue sky of a spring morning day (SI = 1.03, **Figure 13**, number 2). Many vehicles overtook his car one right after the other although some law-abiding cars followed his car. He continued driving safely (**Figure 13**). One can see that his safe driving gave good values of SI, that is, near 1.0 as can be seen in the SI values from 2 to 8 (**Figure 13**).

It is very unique result that a specific behaviour, eating lunch, decreased the SI value (SI = 0.61, **Figure 13**, number 9). We can explain these results as the following: the mind (his brain function, i.e. autonomic nerve function) concentrated to enjoying foods, digesting them in the stomach and even pay less attention to environment. It seems that a dynamic CVCS response to environment is not dominant when eating lunch.

One can see that SI decreased when the subject walked into the hospital and visited/stayed in the room of his mother-in-law (see **Figure 13**, numbers 10 and 11, SI = 0.64 and 0.53, respectively). After going out from the hospital, SI recovered: during driving and shopping at the super market (see **Figure 13**, numbers 12 and 13). We would like to conclude that mDFA can capture anxiety/worry of a subject.

The driver's heart rate was monitored by the aforementioned device (**Figure 10**). **Figure 12** shows an example result of mDFA computation. **Figure 12B** represents a 2000-beat recording, that is, an example time series. **Figure 12A** shows an expanded time series of heart rate recording (arrows). Interval signals were transferred to an iPod and stored in it. The iPod has our mDFA program [4, 5]. The program instantaneously computed the scaling exponent (SI) from the heart rate time series, immediately after capturing 2000 heartbeats (**Figure 12C**). **Figure 12D** shows a summary of the characteristics of the data (i.e. the file-name (interval. txt), 37 min and 0.2 s in total recording time for the 2000 beats, R-R interval value and heart rate (beat per min, BPM) for the last heartbeat, i.e. 1046 ms.). **Figure 12C** indicates that driv-

ing safely gives a perfect healthy scaling exponent near 1.0. Here, the SI is 0.99.

**Figure 13.** Fourteen consecutive EKG monitoring and resulting SIs.

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The last result (**Figure 13**, number 14, SI = 0.77) is interesting. When meeting a new person (the driver's brother-in-law) in order to greet him, SI decreased again to a very low value (**Figure 13**, number 14, SI = 0.77), which indicates that the volunteer subject is very nervous. He said that he tried NOT to display an ungentlemanly attitude to the son of mother-in-law.

**Figure 14** shows two examples of iPod-mDFA screen view. This might give convincing evidence for the idea that 'stressfulness decreases SI'. We would like to emphasise that iPodmDFA is beneficial more than we have expected.

In conclusion, stress decreases SI down to a lower value. We would like to emphasise that three examples, SI = 0.64, and SI = 0.53, SI = 0.77, are great results of iPod-mDFA gadget, and read-out time after 2000 heartbeat detections is only 1–2 s. All SI monitoring were instantaneously computed by iPod-mDFA system as shown in **Figure 13**.
