Irregular Breathing Classification from Multiple Patient Datasets - Prediction and Classification of Respiratory Motion - page 122

80

70

Min of γ i = 0.02
Max of γ i = 1
Mean of γ i = 0.92

60

50

40

30

20

10

0

0 0.2 0.4 0.6 0.8 1

Ratio ( γ i )

Fig. 6.10 Frequency distribution of ratio c ðÞ . Here c i is the ratio of the true negative range (R i TN )
to the period of observation (T i ), thus it is dimensionless. The ratio c ðÞ for each breathing dataset
is distributed from 0.02 to 1 with 0.92 as the average ratio value

(a)

-1561

Breathing curv e
Extrema
Irregular point

Patient i = 1, γ 1 =0.98
Regular Breathing Pattern

-1562

-1563

-1564

-1565

-1566

2.505 2.51 2.515 2.52

Data Time Index(Second)

(b)

x 1 0 4

-1662.5

Patient i = 177, γ 177 =0.98
Regular Breathing Pattern

Breathing curve
Extrema
Irregular point

-1663

-1663.5

-1664

-1664.5

1.61 1.615 1.62 1.625

x 10 4

Data Time Index(Second)

Fig. 6.11 Representing regular breathing patterns, a patient number 1 with the ratio c i ¼ 0 : 98,
and b patient number 177 with the ratio c 177 ¼ 0 : 98

Figure 6.12 shows gray-level breathing patterns in the given datasets. Even
though the gray-level breathing patterns show some consecutive irregular points, the
overall breathing patterns are almost identical as shown in Fig. 6.12 . Figure 6.13
shows irregular breathing patterns in the given datasets. Note that the breathing
pattern in Fig. 6.13 b with a very low ratio c 317 ¼ 0 : 5 ð Þ is void of regular patterns
and that there exists a mass of irregular breathing points in Fig. 6.13 .