Biomedical Engineering Reference
In-Depth Information
(a)
-1492
Patient i = 162, γ 162 =0.87
Gray-level Breathing Pattern
Breathing curv e
Extrema
Irregular point
-1493
-1494
-1495
-1496
-1497
6820
6840
6860
6880
6900
6920
6940
6960
Data Time Index(Second)
(b)
-1709
Patient i = 413, γ 413 =0.84
Gray-level Breathing Pattern
Breathing curv e
Extrema
Irregular point
-1710
-1711
-1712
-1713
1.112
1.114
1.116
1.118
1.12
1.122
1.124
1.126
x 10 4
Data Time Index(Second)
Fig. 6.12 Representing gray-level breathing patterns, a patient number 162 with the ratio
c 162 ¼ 0 : 87, and b patient number 413 with the ratio c 413 ¼ 0 : 84
(a)
-1656
Breathing curve
Extrema
Irregular point
Patient i = 125, γ 125 =0.63
Irregular Breathing Pattern
-1658
-1660
-1662
-1664
5600
5650
5700
5750
Data Time Index(Second)
(b)
-1524
Breathing curv e
Extrema
Irregular point
Patient i = 317, γ 317 =0.51
Irregular Breathing Pattern
-1525
-1526
-1527
-1528
1.37
1.375
1.38
1.385
x 10 4
Data Time Index(Second)
Fig. 6.13
Representing
irregular
breathing
patterns,
a
patient
number
125
with
the
ratio
c 125 ¼ 0 : 63, and b patient number 317 with the ratio c 317 ¼ 0 : 51
6.5.5 Classifier Performance
We evaluate the classification performance whether the breathing patterns are
irregular or regular to extract the true positive/negative ranges and the ratio as
shown in Fig. 6.14 . To decide the regular/irregular breathing pattern of the patient
datasets, we have varied observation periods (T i ) for feature extraction with 900,
300, and 100 s.
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