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Table 2 Number of embolism events according to X-ray computed microtomography (µCT) and linear discriminant analysis (LDA) model for the 132 AE datasets of sensor AE1

From: X-ray microtomography and linear discriminant analysis enable detection of embolism-related acoustic emissions

AE dataset

µCT

LDA

AE dataset

µCT

LDA

1–2

0–2

0–0

67–68

1–0

1–0

3–4

0–2

0–0

69–70

76–0

85–5

5–6

0–1

1–0

71–72

1–0

1–1

7–8

0–29

0–44

73–74

1–0

1–1

9–10

0–6

10–1

75–76

1–0

2–1

11–12

0–9

2–1

77–78

1–0

1–6

13–14

0–3

0–1

79–80

33–0

1–2

15–16

0–1

9–0

81–82

28–0

1–5

17–18

0–1

1–0

83–84

1–0

1–3

19–20

3–0

1–0

85–86

4–0

2–3

21–22

1–0

0–1

87–88

1–0

1–1

23–24

5–0

1–1

89–90

1–0

1–1

25–26

1–0

1–2

91–92

1–0

1–16

27–28

1–0

1–5

93–94

5–0

2–3

29–30

1–0

1–1

95–96

1–0

0–2

31–32

2–0

1–1

97–98

1–0

1–3

33–34

1–0

1–2

99–100

1–0

1–14

35–36

2–0

0–1

101–102

1–0

1–0

37–38

1–0

0–3

103–104

32–1

2–4

39–40

2–0

1–2

105–106

0–6

2–1

41–42

6–0

2–12

107–108

0–4

1–4

43–44

15–0

4–1

109–110

0–1

11–1

45–46

3–0

1–1

111–112

3–0

5–4

47–48

1–0

5–11

113–114

1–0

4–10

49–50

1–19

1–4

115–116

2–0

1–1

51–52

0–2

4–1

117–118

1–4

1–5

53–54

1–0

4–1

119–120

0–62

14–73

55–56

8–0

1–1

121–122

0–16

9–0

57–58

1–0

5–0

123–124

12–0

1–0

59–60

1–0

1–2

125–126

2–0

1–4

61–62

1–0

1–1

127–128

1–0

1–1

63–64

1–0

5–1

129–130

1–0

1–8

65–66

14–0

1–1

131–132

1–0

1–7

  1. Italic values illustrate sufficient embolism-related AE detection by LDA in accordance with the number of embolism events by µCT. For every AE dataset, an LDA model was trained on all remaining datasets