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Table 1 Neonatal sleep and wake classification results (five-fold cross-validation) using different pre-trained CNNs combined with an SVM classifier

From: Can pre-trained convolutional neural networks be directly used as a feature extractor for video-based neonatal sleep and wake classification?

Video frame

  

Se %

Sp %

Ac %

P %

SVM

RGB

VGG16

FCL6

52.4

64.05

58.3

57.7

FCL7

52.6

75.1

64.3

66.5

FCL8

40.9

90.0

66.2

40.2

Thermal

FCL6

73.1

40.16

58.4

60.2

FCL7

72.4

40.3

58.1

60.0

FCL8

71.2

41.5

58.0

60.1

RGB

AlexNet

FCL7

69.8

61.0

65.3

62.7

Thermal

FCL7

61.0

40.4

49.9

55.0

RGB

FCL8

59.5

71.2

65.7

64.7

Thermal

FCL8

56.5

60.0

58.4

52.7

RGB

VGG19

FCL6

81.9

36.4

52.6

54.6

FCL7

81.0

36.1

51.9

54.5

FCL8

40.9

90.0

65.2

79.4

Thermal

FCL6

63.6

54.2

57.1

63.2

FCL7

67.6

45.0

54.6

60.3

FCL8

62.1

52.4

58.1

61.8

RGB

InceptionV3

FCL

97.4

30.0

55.1

52.5

Thermal

FCL

67.3

47.6

58.7

61.4

RGB

ResNet-18

FCL

73.2

50.8

61.2

58.2

Thermal

FCL

66.7

46.7

58.3

60.8

RGB

GoogLeNet

FCL

77.7

41.8

55.3

55.5

Thermal

FCL

66.6

46.7

57.83

60.8

  1. *true positive (TP) = VEEG depict sleep and correctly identified as sleep by our feature extraction approach, false positive (FP) = VEEG depict awake and incorrectly identified as sleep by our feature extraction approach, true negative (TN) = VEEG depict awake and correctly as awake identified our feature extraction approach, false negative (FN) = VEEG depict sleep and incorrectly identified as awake by our feature extraction approach