Table 2 CNN + RNN classification results, evaluated on test days for each participant (Testing dataset and training dataset).

From: Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG

Subject id

Sensitivity %

Specificity %

Accuracy %

Number of correctly predicted 10 minutes intervals/total

5-min ECG

10-min voting

5-min ECG

10 min voting

5-min ECG

10 min voting

Classification results evaluated on the testing days

Subject 1

79.7

80.5

69.4

73.3

73.3

76

61/82 = 74.4%

Subject 2

81.8

81.8

82.2

88.0

82.2

87.5

154/178 = 86.5%

Subject 3

82.4

76.5

89.6

94.6

89.2

93.7

166/179 = 92.7%

Subject 4

100

100

81.1

82.0

84.8

85.6

128/153 = 83.6%

Average

86.0 ± 9.4

84.7 ± 10.4

80.6 ± 8.3

84.5 ± 9.0

82.4 ± 6.7

85.7 ± 7.3

84.3%

CNN + RNN classification results evaluated on the training days.

Subject 1

74.8

75.2

84.6

88.6

80.0

82.3

78/95 = 82.1%

Subject 2

85.9

84.1

92.8

96.3

91.4

94.1

170/180 = 94.4%

Subject 3

100

100

84.6

89.2

87.2

91.1

240/267 = 89.9%

Subject 4

92.6

90.2

91.9

94.6

92.2

92.6

145/158 = 91.8%

Average

88.3 ± 10.6

87.4 ± 10.4

88.5 ± 4.4

92.2 ± 3.8

87.7 ± 5.5

90.0 ± 5.2

89.6%

  1. The 5-min column presents the results for the 5-minutes ECG segments used during training, whereas the 10-minute voting column shows the results when taking the majority class corresponding to all heartbeats in a 10-minute window of time.